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They made the test environment public last June and are launching the actual network during 2020.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Bitcoin has now existed for over a decade, and almost everyone has owned bitcoin at some point – or at least knows someone who has. Still, bitcoin never became the digital money it was meant to be. Most probably, your local grocery store doesn’t accept it, nor do you use it when you need to transfer money to your friends or family. 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This means that the value of Libra should only fluctuate based on the value of these backing assets.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Only trusted parties, the members of the Libra Association, are allowed to run servers that validate the transactions. Therefore, the proof-of-work mechanism is not required. This increases both the speed and throughput of processing transactions (and does not require the crazy amounts of electricity consumed by the big cryptocurrencies). The validating nodes still have to agree to confirm a transaction, and the network can tolerate up to ⅓ of the validators to be compromised.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"The test environment for Libra has been available since June, so developers and companies can already start getting prepared for making and accepting payments in Libra.The rest of this article explains the basic concepts of Libra and has code examples for creating transactions in the testnet to show you how easy transferring money can be with Libra.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Sending money to your friend shouldn’t be harder than getting them an Uber ride home. Libra has the potential to bridge the gap between traditional financial networks and new digital currency technology while reducing the costs for everyone — especially consumers.\n\n- Peter Hazlehurst, Head of Payments and Risk, Uber Technologies, Inc.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"blockquote"},{"data":{},"content":[{"data":{},"marks":[],"value":"Accounts","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"The only thing you need for setting up a new account for sending and receiving transactions is a public-private key pair you can create by using the Ed25519 signature scheme. This key-pair is used to sign the transactions digitally, and the account identifier (address) is derived from the public key by hashing. You can create a new account in the Libra system by sending money to it or by making an account creation transaction.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"After creating the key-pair, you can create the account address by making a SHA3 hash of the public key.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Transactions","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"To make a transaction, you need to specify the following information:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Transaction sequence number","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Gas amount. 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The serialized data is then signed, and the transaction is ready to be sent to the Libra API.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The amount of money to be sent nor the receiver of the payment is not specified in the transaction attributes. This information is included in the transaction script and arguments, which are written in a Libra-specific programming language called Move.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"‘Move’ transaction script","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"The transaction script makes it possible for you to create much more complex transactions than simple money transfers from one account to another. These type of programmable transactions are also called “smart contracts”, and you can also specify for example several receivers for the money and define conditions that need to be met for the transaction to go through.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"For this, Libra has its own programming language called Move. A Libra -specific programming language was designed to efficiently and safely create logic around transactions.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"To get started, you don’t need to learn this new language, as there are ready-made scripts for most common use cases in the Libra code repository.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Libra API","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Libra API uses gRPC, which is a web-service framework created by Google. The API is defined in service definition files, which are used to generate the client code for different platforms. The testnet API is running at ","nodeType":"text"},{"data":{"uri":"http://ac.testnet.libra.org:8000"},"content":[{"data":{},"marks":[],"value":"http://ac.testnet.libra.org:8000","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" and can be accessed without registration.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"For testing purposes, Libra also provides a service for adding money (minting) to test accounts.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Behind the API, the transaction will go through different validations after which it will be sent to other validator nodes in the network. The validators will vote on the transaction to be executed, and the changes will then be written to the ledger. You can find a more detailed description from the Libra documentation: ","nodeType":"text"},{"data":{"uri":"https://developers.libra.org/docs/life-of-a-transaction"},"content":[{"data":{},"marks":[],"value":"https://developers.libra.org/docs/life-of-a-transaction","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Making the API call","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"JLibra is a Java client library I started implementing as an open source project after the testnet was made public. JLibra is used for creating transactions and querying information about the accounts. There are also several other client libraries available for different programming languages. By using a client library, you don’t need to implement the serialization, signing and the gRPC call yourself. If you are interested in the details of those, you can find them in the source code.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The first step is to generate key-pairs for the accounts that are involved in the transaction. ","nodeType":"text"},{"data":{"uri":"https://github.com/ketola/jlibra/blob/master/jlibra-examples/src/main/java/dev/jlibra/example/GenerateKeysExample.java"},"content":[{"data":{},"marks":[],"value":"GenerateKeysExample.java","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Next, you need to create some money for testing purposes. This is done using the Libra faucet API and is obviously only available in the test environment. ","nodeType":"text"},{"data":{"uri":"https://github.com/ketola/jlibra/blob/master/jlibra-examples/src/main/java/dev/jlibra/example/MintExample.java"},"content":[{"data":{},"marks":[],"value":"MintExample.java","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"After creating money in the accounts, you can check the balance by querying the API. From the response, you can also find the account sequence number for the transaction. 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Now your app can have all the good stuff from both native and web apps in one application and you have total control of the deployment (no app stores), encrypted notifications (not readable by third party services) and your users can choose to install the application on their devices allowing connectivity independence and native app-like experience.\n","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Sounds to good to be true?","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"Almost. The features are only available in the latest versions of the modern browsers but you can start implementing your progressive app today. Users with modern browsers get the good stuff immediately and others can use the same app the traditional way: through the browser window until support for these new features spread.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The share of Chrome users is already over 50% so I believe this is viable option, if you are considering a new application now. 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I no longer need to keep checking my emails or slack messages for system status notifications and I can keep those programs closed when I want to focus on something else.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"So how does it all work?","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"I have a Logitech G815 keyboard and unfortunately the LED configuration tool that ships with it (G-Hub) supports only some flashy animations and other pretty useless effects. But it does ship with an easy to use SDK, which you can use to control the LED colors from an external program!","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"So, I wrote a small program which controls the RGB LEDs on my keyboard. 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Algorithms can save lives but when not done right they can truly make our lives worse. These notions bring up the responsibility of design to the fore.","marks":[],"data":{}}],"data":{}},{"nodeType":"blockquote","content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"As designers we are responsible in making the future happen and at the same time making sure it is one that we would want to live in.","marks":[],"data":{}}],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Technological singularity where there are more possibilities than we are producing is at our hands. There is still so much more that we can achieve through technology which should make everyone excited. 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What is the tone this AI assistant should ","nodeType":"text"},{"data":{"uri":"https://youtu.be/5nll1K1YHdQ?t=5h8m57s"},"content":[{"data":{},"marks":[],"value":"deliver news we can’t imagine yet","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"?","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"},{"data":{"uri":"https://youtu.be/5nll1K1YHdQ?t=2h54m49s"},"content":[{"data":{},"marks":[],"value":"Zalando","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" presented a really useful model for understanding the relationship between data, intelligence and experience. Let’s say you’re building a smart service that helps grocery shoppers discover suitable recipes and gather them at their local grocery store. 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The algorithms needed for data processing and predictive analytics are already freely available. Read the advice of Samuli Visuri, Nitor's analytics business director, on how to make the most of analytics.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With regard to artificial intelligence and machine learning, there is a common illusion a single technology, software, or algorithm would help a company boost its business. As if there would be some sort of a black box, which digests all the data and spits out an operational guideline.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Sorry to disappoint, there is no black box. There has been no new mystical breakthrough in algorithm development, and no software can standardise data processing and predicting the future. 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","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 3: Simplify and succeed ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" Business is fundamentally about predicting the future and taking action accordingly. The point of artificial intelligence and machine learning is not in the use of brilliant algorithms, but rather in the ability to simplify key business questions into mathematical models. The winner is the one who can formulate the right abstraction for the key question – in a way that it can be answered.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 4: Acquire expertise that will benefit you immediately ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Almost without exception, the best option is to quickly implement a Proof-of-Concept or a smaller solution, which yields immediately measurable business benefits. This is, of course, easier said than done! However, an experienced professional is, together with business experts, able to find a suitable starting point. A consultant is not the best to advise on how to do business. But she knows the possibilities of using data and is able to harness large amounts of data in such a way it is useful in decision making and running business. 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Strategies that can turn possibility into action form the basis of sustainable digital development. But you'd be wrong to think brilliant algorithms are at the core of all data-led operations. Real success is rooted in the ability to simplify key questions and present them as mathematical models. We can help you formulate the right abstract model for the right question - and deliver the answers your business has been looking for. ","marks":[],"data":{}}],"data":{}}]},"references":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6CWb2esZsQNFH1dItGAv2l","type":"Entry","createdAt":"2020-12-09T07:10:27.084Z","updatedAt":"2020-12-09T14:43:42.884Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"namedReference"}},"locale":"en-US"},"fields":{"name":"Read how we improved customer experience with data science","entry":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"42YMVYO76Jx4qNct4V86V1","type":"Entry","createdAt":"2020-11-27T17:30:46.555Z","updatedAt":"2020-12-09T08:54:30.831Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":3,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["en","fi"],"title":"Providing Business Value with Data Science at Finnair: The Five-Step Model","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"VYijgbOjloAoQfD3OWxgL","type":"Asset","createdAt":"2020-11-27T15:39:00.761Z","updatedAt":"2020-11-27T16:26:38.909Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"locale":"en-US"},"fields":{"title":"FIN_Airbus_A330_high_Resolution_jpg_79.jpg","description":"Photo: Finnair, 3D Aviation / Ole Wind Andersen","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/VYijgbOjloAoQfD3OWxgL/db6912fc523018fbebccb72a8531f55d/FIN_Airbus_A330_high_Resolution_jpg_79.jpg","details":{"size":926226,"image":{"width":1985,"height":1197}},"fileName":"FIN Airbus A330 high Resolution jpg_79.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"We had the opportunity of improving Finnair's Operational Analytics efforts with Data Science. In this blog post, we'll walk you through the five key steps we took to ensure the best possible outcome.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Airline operations are complex. Keeping flights in schedule, rerouting passengers who missed their connections, and in general, reacting to unexpected events are just some problems Operations Controllers must deal with, while considering factors such as weather conditions, travel restrictions, and crew availability. In managing operations, airlines make numerous decisions every day, the outcomes of which may depend on each other and which can have ripple effects on many flights. For example, delaying a flight to wait for connecting passengers may cause the aircraft to arrive late on its return flight, possibly leading to other passengers missing their connecting flights.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Such effects depend on many factors, such as the passenger makeup of all affected flights, which makes it difficult to weigh their importance in decision making. Consequently, coarse, heuristic models have traditionally been applied, which can simplify the problem but also suffer from biases, leading to suboptimal decisions. 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Identifying the Problem","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Working at an airline of Finnair's scale is, for a Data Scientist, like a dream come true: the data is plentiful and well organized, and there are seemingly endless optimization problems to solve. Of course, as for any mature industry, many of the fundamental problems are already solved. Thus, when we started our work, the first questions were: what problems we can solve, and what problems should we solve utilizing the customer's data assets to create as much value as possible, in terms of both financial and customer success.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When starting a project, our goal as consultants is to understand the challenges the customer is facing. Thus, it's natural to begin by workshopping with the customer's domain experts. 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This would allow us to paint a clearer picture of upcoming and ongoing flights, leading to easier and more informed decision-making.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"2. Getting the First Version Out","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After choosing the first problem to tackle, we set out to build a Minimum Viable Product (MVP). In software, an MVP is an application with as few features as possible to satisfy users.The goal is that after release, new features will be added in response to feedback from users. By contrast, in a Data Science product, an MVP can also involve including simplifying assumptions in a model. The idea is that it's easier to engage stakeholders in a feedback loop when they can interact with the results and validate them against their own domain knowledge. The model can then be made more complex in response to feedback, if it turns out to be inadequate.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When starting to work on the MVP, a lot of subtle technical and data-related aspects need to be clarified with the customer. Typical questions addressed in this phase include, for instance, what are the right data sources to be used when solving the problem, and how the varying data sources need to be combined, cleaned and pre-processed for further modeling efforts. Diving into the data, formulating and validating assumptions of the models, and fleshing out initial solution designs makes it clear if the planned approach will work. From the customer’s point of view, this phase validates that the link between domain knowledge and the data is correctly interpreted and verifies that results produced by the model seem reasonable. Often, this phase also produces secondary business value as it can, for example, reveal deficiencies in data quality and clarify some aspects of existing business processes.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"In our work with Finnair, there was a great deal of domain knowledge to digest in the first few weeks in particular. 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From this feedback, it became clear where further development efforts should be concentrated.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Some key insights from this process were:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"For users to be engaged and provide feedback, the user experience needs to be smooth, and there needs to be a clear feedback channel.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"In order for expert users to trust a model, they need transparency over what goes in and what comes out.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"While iterative development continues after this initial MVP stage, the initial iteration is crucial as it improves the application holistically and makes it more robust for the next phase. For instance, with Finnair, the iterative development led into leaving out some irrelevant features in the user interfaces of the applications while at the same time including other features that were initially not seen important. End users also spotted several exceptional cases that were not handled correctly by the initial models, which led to improving them further.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"4. Evolution into a “Production-Ready” Application","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After the application was iterated on and validated through initial feedback, it started to be ready for wider adoption. By following good software design practices from the beginning, transitioning from the MVP into a production application was seamless. To achieve this, some key points we followed were:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Portability: in Python, for example, using virtual environments or containers ensures that the code works when deployed in different environments (e.g. moving from development to staging).","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Maintainability: writing modular code makes it easy to make modifications and switch out dependencies when required; following best practices of software version control helps keep track of changes and is useful in debugging.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Performance: in data-intensive applications, designing code to be performant from the start helps keep the development feedback loop fast. In near-real-time applications like ours, it is also crucial to ensure that adding complexity to models doesn't degrade performance significantly. Luckily, Data Science-oriented software libraries like Pandas are written with performance in mind, so it's often enough to follow best practices.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Customer's needs: it is important to select tools that the customer is invested in; common reporting tools for results, and programming languages that are commonly used in the organization (e.g., with Finnair, it was deemed better to transition to a reporting tool widely used in the organization for presenting results instead of the initial custom tools developed).","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Monitoring and security have to be considered thoroughly.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"In our project, we were in the \"production ready\" stage in about five months, which is when it was introduced to a larger group of users. At this stage, we dropped the custom dashboard built in the MVP, and delivered results in Finnair's internal Business Intelligence environment; this had two main benefits:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"it was already familiar to the users, so adoption was frictionless, and ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"managing user access became trivial. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"ordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"All in all, the transition to a wider user base was surprisingly smooth, although the amount of feedback naturally increased remarkably. Thus, we introduced some custom forms for collecting and storing feedback, which significantly improved the feedback loop.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"5. A Suite of Further Applications","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"At this point, we had a mathematical model of the core business. While still being iteratively developed, it was mature enough to yield more accurate insight than previous solutions. Having selected the initial problem carefully, we knew that the core model created opportunities for a suite of other tools and optimization processes to further improve the efficiency of business operations. Thus, together with Finnair, we planned several new MVPs that would build on this model. There were two motivations for moving forward with multiple applications: on the one hand, we wanted to quickly create more value where it was possible; and on the other hand, we wanted to lay out clarified directions for Finnair’s operational analytics by exploring the application space and validating our ideas.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The suite of applications developed was adopted to daily operational use smoothly, which was supported by training sessions for the end users as well as an on-going gathering of feedback. Sharing information about the tools for the organization and quickly incorporating improvement suggestions to the models enabled the inclusion of key people in the development efforts and fast extraction of value from the tools. This resulted in increased customer satisfaction as well as significant cost savings at Finnair. In conclusion, the five-step data science process described in this blog post has proven to be a valuable guideline as Finnair’s operational analytics area continues to evolve.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"authors":[],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"providing-business-value-data-science-finnair-five-step-model","publishDate":"2020-11-23","showRelatedArticles":true}}}}]}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"60V2KyzLyECDfQbUZjN0wl","type":"Entry","createdAt":"2020-11-11T06:52:15.070Z","updatedAt":"2020-12-15T19:32:12.607Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":7,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"expertiseBlock"}},"locale":"en-US"},"fields":{"title":"Machine learning and artificial intelligence","paragraph":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Machine learning and artificial intelligence can solve classification problems and detect patterns in large volumes of data efficiently. We use them for risk assessment and customer profiling, among other things. We can help you efficiently map out applications for artificial intelligence and convert its models into products.","marks":[],"data":{}}],"data":{}}]},"references":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"1ueCpzpX0l4egaRjz1NbuR","type":"Entry","createdAt":"2020-12-09T07:14:51.015Z","updatedAt":"2020-12-09T14:43:06.744Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"namedReference"}},"locale":"en-US"},"fields":{"name":"Nitor invests in Artificial Intelligence","entry":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"tkwqWR8RXJGlzjfyYWaDo4","type":"Entry","createdAt":"2020-12-08T14:29:54.239Z","updatedAt":"2020-12-08T14:29:54.239Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"​Nitor invests in Artificial Intelligence","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"yIT7UV7fRq2t77qvxdrTY1","type":"Asset","createdAt":"2020-11-27T16:54:46.801Z","updatedAt":"2020-11-27T16:54:46.801Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"nitor-insight-2018-05.jpg","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/yIT7UV7fRq2t77qvxdrTY1/419f76a258461d9f1de097224763773e/nitor-insight-2018-05.jpg","details":{"size":361936,"image":{"width":2280,"height":1282}},"fileName":"nitor-insight-2018-05.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Nitor has founded a new business focused on Artificial Intelligence. Nitor Insight will offer research and consulting services on data science, AI, and machine learning. Nitor Insight started its operations in early 2018 and will be developed into a significant part of Nitor’s services and business.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"italic"}],"value":"Nitor Insight’s launch has been prepared by Nitor’s Data Scientists ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Sami Airaksinen","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":" , ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Tuomas Ritola","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":", and ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Juha Syrjälä","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":" as well as Principal Consultant ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Otto Manninen","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":".","nodeType":"text"},{"data":{},"marks":[],"value":"\n","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“We are extremely excited about launching Nitor Insight. AI and machine learning are an essential part of sustainable digital development. By broadening our offering, we can produce even better services for our current customers as well as target entirely new business areas,” ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"Matti Vilmi","nodeType":"text"},{"data":{},"marks":[],"value":", Nitor’s CEO, explains.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"AI, machine learning, and data science have become increasingly popular within the past few years and continue to be hot topics. Advanced technologies, increasing amounts of data, and the development of platforms such as Amazon, Google, and Microsoft, make the solutions an even bigger part of everyday life.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“Our customers handle large amounts of data, and machine learning is a way to create value out of this data. We have utilized machine learning throughout the years in making prognoses and recommendations within, for example, the media, finance, retail, logistics, and transportation industries. Lately, we have especially worked with different kinds of customer-service chatbots and other speech-related services,” Otto Manninen says.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Even though there is plenty of hype around AI, practicality and creating real value is a priority at Nitor Insight. Machine learning enables automation of solutions that wouldn’t have been sensible with traditional methods. People can focus on solving more important problems, aided by machines.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“The benefits are obvious: machine learning is now mature enough that it can be used in business-critical systems. Consumers are also increasingly starting to demand intelligent functions from all the services they use. And these are now often based on machine learning,” Tuomas Ritola explains.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"In addition to automating routine actions as well as supplementing current digital services, AI and machine learning offer many interesting new possibilities. Ideas move quickly from the academic world into productization and commercialization.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“The development of speech recognition and natural language processing has created opportunities for new kinds of chat-based services. The AI platforms and personal assistants from the big service providers will slowly change people’s habits and expectations of how to transact with businesses,” Juha Syrjälä says.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“I would estimate that the next big discussion topic after deep learning will be Generative Adversarial (Neural) Networks or GANs. These neural networks enable AI to learn how to create realistic photographs and other visualizations. It will be also interesting to see when Neural Processing Unit or NPU-accelerated devices become common and alter the current computation ecosystems,” Sami Airaksinen adds.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"With Nitor Insight, AI, machine learning, and data science are now a significant part of Nitor’s services. They are a part of Nitor’s sustainable digital development that already includes pragmatic architecture, design, large-scale agility, and technology services.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"nitor-invests-artificial-intelligence","publishDate":"2018-06-13"}}}}]}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6IMKZh2y8erek8LfALQz3P","type":"Entry","createdAt":"2020-11-11T06:53:12.936Z","updatedAt":"2020-12-15T19:32:28.180Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":5,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"expertiseBlock"}},"locale":"en-US"},"fields":{"title":"Data storage and management","paragraph":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With the right data storage and management system, you can enable seamless development and elegant use of analytics, knowledge-based management and other data-based services. Our cost-effective solutions are always guided by the principles of information security, reliability and the ability to scale to meet your organisation’s needs.","marks":[],"data":{}}],"data":{}}]}}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2zNC67HdO7loGBkCFSLTgk","type":"Entry","createdAt":"2020-12-09T07:33:49.356Z","updatedAt":"2020-12-14T14:32:40.852Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":3,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"contactBlock"}},"locale":"en-US"},"fields":{"title":"Contact us","additionalInfo":["phone number","email"],"people":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6LCYDgXGe75zxb2BEb9Mp3","type":"Entry","createdAt":"2020-12-08T09:46:11.310Z","updatedAt":"2020-12-17T21:54:04.673Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":4,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"person"}},"locale":"en-US"},"fields":{"name":"Samuli Visuri","jobTitle":"Managing director, Analytics services","email":"samuli.visuri@nitor.com","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2ezVsrZfIaOggjXWq56qMy","type":"Asset","createdAt":"2020-11-27T17:22:24.531Z","updatedAt":"2020-11-27T17:22:24.531Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"Samuli Visuri profile blue cropped","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/2ezVsrZfIaOggjXWq56qMy/f6b0a87fc8bfc8e22b192c793adc1a04/Samuli_Visuri_profile_blue_cropped.jpg","details":{"size":1787811,"image":{"width":2000,"height":2000}},"fileName":"Samuli_Visuri_profile_blue_cropped.jpg","contentType":"image/jpeg"}}}}}]}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"1zz3jysm45N7BDTO2fAzhZ","type":"Entry","createdAt":"2020-12-17T16:36:01.798Z","updatedAt":"2020-12-17T16:36:01.798Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"highlight"}},"locale":"en-US"},"fields":{"name":"Analytics articles highlight","title":"Read more","articles":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"QEskfaQufwc7FyhMdGZMP","type":"Entry","createdAt":"2020-12-08T09:46:54.481Z","updatedAt":"2020-12-17T13:30:36.198Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":12,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"CIO - move from collecting data to utilising it","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"5NAZ0U8qKv6cSVfigvEp35","type":"Asset","createdAt":"2020-11-27T17:02:28.455Z","updatedAt":"2020-12-08T09:44:03.496Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"locale":"en-US"},"fields":{"title":"Samuli Visuri sitting ","description":"Samuli Visuri, People","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/5NAZ0U8qKv6cSVfigvEp35/cdcaa6e505a839fe24d5a870c6a2f389/Samuli_Visuri_portrait.jpg","details":{"size":320976,"image":{"width":1792,"height":1008}},"fileName":"Samuli_Visuri_portrait.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The revolution in using data is based on the increase in computing power and open source development. The algorithms needed for data processing and predictive analytics are already freely available. Read the advice of Samuli Visuri, Nitor's analytics business director, on how to make the most of analytics.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With regard to artificial intelligence and machine learning, there is a common illusion a single technology, software, or algorithm would help a company boost its business. As if there would be some sort of a black box, which digests all the data and spits out an operational guideline.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Sorry to disappoint, there is no black box. There has been no new mystical breakthrough in algorithm development, and no software can standardise data processing and predicting the future. Unfortunately, results that really benefit the business are not created by connecting data sources into a crystal ball.   ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"However, getting started is not that complicated. The revolution in using data is based on increased computing capacity and open source development. Companies can set up a powerful computing environment in minutes, and with reasonable costs, if they are willing to use cloud services. The algorithms needed for data processing and predictive analytics are already freely available. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"The good news, then, is that the that business managers don’t have to whine about various technology choices. You don’t even need expensive licenses. All you need is an expert, who knows how to use and modify algorithms to suit your business needs. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 3: Simplify and succeed ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" Business is fundamentally about predicting the future and taking action accordingly. The point of artificial intelligence and machine learning is not in the use of brilliant algorithms, but rather in the ability to simplify key business questions into mathematical models. The winner is the one who can formulate the right abstraction for the key question – in a way that it can be answered.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 4: Acquire expertise that will benefit you immediately ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Almost without exception, the best option is to quickly implement a Proof-of-Concept or a smaller solution, which yields immediately measurable business benefits. This is, of course, easier said than done! However, an experienced professional is, together with business experts, able to find a suitable starting point. A consultant is not the best to advise on how to do business. But she knows the possibilities of using data and is able to harness large amounts of data in such a way it is useful in decision making and running business. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-3","content":[{"nodeType":"text","value":"Conclusions ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"When it comes to buying analytics, the choice is clear. You should choose a competent data scientist who understands the customer. At Nitor, we focus on resolving the customer’s problems in a business-oriented way, with tools best suited to the task at hand instead of relying on technology-driven solutions. If you would like to know more, we’d be happy to come and start charting potential development targets and creating business value for you.  ","marks":[],"data":{}}],"data":{}}]},"authors":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6LCYDgXGe75zxb2BEb9Mp3","type":"Entry","createdAt":"2020-12-08T09:46:11.310Z","updatedAt":"2020-12-17T21:54:04.673Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":4,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"person"}},"locale":"en-US"},"fields":{"name":"Samuli Visuri","jobTitle":"Managing director, Analytics services","email":"samuli.visuri@nitor.com","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2ezVsrZfIaOggjXWq56qMy","type":"Asset","createdAt":"2020-11-27T17:22:24.531Z","updatedAt":"2020-11-27T17:22:24.531Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"Samuli Visuri profile blue cropped","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/2ezVsrZfIaOggjXWq56qMy/f6b0a87fc8bfc8e22b192c793adc1a04/Samuli_Visuri_profile_blue_cropped.jpg","details":{"size":1787811,"image":{"width":2000,"height":2000}},"fileName":"Samuli_Visuri_profile_blue_cropped.jpg","contentType":"image/jpeg"}}}}}],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"cio-move-from-collecting-data-to-utilising-it","publishDate":"2020-05-27","showRelatedArticles":true}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"42YMVYO76Jx4qNct4V86V1","type":"Entry","createdAt":"2020-11-27T17:30:46.555Z","updatedAt":"2020-12-09T08:54:30.831Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":3,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["en","fi"],"title":"Providing Business Value with Data Science at Finnair: The Five-Step Model","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"VYijgbOjloAoQfD3OWxgL","type":"Asset","createdAt":"2020-11-27T15:39:00.761Z","updatedAt":"2020-11-27T16:26:38.909Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"locale":"en-US"},"fields":{"title":"FIN_Airbus_A330_high_Resolution_jpg_79.jpg","description":"Photo: Finnair, 3D Aviation / Ole Wind Andersen","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/VYijgbOjloAoQfD3OWxgL/db6912fc523018fbebccb72a8531f55d/FIN_Airbus_A330_high_Resolution_jpg_79.jpg","details":{"size":926226,"image":{"width":1985,"height":1197}},"fileName":"FIN Airbus A330 high Resolution jpg_79.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"We had the opportunity of improving Finnair's Operational Analytics efforts with Data Science. In this blog post, we'll walk you through the five key steps we took to ensure the best possible outcome.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Airline operations are complex. Keeping flights in schedule, rerouting passengers who missed their connections, and in general, reacting to unexpected events are just some problems Operations Controllers must deal with, while considering factors such as weather conditions, travel restrictions, and crew availability. In managing operations, airlines make numerous decisions every day, the outcomes of which may depend on each other and which can have ripple effects on many flights. For example, delaying a flight to wait for connecting passengers may cause the aircraft to arrive late on its return flight, possibly leading to other passengers missing their connecting flights.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Such effects depend on many factors, such as the passenger makeup of all affected flights, which makes it difficult to weigh their importance in decision making. Consequently, coarse, heuristic models have traditionally been applied, which can simplify the problem but also suffer from biases, leading to suboptimal decisions. In today's competitive market, in order for airlines to provide the best quality of service, it's increasingly important to take full advantage of their data assets in operational decision making.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Recently, we had an opportunity to do Data Science work with Finnair, focusing on their Operational Analytics efforts. Our work resulted in mathematical models and software tools, the use of which significantly improved customer experience, improved workflows in the Operations Control Center, and resulted in remarkable cost savings. In this blog post, we describe the five main steps that contributed to the success of our journey with Finnair.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"1. Identifying the Problem","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Working at an airline of Finnair's scale is, for a Data Scientist, like a dream come true: the data is plentiful and well organized, and there are seemingly endless optimization problems to solve. Of course, as for any mature industry, many of the fundamental problems are already solved. Thus, when we started our work, the first questions were: what problems we can solve, and what problems should we solve utilizing the customer's data assets to create as much value as possible, in terms of both financial and customer success.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When starting a project, our goal as consultants is to understand the challenges the customer is facing. Thus, it's natural to begin by workshopping with the customer's domain experts. In this phase, focus is the key: real-life situations always consist of many interacting variables, and the amount of details can be daunting. Filtering out irrelevant characteristics of the problem and striving to simplify a possible solution will pay off in the form of better results, faster.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When we entered Finnair, a high-level list of important business problems had already been collected in internal workshops. Together, we prioritized this list and considered potential solutions in more detail. We quickly identified that the most useful starting point would be a mathematical model of the passengers' journeys end-to-end, to replace less accurate, higher-level indicators that were used previously. This would allow us to paint a clearer picture of upcoming and ongoing flights, leading to easier and more informed decision-making.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"2. Getting the First Version Out","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After choosing the first problem to tackle, we set out to build a Minimum Viable Product (MVP). In software, an MVP is an application with as few features as possible to satisfy users.The goal is that after release, new features will be added in response to feedback from users. By contrast, in a Data Science product, an MVP can also involve including simplifying assumptions in a model. The idea is that it's easier to engage stakeholders in a feedback loop when they can interact with the results and validate them against their own domain knowledge. The model can then be made more complex in response to feedback, if it turns out to be inadequate.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When starting to work on the MVP, a lot of subtle technical and data-related aspects need to be clarified with the customer. Typical questions addressed in this phase include, for instance, what are the right data sources to be used when solving the problem, and how the varying data sources need to be combined, cleaned and pre-processed for further modeling efforts. Diving into the data, formulating and validating assumptions of the models, and fleshing out initial solution designs makes it clear if the planned approach will work. From the customer’s point of view, this phase validates that the link between domain knowledge and the data is correctly interpreted and verifies that results produced by the model seem reasonable. Often, this phase also produces secondary business value as it can, for example, reveal deficiencies in data quality and clarify some aspects of existing business processes.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"In our work with Finnair, there was a great deal of domain knowledge to digest in the first few weeks in particular. Luckily, we were able to work closely together with their helpful and extremely knowledgeable staff, and we managed to have an MVP, a Python application periodically running the model and an R Shiny visualization, running in two months.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"3. Gathering Feedback and Iterative Development of the Model","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After the MVP was running, we started gathering feedback from the customer domain experts and stakeholders. From this feedback, it became clear where further development efforts should be concentrated.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Some key insights from this process were:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"For users to be engaged and provide feedback, the user experience needs to be smooth, and there needs to be a clear feedback channel.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"In order for expert users to trust a model, they need transparency over what goes in and what comes out.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"While iterative development continues after this initial MVP stage, the initial iteration is crucial as it improves the application holistically and makes it more robust for the next phase. For instance, with Finnair, the iterative development led into leaving out some irrelevant features in the user interfaces of the applications while at the same time including other features that were initially not seen important. End users also spotted several exceptional cases that were not handled correctly by the initial models, which led to improving them further.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"4. Evolution into a “Production-Ready” Application","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After the application was iterated on and validated through initial feedback, it started to be ready for wider adoption. By following good software design practices from the beginning, transitioning from the MVP into a production application was seamless. To achieve this, some key points we followed were:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Portability: in Python, for example, using virtual environments or containers ensures that the code works when deployed in different environments (e.g. moving from development to staging).","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Maintainability: writing modular code makes it easy to make modifications and switch out dependencies when required; following best practices of software version control helps keep track of changes and is useful in debugging.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Performance: in data-intensive applications, designing code to be performant from the start helps keep the development feedback loop fast. In near-real-time applications like ours, it is also crucial to ensure that adding complexity to models doesn't degrade performance significantly. Luckily, Data Science-oriented software libraries like Pandas are written with performance in mind, so it's often enough to follow best practices.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Customer's needs: it is important to select tools that the customer is invested in; common reporting tools for results, and programming languages that are commonly used in the organization (e.g., with Finnair, it was deemed better to transition to a reporting tool widely used in the organization for presenting results instead of the initial custom tools developed).","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Monitoring and security have to be considered thoroughly.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"In our project, we were in the \"production ready\" stage in about five months, which is when it was introduced to a larger group of users. At this stage, we dropped the custom dashboard built in the MVP, and delivered results in Finnair's internal Business Intelligence environment; this had two main benefits:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"it was already familiar to the users, so adoption was frictionless, and ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"managing user access became trivial. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"ordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"All in all, the transition to a wider user base was surprisingly smooth, although the amount of feedback naturally increased remarkably. Thus, we introduced some custom forms for collecting and storing feedback, which significantly improved the feedback loop.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"5. A Suite of Further Applications","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"At this point, we had a mathematical model of the core business. While still being iteratively developed, it was mature enough to yield more accurate insight than previous solutions. Having selected the initial problem carefully, we knew that the core model created opportunities for a suite of other tools and optimization processes to further improve the efficiency of business operations. Thus, together with Finnair, we planned several new MVPs that would build on this model. There were two motivations for moving forward with multiple applications: on the one hand, we wanted to quickly create more value where it was possible; and on the other hand, we wanted to lay out clarified directions for Finnair’s operational analytics by exploring the application space and validating our ideas.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The suite of applications developed was adopted to daily operational use smoothly, which was supported by training sessions for the end users as well as an on-going gathering of feedback. Sharing information about the tools for the organization and quickly incorporating improvement suggestions to the models enabled the inclusion of key people in the development efforts and fast extraction of value from the tools. This resulted in increased customer satisfaction as well as significant cost savings at Finnair. In conclusion, the five-step data science process described in this blog post has proven to be a valuable guideline as Finnair’s operational analytics area continues to evolve.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"authors":[],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"providing-business-value-data-science-finnair-five-step-model","publishDate":"2020-11-23","showRelatedArticles":true}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"tkwqWR8RXJGlzjfyYWaDo4","type":"Entry","createdAt":"2020-12-08T14:29:54.239Z","updatedAt":"2020-12-08T14:29:54.239Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"​Nitor invests in Artificial Intelligence","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"yIT7UV7fRq2t77qvxdrTY1","type":"Asset","createdAt":"2020-11-27T16:54:46.801Z","updatedAt":"2020-11-27T16:54:46.801Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"nitor-insight-2018-05.jpg","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/yIT7UV7fRq2t77qvxdrTY1/419f76a258461d9f1de097224763773e/nitor-insight-2018-05.jpg","details":{"size":361936,"image":{"width":2280,"height":1282}},"fileName":"nitor-insight-2018-05.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Nitor has founded a new business focused on Artificial Intelligence. Nitor Insight will offer research and consulting services on data science, AI, and machine learning. Nitor Insight started its operations in early 2018 and will be developed into a significant part of Nitor’s services and business.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"italic"}],"value":"Nitor Insight’s launch has been prepared by Nitor’s Data Scientists ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Sami Airaksinen","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":" , ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Tuomas Ritola","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":", and ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Juha Syrjälä","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":" as well as Principal Consultant ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Otto Manninen","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":".","nodeType":"text"},{"data":{},"marks":[],"value":"\n","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“We are extremely excited about launching Nitor Insight. AI and machine learning are an essential part of sustainable digital development. By broadening our offering, we can produce even better services for our current customers as well as target entirely new business areas,” ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"Matti Vilmi","nodeType":"text"},{"data":{},"marks":[],"value":", Nitor’s CEO, explains.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"AI, machine learning, and data science have become increasingly popular within the past few years and continue to be hot topics. Advanced technologies, increasing amounts of data, and the development of platforms such as Amazon, Google, and Microsoft, make the solutions an even bigger part of everyday life.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“Our customers handle large amounts of data, and machine learning is a way to create value out of this data. We have utilized machine learning throughout the years in making prognoses and recommendations within, for example, the media, finance, retail, logistics, and transportation industries. Lately, we have especially worked with different kinds of customer-service chatbots and other speech-related services,” Otto Manninen says.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Even though there is plenty of hype around AI, practicality and creating real value is a priority at Nitor Insight. Machine learning enables automation of solutions that wouldn’t have been sensible with traditional methods. People can focus on solving more important problems, aided by machines.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“The benefits are obvious: machine learning is now mature enough that it can be used in business-critical systems. Consumers are also increasingly starting to demand intelligent functions from all the services they use. And these are now often based on machine learning,” Tuomas Ritola explains.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"In addition to automating routine actions as well as supplementing current digital services, AI and machine learning offer many interesting new possibilities. Ideas move quickly from the academic world into productization and commercialization.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“The development of speech recognition and natural language processing has created opportunities for new kinds of chat-based services. The AI platforms and personal assistants from the big service providers will slowly change people’s habits and expectations of how to transact with businesses,” Juha Syrjälä says.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“I would estimate that the next big discussion topic after deep learning will be Generative Adversarial (Neural) Networks or GANs. These neural networks enable AI to learn how to create realistic photographs and other visualizations. It will be also interesting to see when Neural Processing Unit or NPU-accelerated devices become common and alter the current computation ecosystems,” Sami Airaksinen adds.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"With Nitor Insight, AI, machine learning, and data science are now a significant part of Nitor’s services. 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But what builds business resilience? To put it simply, quick reflexes and customer-focused innovation. We can help you forge the path to resilience with an adaptive digital strategy that anticipates changes in the business environment and your customer's needs. You'll learn to develop flexible structures, identify opportunities and lead the path to change. 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How can you develop your business, when an increasing amount of uncontrollable change factors are affecting customer behaviour, expectations and even the chances of accessing the services offered?","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"We are still weary after the onslaught of COVID-19 last spring, but positive expectations help us all move on. However, few organisations can afford to rely on glorious expectations alone. The past months have revealed how vulnerable, but also how agile companies are in terms of change. But most of all, the experience has taught us what it is we need to do right now in order to be successful in the future.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"I have discussed with several business executives over the past weeks and followed general financial news, and I think it’s safe to say that many business fundamentals are in disarray. It is not just the service channels and production lines that have undergone transformation: customer choices, business models and their implementation have changed as well. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Business models, decision-making, talent flexibility and operational agility have been put to the test in many business sectors. As many restaurants had to close their doors, employees were transferred to grocery stores to support their online shopping and delivery service capacity. ABC service stations started delivering pre-ordered meals safely and easily straight to cars. The design and production processes of display furnishers were tuned to the production of plexiglass shields for the checkout counters of take away restaurants and retail stores. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The ability to innovate, experiment and quickly implement has saved many companies from dire straits.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Look ahead","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"In the midst of the pandemic we go from one phase to another and try to predict the next ones. We identify and assess different measures that we can adopt in order to prepare for the next phases and to overcome them. 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It is also possible to utilise","nodeType":"text"},{"data":{"target":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"QEskfaQufwc7FyhMdGZMP","type":"Entry","createdAt":"2020-12-08T09:46:54.481Z","updatedAt":"2020-12-17T13:30:36.198Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":12,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"CIO - move from collecting data to utilising it","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"5NAZ0U8qKv6cSVfigvEp35","type":"Asset","createdAt":"2020-11-27T17:02:28.455Z","updatedAt":"2020-12-08T09:44:03.496Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"locale":"en-US"},"fields":{"title":"Samuli Visuri sitting ","description":"Samuli Visuri, People","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/5NAZ0U8qKv6cSVfigvEp35/cdcaa6e505a839fe24d5a870c6a2f389/Samuli_Visuri_portrait.jpg","details":{"size":320976,"image":{"width":1792,"height":1008}},"fileName":"Samuli_Visuri_portrait.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The revolution in using data is based on the increase in computing power and open source development. The algorithms needed for data processing and predictive analytics are already freely available. Read the advice of Samuli Visuri, Nitor's analytics business director, on how to make the most of analytics.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With regard to artificial intelligence and machine learning, there is a common illusion a single technology, software, or algorithm would help a company boost its business. As if there would be some sort of a black box, which digests all the data and spits out an operational guideline.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Sorry to disappoint, there is no black box. There has been no new mystical breakthrough in algorithm development, and no software can standardise data processing and predicting the future. 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","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 3: Simplify and succeed ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" Business is fundamentally about predicting the future and taking action accordingly. The point of artificial intelligence and machine learning is not in the use of brilliant algorithms, but rather in the ability to simplify key business questions into mathematical models. The winner is the one who can formulate the right abstraction for the key question – in a way that it can be answered.  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ABC service stations started delivering pre-ordered meals safely and easily straight to cars. The design and production processes of display furnishers were tuned to the production of plexiglass shields for the checkout counters of take away restaurants and retail stores. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The ability to innovate, experiment and quickly implement has saved many companies from dire straits.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Look ahead","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"In the midst of the pandemic we go from one phase to another and try to predict the next ones. We identify and assess different measures that we can adopt in order to prepare for the next phases and to overcome them. After the initial shock, a growing number of companies are getting prepared for the post-pandemic world. It will come, and although it might be difficult to see during the acute crisis, the seeds for future growth are sown in the midst of the present crisis. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Consequently, I would like to encourage leaders to cast their eyes over the crisis and to engage their organisations to do so as well. While managers are doing their best to minimise the current financial and personal losses, it is essential that they also take time for devising visions and plans for the future.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Tolerate uncertainty","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"While the past months have taught us a lesson or two, we still cannot foretell the future. But we are certainly better equipped to prepare for it. A growing number of companies are developing and implementing tools for understanding and predicting future customer needs and factors that drive change in their operating environment.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Scenarios are modeled and evaluated on the basis of data, customer insight, business risks and opportunities. By selecting the appropriate scenario, companies can direct their future business while finding their balance in the current storm. But you should not waste too much time on selecting the right scenario. If you wait for the fog to subside before you make your pick, you have already lost.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Develop talents","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"A successful leader dares to break normative leadership structures. The ability to focus needs to be coupled with the ability to be sensitive to the buzz surrounding our operating environment; it is important to recognise and evaluate the flickering signals. Our tendency to rely on what we know and trust has been put to question. We no longer draw strength from past success and the lessons learnt. 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It is also possible to utilise","nodeType":"text"},{"data":{"target":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"QEskfaQufwc7FyhMdGZMP","type":"Entry","createdAt":"2020-12-08T09:46:54.481Z","updatedAt":"2020-12-17T13:30:36.198Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":12,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"CIO - move from collecting data to utilising it","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"5NAZ0U8qKv6cSVfigvEp35","type":"Asset","createdAt":"2020-11-27T17:02:28.455Z","updatedAt":"2020-12-08T09:44:03.496Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"locale":"en-US"},"fields":{"title":"Samuli Visuri sitting ","description":"Samuli Visuri, People","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/5NAZ0U8qKv6cSVfigvEp35/cdcaa6e505a839fe24d5a870c6a2f389/Samuli_Visuri_portrait.jpg","details":{"size":320976,"image":{"width":1792,"height":1008}},"fileName":"Samuli_Visuri_portrait.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The revolution in using data is based on the increase in computing power and open source development. The algorithms needed for data processing and predictive analytics are already freely available. Read the advice of Samuli Visuri, Nitor's analytics business director, on how to make the most of analytics.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With regard to artificial intelligence and machine learning, there is a common illusion a single technology, software, or algorithm would help a company boost its business. As if there would be some sort of a black box, which digests all the data and spits out an operational guideline.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Sorry to disappoint, there is no black box. There has been no new mystical breakthrough in algorithm development, and no software can standardise data processing and predicting the future. Unfortunately, results that really benefit the business are not created by connecting data sources into a crystal ball.   ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"However, getting started is not that complicated. The revolution in using data is based on increased computing capacity and open source development. Companies can set up a powerful computing environment in minutes, and with reasonable costs, if they are willing to use cloud services. The algorithms needed for data processing and predictive analytics are already freely available. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"The good news, then, is that the that business managers don’t have to whine about various technology choices. You don’t even need expensive licenses. All you need is an expert, who knows how to use and modify algorithms to suit your business needs. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 3: Simplify and succeed ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" Business is fundamentally about predicting the future and taking action accordingly. The point of artificial intelligence and machine learning is not in the use of brilliant algorithms, but rather in the ability to simplify key business questions into mathematical models. The winner is the one who can formulate the right abstraction for the key question – in a way that it can be answered.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 4: Acquire expertise that will benefit you immediately ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Almost without exception, the best option is to quickly implement a Proof-of-Concept or a smaller solution, which yields immediately measurable business benefits. This is, of course, easier said than done! However, an experienced professional is, together with business experts, able to find a suitable starting point. A consultant is not the best to advise on how to do business. But she knows the possibilities of using data and is able to harness large amounts of data in such a way it is useful in decision making and running business. 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Surviving the quick turns of last spring has given us the strength, ability and tools to design and decide how to be relevant in the future. Currently we are dismantling silos and breaking locked-in value chains and ecosystems. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Realignment and new partnerships will invigorate many. Flexible processes and the ability to react and decide quickly enable you to produce added value for customers, whose behaviour is still guided by a growing number of change factors, some of which are hard to predict. It is time to move on and to build the new normal proactively.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Nitor helps in the management of digital transformation, in the development of agile strategies and growth, in planning sustainable architecture solutions and in renewing organisational resilience. 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The algorithms needed for data processing and predictive analytics are already freely available. Read the advice of Samuli Visuri, Nitor's analytics business director, on how to make the most of analytics.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With regard to artificial intelligence and machine learning, there is a common illusion a single technology, software, or algorithm would help a company boost its business. As if there would be some sort of a black box, which digests all the data and spits out an operational guideline.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Sorry to disappoint, there is no black box. There has been no new mystical breakthrough in algorithm development, and no software can standardise data processing and predicting the future. Unfortunately, results that really benefit the business are not created by connecting data sources into a crystal ball.   ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"However, getting started is not that complicated. The revolution in using data is based on increased computing capacity and open source development. Companies can set up a powerful computing environment in minutes, and with reasonable costs, if they are willing to use cloud services. The algorithms needed for data processing and predictive analytics are already freely available. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"The good news, then, is that the that business managers don’t have to whine about various technology choices. You don’t even need expensive licenses. All you need is an expert, who knows how to use and modify algorithms to suit your business needs. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 3: Simplify and succeed ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" Business is fundamentally about predicting the future and taking action accordingly. The point of artificial intelligence and machine learning is not in the use of brilliant algorithms, but rather in the ability to simplify key business questions into mathematical models. The winner is the one who can formulate the right abstraction for the key question – in a way that it can be answered.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 4: Acquire expertise that will benefit you immediately ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Almost without exception, the best option is to quickly implement a Proof-of-Concept or a smaller solution, which yields immediately measurable business benefits. This is, of course, easier said than done! However, an experienced professional is, together with business experts, able to find a suitable starting point. A consultant is not the best to advise on how to do business. But she knows the possibilities of using data and is able to harness large amounts of data in such a way it is useful in decision making and running business. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-3","content":[{"nodeType":"text","value":"Conclusions ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"When it comes to buying analytics, the choice is clear. You should choose a competent data scientist who understands the customer. At Nitor, we focus on resolving the customer’s problems in a business-oriented way, with tools best suited to the task at hand instead of relying on technology-driven solutions. If you would like to know more, we’d be happy to come and start charting potential development targets and creating business value for you.  ","marks":[],"data":{}}],"data":{}}]},"authors":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6LCYDgXGe75zxb2BEb9Mp3","type":"Entry","createdAt":"2020-12-08T09:46:11.310Z","updatedAt":"2020-12-17T21:54:04.673Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":4,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"person"}},"locale":"en-US"},"fields":{"name":"Samuli Visuri","jobTitle":"Managing director, Analytics services","email":"samuli.visuri@nitor.com","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2ezVsrZfIaOggjXWq56qMy","type":"Asset","createdAt":"2020-11-27T17:22:24.531Z","updatedAt":"2020-11-27T17:22:24.531Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"Samuli Visuri profile blue cropped","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/2ezVsrZfIaOggjXWq56qMy/f6b0a87fc8bfc8e22b192c793adc1a04/Samuli_Visuri_profile_blue_cropped.jpg","details":{"size":1787811,"image":{"width":2000,"height":2000}},"fileName":"Samuli_Visuri_profile_blue_cropped.jpg","contentType":"image/jpeg"}}}}}],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"cio-move-from-collecting-data-to-utilising-it","publishDate":"2020-05-27","showRelatedArticles":true}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"uVrVvtieWJKXzDXiRrfx8","type":"Entry","createdAt":"2020-12-08T08:36:19.128Z","updatedAt":"2020-12-08T08:36:19.128Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["en"],"title":"DNA, a GPTW Winner, Set the Bar for Agile Sky High","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6hcWQEEC2lhQpVJ9sd6Ha","type":"Asset","createdAt":"2020-12-08T08:22:02.301Z","updatedAt":"2020-12-08T08:22:02.301Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"DNA headquarter","description":"Outside teleoperator DNA's headquarter, company flags","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/6hcWQEEC2lhQpVJ9sd6Ha/12266d1101d728f0dd8c1d071b7a00ad/Blogi_dna_ketteryydentila_analyysi.jpg","details":{"size":923137,"image":{"width":2400,"height":1350}},"fileName":"Blogi_dna_ketteryydentila_analyysi.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The Finnish mobile network operator DNA has done a fantastic job over the years in making its IT functions more agile. The agile development methodologies fit in well with DNA’s bold and experimentative culture.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"DNA had already discovered the benefits of agile methodologies through faster and smoother development, but they also noticed that it’s nowhere near enough to stop the agile transformation at the product development level. The best possible results are achieved when agility is adopted by the entire organization – including top management.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"To find out how to fully utilize the benefits of agile, DNA’s HR Director ","marks":[],"data":{}},{"nodeType":"text","value":"Marko Rissanen","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" asked us to check the status of agility in their organization with the help of our Current state analysis.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"“When the development needs were brought into the open, it helped DNA to take the next step towards bringing agile forward, with a strong mandate from the management,” Rissanen says.","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"What does the current state analysis entail? 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We also aim at including different operations throughout the organization as widely as possible.","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Who can benefit from the current\nstate analysis?","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"We have executed the analysis, for example, in a big organization in the catering industry. What we noticed was that the traditional project-based way to operate is way too stiff for fast-paced changes such as food trends. 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Two sets of eyes can see better, and working in pairs offers more depth to the analysis – working together is also a basic element of agile methodologies, so we are setting an example too.","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Hear more about the current state analysis","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"If you want to know more about how to have an insight about your current state of agility, get in touch with our 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They made the test environment public last June and are launching the actual network during 2020.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Bitcoin has now existed for over a decade, and almost everyone has owned bitcoin at some point – or at least knows someone who has. Still, bitcoin never became the digital money it was meant to be. Most probably, your local grocery store doesn’t accept it, nor do you use it when you need to transfer money to your friends or family. The reasons holding most of the cryptocurrencies back include their inability to scale and the volatility of their value.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Facebook is now on a mission to create a new crypto-currency for the masses, with features that target the problems with the previous ones:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Instead of creating the currency by mining and leaving the value to be determined by the market, Libra will have the value of the currency backed by a reserve of assets like bank deposits and government securities. This means that the value of Libra should only fluctuate based on the value of these backing assets.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Only trusted parties, the members of the Libra Association, are allowed to run servers that validate the transactions. Therefore, the proof-of-work mechanism is not required. This increases both the speed and throughput of processing transactions (and does not require the crazy amounts of electricity consumed by the big cryptocurrencies). The validating nodes still have to agree to confirm a transaction, and the network can tolerate up to ⅓ of the validators to be compromised.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"The test environment for Libra has been available since June, so developers and companies can already start getting prepared for making and accepting payments in Libra.The rest of this article explains the basic concepts of Libra and has code examples for creating transactions in the testnet to show you how easy transferring money can be with Libra.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Sending money to your friend shouldn’t be harder than getting them an Uber ride home. Libra has the potential to bridge the gap between traditional financial networks and new digital currency technology while reducing the costs for everyone — especially consumers.\n\n- Peter Hazlehurst, Head of Payments and Risk, Uber Technologies, Inc.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"blockquote"},{"data":{},"content":[{"data":{},"marks":[],"value":"Accounts","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"The only thing you need for setting up a new account for sending and receiving transactions is a public-private key pair you can create by using the Ed25519 signature scheme. This key-pair is used to sign the transactions digitally, and the account identifier (address) is derived from the public key by hashing. You can create a new account in the Libra system by sending money to it or by making an account creation transaction.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"After creating the key-pair, you can create the account address by making a SHA3 hash of the public key.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Transactions","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"To make a transaction, you need to specify the following information:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Transaction sequence number","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Gas amount. Gas is a small payment for making the transaction that will be deducted from the sender account.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Sender account address","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Expiration time","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The public key of the sending account","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Transaction script with arguments","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Signature","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"You also need to serialize the transaction data by using a serialization mechanism that is unique to Libra. The serialized data is then signed, and the transaction is ready to be sent to the Libra API.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The amount of money to be sent nor the receiver of the payment is not specified in the transaction attributes. This information is included in the transaction script and arguments, which are written in a Libra-specific programming language called Move.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"‘Move’ transaction script","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"The transaction script makes it possible for you to create much more complex transactions than simple money transfers from one account to another. These type of programmable transactions are also called “smart contracts”, and you can also specify for example several receivers for the money and define conditions that need to be met for the transaction to go through.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"For this, Libra has its own programming language called Move. A Libra -specific programming language was designed to efficiently and safely create logic around transactions.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"To get started, you don’t need to learn this new language, as there are ready-made scripts for most common use cases in the Libra code repository.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Libra API","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Libra API uses gRPC, which is a web-service framework created by Google. The API is defined in service definition files, which are used to generate the client code for different platforms. The testnet API is running at ","nodeType":"text"},{"data":{"uri":"http://ac.testnet.libra.org:8000"},"content":[{"data":{},"marks":[],"value":"http://ac.testnet.libra.org:8000","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" and can be accessed without registration.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"For testing purposes, Libra also provides a service for adding money (minting) to test accounts.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Behind the API, the transaction will go through different validations after which it will be sent to other validator nodes in the network. The validators will vote on the transaction to be executed, and the changes will then be written to the ledger. You can find a more detailed description from the Libra documentation: ","nodeType":"text"},{"data":{"uri":"https://developers.libra.org/docs/life-of-a-transaction"},"content":[{"data":{},"marks":[],"value":"https://developers.libra.org/docs/life-of-a-transaction","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Making the API call","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"JLibra is a Java client library I started implementing as an open source project after the testnet was made public. JLibra is used for creating transactions and querying information about the accounts. There are also several other client libraries available for different programming languages. By using a client library, you don’t need to implement the serialization, signing and the gRPC call yourself. If you are interested in the details of those, you can find them in the source code.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The first step is to generate key-pairs for the accounts that are involved in the transaction. ","nodeType":"text"},{"data":{"uri":"https://github.com/ketola/jlibra/blob/master/jlibra-examples/src/main/java/dev/jlibra/example/GenerateKeysExample.java"},"content":[{"data":{},"marks":[],"value":"GenerateKeysExample.java","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Next, you need to create some money for testing purposes. This is done using the Libra faucet API and is obviously only available in the test environment. ","nodeType":"text"},{"data":{"uri":"https://github.com/ketola/jlibra/blob/master/jlibra-examples/src/main/java/dev/jlibra/example/MintExample.java"},"content":[{"data":{},"marks":[],"value":"MintExample.java","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"After creating money in the accounts, you can check the balance by querying the API. From the response, you can also find the account sequence number for the transaction. ","nodeType":"text"},{"data":{"uri":"https://github.com/ketola/jlibra/blob/master/jlibra-examples/src/main/java/dev/jlibra/example/GetAccountStateExample.java"},"content":[{"data":{},"marks":[],"value":"GetAccountStateExample.java","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"ordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"Now you can transfer the money minted in step 2 between accounts.","nodeType":"text"},{"data":{"uri":"https://github.com/ketola/jlibra/blob/master/jlibra-examples/src/main/java/dev/jlibra/example/TransferExample.java"},"content":[{"data":{},"marks":[],"value":"TransferExample.java","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Hopefully, you got an idea of how simple and fast transferring money can be with Libra. 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Now your app can have all the good stuff from both native and web apps in one application and you have total control of the deployment (no app stores), encrypted notifications (not readable by third party services) and your users can choose to install the application on their devices allowing connectivity independence and native app-like experience.\n","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Sounds to good to be true?","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"Almost. The features are only available in the latest versions of the modern browsers but you can start implementing your progressive app today. Users with modern browsers get the good stuff immediately and others can use the same app the traditional way: through the browser window until support for these new features spread.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The share of Chrome users is already over 50% so I believe this is viable option, if you are considering a new application now. 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I no longer need to keep checking my emails or slack messages for system status notifications and I can keep those programs closed when I want to focus on something else.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"So how does it all work?","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"I have a Logitech G815 keyboard and unfortunately the LED configuration tool that ships with it (G-Hub) supports only some flashy animations and other pretty useless effects. But it does ship with an easy to use SDK, which you can use to control the LED colors from an external program!","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"So, I wrote a small program which controls the RGB LEDs on my keyboard. 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And by creatives, I mean all kinds of creative people involved in film, music, business, and – to a greater extend nowadays – digital design. A team of four nitoreans flew across the Atlantic to find out what this event might offer.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"SxSW kicked off with themes that touched the softer side of humanity. ","marks":[],"data":{}},{"nodeType":"text","value":"Esther","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" ","marks":[],"data":{}},{"nodeType":"text","value":"Perel","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" reasoned that our work environments are based on human relationships and we should prioritize them. 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Algorithms can save lives but when not done right they can truly make our lives worse. These notions bring up the responsibility of design to the fore.","marks":[],"data":{}}],"data":{}},{"nodeType":"blockquote","content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"As designers we are responsible in making the future happen and at the same time making sure it is one that we would want to live in.","marks":[],"data":{}}],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Technological singularity where there are more possibilities than we are producing is at our hands. There is still so much more that we can achieve through technology which should make everyone excited. We just need to keep our minds clear about the higher purpose so we can make great services and products that support people’s lives.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"After appearances by Hollywood stars like ","marks":[],"data":{}},{"nodeType":"text","value":"Elisabeth Moss","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" and music icons like the Beastie Boys, the festival is definitely not limited in any way. 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Powerful AI solutions can then use that data to automatically generate variations of interfaces, optimize inventories, balance server load and so much more. When a genetic algorithm can independently explore and test hundreds of alternative and choose the ones that are proven to work, who needs a designer anymore?","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Clearly data helps us optimize our systems based on their results,\n\nbut how do you know what to optimize for?","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"blockquote"},{"data":{},"content":[{"data":{},"marks":[],"value":"This is where design and data meet in an interesting way. 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What is the tone this AI assistant should ","nodeType":"text"},{"data":{"uri":"https://youtu.be/5nll1K1YHdQ?t=5h8m57s"},"content":[{"data":{},"marks":[],"value":"deliver news we can’t imagine yet","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":"?","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"},{"data":{"uri":"https://youtu.be/5nll1K1YHdQ?t=2h54m49s"},"content":[{"data":{},"marks":[],"value":"Zalando","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" presented a really useful model for understanding the relationship between data, intelligence and experience. Let’s say you’re building a smart service that helps grocery shoppers discover suitable recipes and gather them at their local grocery store. As the customer walks out of the store with flour, milk, sugar and eggs, ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"data","nodeType":"text"},{"data":{},"marks":[],"value":" is being generated. Based on the ingredients and the customer’s history, a solution’s ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"intelligence","nodeType":"text"},{"data":{},"marks":[],"value":" layer can draw a conclusion that this person is baking a cake. But the AI can’t understand the ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"experience","nodeType":"text"},{"data":{},"marks":[],"value":" of baking a cake with your friends, which is the reason the customer went to the store in the first place. 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With the help of our expert data scientists, we’ll select the critical data to deliver the most relevant insights for your business. There's no need for big investments in technology. We'll design a process that easily serves your entire organisation and helps everyone understand the business benefits. 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The algorithms needed for data processing and predictive analytics are already freely available. Read the advice of Samuli Visuri, Nitor's analytics business director, on how to make the most of analytics.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With regard to artificial intelligence and machine learning, there is a common illusion a single technology, software, or algorithm would help a company boost its business. As if there would be some sort of a black box, which digests all the data and spits out an operational guideline.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Sorry to disappoint, there is no black box. There has been no new mystical breakthrough in algorithm development, and no software can standardise data processing and predicting the future. Unfortunately, results that really benefit the business are not created by connecting data sources into a crystal ball.   ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"However, getting started is not that complicated. The revolution in using data is based on increased computing capacity and open source development. Companies can set up a powerful computing environment in minutes, and with reasonable costs, if they are willing to use cloud services. The algorithms needed for data processing and predictive analytics are already freely available. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"The good news, then, is that the that business managers don’t have to whine about various technology choices. You don’t even need expensive licenses. All you need is an expert, who knows how to use and modify algorithms to suit your business needs. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 3: Simplify and succeed ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" Business is fundamentally about predicting the future and taking action accordingly. The point of artificial intelligence and machine learning is not in the use of brilliant algorithms, but rather in the ability to simplify key business questions into mathematical models. The winner is the one who can formulate the right abstraction for the key question – in a way that it can be answered.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 4: Acquire expertise that will benefit you immediately ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Almost without exception, the best option is to quickly implement a Proof-of-Concept or a smaller solution, which yields immediately measurable business benefits. This is, of course, easier said than done! However, an experienced professional is, together with business experts, able to find a suitable starting point. A consultant is not the best to advise on how to do business. But she knows the possibilities of using data and is able to harness large amounts of data in such a way it is useful in decision making and running business. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-3","content":[{"nodeType":"text","value":"Conclusions ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"When it comes to buying analytics, the choice is clear. You should choose a competent data scientist who understands the customer. At Nitor, we focus on resolving the customer’s problems in a business-oriented way, with tools best suited to the task at hand instead of relying on technology-driven solutions. If you would like to know more, we’d be happy to come and start charting potential development targets and creating business value for you.  ","marks":[],"data":{}}],"data":{}}]},"authors":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6LCYDgXGe75zxb2BEb9Mp3","type":"Entry","createdAt":"2020-12-08T09:46:11.310Z","updatedAt":"2020-12-17T21:54:04.673Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":4,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"person"}},"locale":"en-US"},"fields":{"name":"Samuli Visuri","jobTitle":"Managing director, Analytics services","email":"samuli.visuri@nitor.com","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2ezVsrZfIaOggjXWq56qMy","type":"Asset","createdAt":"2020-11-27T17:22:24.531Z","updatedAt":"2020-11-27T17:22:24.531Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"Samuli Visuri profile blue cropped","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/2ezVsrZfIaOggjXWq56qMy/f6b0a87fc8bfc8e22b192c793adc1a04/Samuli_Visuri_profile_blue_cropped.jpg","details":{"size":1787811,"image":{"width":2000,"height":2000}},"fileName":"Samuli_Visuri_profile_blue_cropped.jpg","contentType":"image/jpeg"}}}}}],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"cio-move-from-collecting-data-to-utilising-it","publishDate":"2020-05-27","showRelatedArticles":true}}}}]}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"5fnCtE1n3J6l3hrfcIwKb6","type":"Entry","createdAt":"2020-11-11T06:51:22.374Z","updatedAt":"2020-12-15T19:31:53.522Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":6,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"expertiseBlock"}},"locale":"en-US"},"fields":{"title":"Business optimisation","paragraph":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Successful businesses predict future possibilities and act on them quickly. Strategies that can turn possibility into action form the basis of sustainable digital development. But you'd be wrong to think brilliant algorithms are at the core of all data-led operations. Real success is rooted in the ability to simplify key questions and present them as mathematical models. We can help you formulate the right abstract model for the right question - and deliver the answers your business has been looking for. ","marks":[],"data":{}}],"data":{}}]},"references":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6CWb2esZsQNFH1dItGAv2l","type":"Entry","createdAt":"2020-12-09T07:10:27.084Z","updatedAt":"2020-12-09T14:43:42.884Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"namedReference"}},"locale":"en-US"},"fields":{"name":"Read how we improved customer experience with data science","entry":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"42YMVYO76Jx4qNct4V86V1","type":"Entry","createdAt":"2020-11-27T17:30:46.555Z","updatedAt":"2020-12-09T08:54:30.831Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":3,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["en","fi"],"title":"Providing Business Value with Data Science at Finnair: The Five-Step Model","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"VYijgbOjloAoQfD3OWxgL","type":"Asset","createdAt":"2020-11-27T15:39:00.761Z","updatedAt":"2020-11-27T16:26:38.909Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"locale":"en-US"},"fields":{"title":"FIN_Airbus_A330_high_Resolution_jpg_79.jpg","description":"Photo: Finnair, 3D Aviation / Ole Wind Andersen","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/VYijgbOjloAoQfD3OWxgL/db6912fc523018fbebccb72a8531f55d/FIN_Airbus_A330_high_Resolution_jpg_79.jpg","details":{"size":926226,"image":{"width":1985,"height":1197}},"fileName":"FIN Airbus A330 high Resolution jpg_79.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"We had the opportunity of improving Finnair's Operational Analytics efforts with Data Science. In this blog post, we'll walk you through the five key steps we took to ensure the best possible outcome.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Airline operations are complex. Keeping flights in schedule, rerouting passengers who missed their connections, and in general, reacting to unexpected events are just some problems Operations Controllers must deal with, while considering factors such as weather conditions, travel restrictions, and crew availability. In managing operations, airlines make numerous decisions every day, the outcomes of which may depend on each other and which can have ripple effects on many flights. For example, delaying a flight to wait for connecting passengers may cause the aircraft to arrive late on its return flight, possibly leading to other passengers missing their connecting flights.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Such effects depend on many factors, such as the passenger makeup of all affected flights, which makes it difficult to weigh their importance in decision making. Consequently, coarse, heuristic models have traditionally been applied, which can simplify the problem but also suffer from biases, leading to suboptimal decisions. In today's competitive market, in order for airlines to provide the best quality of service, it's increasingly important to take full advantage of their data assets in operational decision making.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Recently, we had an opportunity to do Data Science work with Finnair, focusing on their Operational Analytics efforts. Our work resulted in mathematical models and software tools, the use of which significantly improved customer experience, improved workflows in the Operations Control Center, and resulted in remarkable cost savings. In this blog post, we describe the five main steps that contributed to the success of our journey with Finnair.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"1. Identifying the Problem","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Working at an airline of Finnair's scale is, for a Data Scientist, like a dream come true: the data is plentiful and well organized, and there are seemingly endless optimization problems to solve. Of course, as for any mature industry, many of the fundamental problems are already solved. Thus, when we started our work, the first questions were: what problems we can solve, and what problems should we solve utilizing the customer's data assets to create as much value as possible, in terms of both financial and customer success.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When starting a project, our goal as consultants is to understand the challenges the customer is facing. Thus, it's natural to begin by workshopping with the customer's domain experts. In this phase, focus is the key: real-life situations always consist of many interacting variables, and the amount of details can be daunting. Filtering out irrelevant characteristics of the problem and striving to simplify a possible solution will pay off in the form of better results, faster.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When we entered Finnair, a high-level list of important business problems had already been collected in internal workshops. Together, we prioritized this list and considered potential solutions in more detail. We quickly identified that the most useful starting point would be a mathematical model of the passengers' journeys end-to-end, to replace less accurate, higher-level indicators that were used previously. This would allow us to paint a clearer picture of upcoming and ongoing flights, leading to easier and more informed decision-making.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"2. Getting the First Version Out","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After choosing the first problem to tackle, we set out to build a Minimum Viable Product (MVP). In software, an MVP is an application with as few features as possible to satisfy users.The goal is that after release, new features will be added in response to feedback from users. By contrast, in a Data Science product, an MVP can also involve including simplifying assumptions in a model. The idea is that it's easier to engage stakeholders in a feedback loop when they can interact with the results and validate them against their own domain knowledge. The model can then be made more complex in response to feedback, if it turns out to be inadequate.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When starting to work on the MVP, a lot of subtle technical and data-related aspects need to be clarified with the customer. Typical questions addressed in this phase include, for instance, what are the right data sources to be used when solving the problem, and how the varying data sources need to be combined, cleaned and pre-processed for further modeling efforts. Diving into the data, formulating and validating assumptions of the models, and fleshing out initial solution designs makes it clear if the planned approach will work. From the customer’s point of view, this phase validates that the link between domain knowledge and the data is correctly interpreted and verifies that results produced by the model seem reasonable. Often, this phase also produces secondary business value as it can, for example, reveal deficiencies in data quality and clarify some aspects of existing business processes.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"In our work with Finnair, there was a great deal of domain knowledge to digest in the first few weeks in particular. Luckily, we were able to work closely together with their helpful and extremely knowledgeable staff, and we managed to have an MVP, a Python application periodically running the model and an R Shiny visualization, running in two months.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"3. Gathering Feedback and Iterative Development of the Model","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After the MVP was running, we started gathering feedback from the customer domain experts and stakeholders. From this feedback, it became clear where further development efforts should be concentrated.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Some key insights from this process were:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"For users to be engaged and provide feedback, the user experience needs to be smooth, and there needs to be a clear feedback channel.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"In order for expert users to trust a model, they need transparency over what goes in and what comes out.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"While iterative development continues after this initial MVP stage, the initial iteration is crucial as it improves the application holistically and makes it more robust for the next phase. For instance, with Finnair, the iterative development led into leaving out some irrelevant features in the user interfaces of the applications while at the same time including other features that were initially not seen important. End users also spotted several exceptional cases that were not handled correctly by the initial models, which led to improving them further.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"4. Evolution into a “Production-Ready” Application","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After the application was iterated on and validated through initial feedback, it started to be ready for wider adoption. By following good software design practices from the beginning, transitioning from the MVP into a production application was seamless. To achieve this, some key points we followed were:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Portability: in Python, for example, using virtual environments or containers ensures that the code works when deployed in different environments (e.g. moving from development to staging).","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Maintainability: writing modular code makes it easy to make modifications and switch out dependencies when required; following best practices of software version control helps keep track of changes and is useful in debugging.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Performance: in data-intensive applications, designing code to be performant from the start helps keep the development feedback loop fast. In near-real-time applications like ours, it is also crucial to ensure that adding complexity to models doesn't degrade performance significantly. Luckily, Data Science-oriented software libraries like Pandas are written with performance in mind, so it's often enough to follow best practices.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Customer's needs: it is important to select tools that the customer is invested in; common reporting tools for results, and programming languages that are commonly used in the organization (e.g., with Finnair, it was deemed better to transition to a reporting tool widely used in the organization for presenting results instead of the initial custom tools developed).","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Monitoring and security have to be considered thoroughly.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"In our project, we were in the \"production ready\" stage in about five months, which is when it was introduced to a larger group of users. At this stage, we dropped the custom dashboard built in the MVP, and delivered results in Finnair's internal Business Intelligence environment; this had two main benefits:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"it was already familiar to the users, so adoption was frictionless, and ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"managing user access became trivial. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"ordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"All in all, the transition to a wider user base was surprisingly smooth, although the amount of feedback naturally increased remarkably. Thus, we introduced some custom forms for collecting and storing feedback, which significantly improved the feedback loop.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"5. A Suite of Further Applications","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"At this point, we had a mathematical model of the core business. While still being iteratively developed, it was mature enough to yield more accurate insight than previous solutions. Having selected the initial problem carefully, we knew that the core model created opportunities for a suite of other tools and optimization processes to further improve the efficiency of business operations. Thus, together with Finnair, we planned several new MVPs that would build on this model. There were two motivations for moving forward with multiple applications: on the one hand, we wanted to quickly create more value where it was possible; and on the other hand, we wanted to lay out clarified directions for Finnair’s operational analytics by exploring the application space and validating our ideas.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The suite of applications developed was adopted to daily operational use smoothly, which was supported by training sessions for the end users as well as an on-going gathering of feedback. Sharing information about the tools for the organization and quickly incorporating improvement suggestions to the models enabled the inclusion of key people in the development efforts and fast extraction of value from the tools. This resulted in increased customer satisfaction as well as significant cost savings at Finnair. In conclusion, the five-step data science process described in this blog post has proven to be a valuable guideline as Finnair’s operational analytics area continues to evolve.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"authors":[],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"providing-business-value-data-science-finnair-five-step-model","publishDate":"2020-11-23","showRelatedArticles":true}}}}]}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"60V2KyzLyECDfQbUZjN0wl","type":"Entry","createdAt":"2020-11-11T06:52:15.070Z","updatedAt":"2020-12-15T19:32:12.607Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":7,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"expertiseBlock"}},"locale":"en-US"},"fields":{"title":"Machine learning and artificial intelligence","paragraph":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Machine learning and artificial intelligence can solve classification problems and detect patterns in large volumes of data efficiently. 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We can help you efficiently map out applications for artificial intelligence and convert its models into products.","marks":[],"data":{}}],"data":{}}]},"references":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"1ueCpzpX0l4egaRjz1NbuR","type":"Entry","createdAt":"2020-12-09T07:14:51.015Z","updatedAt":"2020-12-09T14:43:06.744Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"namedReference"}},"locale":"en-US"},"fields":{"name":"Nitor invests in Artificial Intelligence","entry":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"tkwqWR8RXJGlzjfyYWaDo4","type":"Entry","createdAt":"2020-12-08T14:29:54.239Z","updatedAt":"2020-12-08T14:29:54.239Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"​Nitor invests in Artificial Intelligence","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"yIT7UV7fRq2t77qvxdrTY1","type":"Asset","createdAt":"2020-11-27T16:54:46.801Z","updatedAt":"2020-11-27T16:54:46.801Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"nitor-insight-2018-05.jpg","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/yIT7UV7fRq2t77qvxdrTY1/419f76a258461d9f1de097224763773e/nitor-insight-2018-05.jpg","details":{"size":361936,"image":{"width":2280,"height":1282}},"fileName":"nitor-insight-2018-05.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Nitor has founded a new business focused on Artificial Intelligence. Nitor Insight will offer research and consulting services on data science, AI, and machine learning. Nitor Insight started its operations in early 2018 and will be developed into a significant part of Nitor’s services and business.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"italic"}],"value":"Nitor Insight’s launch has been prepared by Nitor’s Data Scientists ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Sami Airaksinen","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":" , ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Tuomas Ritola","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":", and ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Juha Syrjälä","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":" as well as Principal Consultant ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Otto Manninen","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":".","nodeType":"text"},{"data":{},"marks":[],"value":"\n","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“We are extremely excited about launching Nitor Insight. AI and machine learning are an essential part of sustainable digital development. By broadening our offering, we can produce even better services for our current customers as well as target entirely new business areas,” ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"Matti Vilmi","nodeType":"text"},{"data":{},"marks":[],"value":", Nitor’s CEO, explains.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"AI, machine learning, and data science have become increasingly popular within the past few years and continue to be hot topics. Advanced technologies, increasing amounts of data, and the development of platforms such as Amazon, Google, and Microsoft, make the solutions an even bigger part of everyday life.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“Our customers handle large amounts of data, and machine learning is a way to create value out of this data. We have utilized machine learning throughout the years in making prognoses and recommendations within, for example, the media, finance, retail, logistics, and transportation industries. Lately, we have especially worked with different kinds of customer-service chatbots and other speech-related services,” Otto Manninen says.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Even though there is plenty of hype around AI, practicality and creating real value is a priority at Nitor Insight. Machine learning enables automation of solutions that wouldn’t have been sensible with traditional methods. People can focus on solving more important problems, aided by machines.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“The benefits are obvious: machine learning is now mature enough that it can be used in business-critical systems. Consumers are also increasingly starting to demand intelligent functions from all the services they use. And these are now often based on machine learning,” Tuomas Ritola explains.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"In addition to automating routine actions as well as supplementing current digital services, AI and machine learning offer many interesting new possibilities. Ideas move quickly from the academic world into productization and commercialization.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“The development of speech recognition and natural language processing has created opportunities for new kinds of chat-based services. The AI platforms and personal assistants from the big service providers will slowly change people’s habits and expectations of how to transact with businesses,” Juha Syrjälä says.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“I would estimate that the next big discussion topic after deep learning will be Generative Adversarial (Neural) Networks or GANs. These neural networks enable AI to learn how to create realistic photographs and other visualizations. It will be also interesting to see when Neural Processing Unit or NPU-accelerated devices become common and alter the current computation ecosystems,” Sami Airaksinen adds.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"With Nitor Insight, AI, machine learning, and data science are now a significant part of Nitor’s services. They are a part of Nitor’s sustainable digital development that already includes pragmatic architecture, design, large-scale agility, and technology services.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"nitor-invests-artificial-intelligence","publishDate":"2018-06-13"}}}}]}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6IMKZh2y8erek8LfALQz3P","type":"Entry","createdAt":"2020-11-11T06:53:12.936Z","updatedAt":"2020-12-15T19:32:28.180Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":5,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"expertiseBlock"}},"locale":"en-US"},"fields":{"title":"Data storage and management","paragraph":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With the right data storage and management system, you can enable seamless development and elegant use of analytics, knowledge-based management and other data-based services. 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The algorithms needed for data processing and predictive analytics are already freely available. Read the advice of Samuli Visuri, Nitor's analytics business director, on how to make the most of analytics.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With regard to artificial intelligence and machine learning, there is a common illusion a single technology, software, or algorithm would help a company boost its business. As if there would be some sort of a black box, which digests all the data and spits out an operational guideline.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Sorry to disappoint, there is no black box. There has been no new mystical breakthrough in algorithm development, and no software can standardise data processing and predicting the future. Unfortunately, results that really benefit the business are not created by connecting data sources into a crystal ball.   ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"However, getting started is not that complicated. The revolution in using data is based on increased computing capacity and open source development. Companies can set up a powerful computing environment in minutes, and with reasonable costs, if they are willing to use cloud services. The algorithms needed for data processing and predictive analytics are already freely available. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"The good news, then, is that the that business managers don’t have to whine about various technology choices. You don’t even need expensive licenses. All you need is an expert, who knows how to use and modify algorithms to suit your business needs. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 3: Simplify and succeed ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" Business is fundamentally about predicting the future and taking action accordingly. The point of artificial intelligence and machine learning is not in the use of brilliant algorithms, but rather in the ability to simplify key business questions into mathematical models. The winner is the one who can formulate the right abstraction for the key question – in a way that it can be answered.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 4: Acquire expertise that will benefit you immediately ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Almost without exception, the best option is to quickly implement a Proof-of-Concept or a smaller solution, which yields immediately measurable business benefits. This is, of course, easier said than done! However, an experienced professional is, together with business experts, able to find a suitable starting point. A consultant is not the best to advise on how to do business. But she knows the possibilities of using data and is able to harness large amounts of data in such a way it is useful in decision making and running business. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-3","content":[{"nodeType":"text","value":"Conclusions ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"When it comes to buying analytics, the choice is clear. You should choose a competent data scientist who understands the customer. At Nitor, we focus on resolving the customer’s problems in a business-oriented way, with tools best suited to the task at hand instead of relying on technology-driven solutions. If you would like to know more, we’d be happy to come and start charting potential development targets and creating business value for you.  ","marks":[],"data":{}}],"data":{}}]},"authors":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6LCYDgXGe75zxb2BEb9Mp3","type":"Entry","createdAt":"2020-12-08T09:46:11.310Z","updatedAt":"2020-12-17T21:54:04.673Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":4,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"person"}},"locale":"en-US"},"fields":{"name":"Samuli Visuri","jobTitle":"Managing director, Analytics services","email":"samuli.visuri@nitor.com","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2ezVsrZfIaOggjXWq56qMy","type":"Asset","createdAt":"2020-11-27T17:22:24.531Z","updatedAt":"2020-11-27T17:22:24.531Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"Samuli Visuri profile blue cropped","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/2ezVsrZfIaOggjXWq56qMy/f6b0a87fc8bfc8e22b192c793adc1a04/Samuli_Visuri_profile_blue_cropped.jpg","details":{"size":1787811,"image":{"width":2000,"height":2000}},"fileName":"Samuli_Visuri_profile_blue_cropped.jpg","contentType":"image/jpeg"}}}}}],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"cio-move-from-collecting-data-to-utilising-it","publishDate":"2020-05-27","showRelatedArticles":true}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"42YMVYO76Jx4qNct4V86V1","type":"Entry","createdAt":"2020-11-27T17:30:46.555Z","updatedAt":"2020-12-09T08:54:30.831Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":3,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["en","fi"],"title":"Providing Business Value with Data Science at Finnair: The Five-Step Model","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"VYijgbOjloAoQfD3OWxgL","type":"Asset","createdAt":"2020-11-27T15:39:00.761Z","updatedAt":"2020-11-27T16:26:38.909Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"locale":"en-US"},"fields":{"title":"FIN_Airbus_A330_high_Resolution_jpg_79.jpg","description":"Photo: Finnair, 3D Aviation / Ole Wind Andersen","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/VYijgbOjloAoQfD3OWxgL/db6912fc523018fbebccb72a8531f55d/FIN_Airbus_A330_high_Resolution_jpg_79.jpg","details":{"size":926226,"image":{"width":1985,"height":1197}},"fileName":"FIN Airbus A330 high Resolution jpg_79.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"We had the opportunity of improving Finnair's Operational Analytics efforts with Data Science. In this blog post, we'll walk you through the five key steps we took to ensure the best possible outcome.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Airline operations are complex. Keeping flights in schedule, rerouting passengers who missed their connections, and in general, reacting to unexpected events are just some problems Operations Controllers must deal with, while considering factors such as weather conditions, travel restrictions, and crew availability. In managing operations, airlines make numerous decisions every day, the outcomes of which may depend on each other and which can have ripple effects on many flights. For example, delaying a flight to wait for connecting passengers may cause the aircraft to arrive late on its return flight, possibly leading to other passengers missing their connecting flights.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Such effects depend on many factors, such as the passenger makeup of all affected flights, which makes it difficult to weigh their importance in decision making. Consequently, coarse, heuristic models have traditionally been applied, which can simplify the problem but also suffer from biases, leading to suboptimal decisions. In today's competitive market, in order for airlines to provide the best quality of service, it's increasingly important to take full advantage of their data assets in operational decision making.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Recently, we had an opportunity to do Data Science work with Finnair, focusing on their Operational Analytics efforts. Our work resulted in mathematical models and software tools, the use of which significantly improved customer experience, improved workflows in the Operations Control Center, and resulted in remarkable cost savings. In this blog post, we describe the five main steps that contributed to the success of our journey with Finnair.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"1. Identifying the Problem","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"Working at an airline of Finnair's scale is, for a Data Scientist, like a dream come true: the data is plentiful and well organized, and there are seemingly endless optimization problems to solve. Of course, as for any mature industry, many of the fundamental problems are already solved. Thus, when we started our work, the first questions were: what problems we can solve, and what problems should we solve utilizing the customer's data assets to create as much value as possible, in terms of both financial and customer success.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When starting a project, our goal as consultants is to understand the challenges the customer is facing. Thus, it's natural to begin by workshopping with the customer's domain experts. In this phase, focus is the key: real-life situations always consist of many interacting variables, and the amount of details can be daunting. Filtering out irrelevant characteristics of the problem and striving to simplify a possible solution will pay off in the form of better results, faster.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When we entered Finnair, a high-level list of important business problems had already been collected in internal workshops. Together, we prioritized this list and considered potential solutions in more detail. We quickly identified that the most useful starting point would be a mathematical model of the passengers' journeys end-to-end, to replace less accurate, higher-level indicators that were used previously. This would allow us to paint a clearer picture of upcoming and ongoing flights, leading to easier and more informed decision-making.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"2. Getting the First Version Out","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After choosing the first problem to tackle, we set out to build a Minimum Viable Product (MVP). In software, an MVP is an application with as few features as possible to satisfy users.The goal is that after release, new features will be added in response to feedback from users. By contrast, in a Data Science product, an MVP can also involve including simplifying assumptions in a model. The idea is that it's easier to engage stakeholders in a feedback loop when they can interact with the results and validate them against their own domain knowledge. The model can then be made more complex in response to feedback, if it turns out to be inadequate.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"When starting to work on the MVP, a lot of subtle technical and data-related aspects need to be clarified with the customer. Typical questions addressed in this phase include, for instance, what are the right data sources to be used when solving the problem, and how the varying data sources need to be combined, cleaned and pre-processed for further modeling efforts. Diving into the data, formulating and validating assumptions of the models, and fleshing out initial solution designs makes it clear if the planned approach will work. From the customer’s point of view, this phase validates that the link between domain knowledge and the data is correctly interpreted and verifies that results produced by the model seem reasonable. Often, this phase also produces secondary business value as it can, for example, reveal deficiencies in data quality and clarify some aspects of existing business processes.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"In our work with Finnair, there was a great deal of domain knowledge to digest in the first few weeks in particular. Luckily, we were able to work closely together with their helpful and extremely knowledgeable staff, and we managed to have an MVP, a Python application periodically running the model and an R Shiny visualization, running in two months.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"3. Gathering Feedback and Iterative Development of the Model","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After the MVP was running, we started gathering feedback from the customer domain experts and stakeholders. From this feedback, it became clear where further development efforts should be concentrated.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Some key insights from this process were:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"For users to be engaged and provide feedback, the user experience needs to be smooth, and there needs to be a clear feedback channel.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"In order for expert users to trust a model, they need transparency over what goes in and what comes out.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"While iterative development continues after this initial MVP stage, the initial iteration is crucial as it improves the application holistically and makes it more robust for the next phase. For instance, with Finnair, the iterative development led into leaving out some irrelevant features in the user interfaces of the applications while at the same time including other features that were initially not seen important. End users also spotted several exceptional cases that were not handled correctly by the initial models, which led to improving them further.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"4. Evolution into a “Production-Ready” Application","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"After the application was iterated on and validated through initial feedback, it started to be ready for wider adoption. By following good software design practices from the beginning, transitioning from the MVP into a production application was seamless. To achieve this, some key points we followed were:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Portability: in Python, for example, using virtual environments or containers ensures that the code works when deployed in different environments (e.g. moving from development to staging).","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Maintainability: writing modular code makes it easy to make modifications and switch out dependencies when required; following best practices of software version control helps keep track of changes and is useful in debugging.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Performance: in data-intensive applications, designing code to be performant from the start helps keep the development feedback loop fast. In near-real-time applications like ours, it is also crucial to ensure that adding complexity to models doesn't degrade performance significantly. Luckily, Data Science-oriented software libraries like Pandas are written with performance in mind, so it's often enough to follow best practices.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Customer's needs: it is important to select tools that the customer is invested in; common reporting tools for results, and programming languages that are commonly used in the organization (e.g., with Finnair, it was deemed better to transition to a reporting tool widely used in the organization for presenting results instead of the initial custom tools developed).","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Monitoring and security have to be considered thoroughly.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"unordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"In our project, we were in the \"production ready\" stage in about five months, which is when it was introduced to a larger group of users. At this stage, we dropped the custom dashboard built in the MVP, and delivered results in Finnair's internal Business Intelligence environment; this had two main benefits:","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"it was already familiar to the users, so adoption was frictionless, and ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"},{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"managing user access became trivial. ","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"list-item"}],"nodeType":"ordered-list"},{"data":{},"content":[{"data":{},"marks":[],"value":"All in all, the transition to a wider user base was surprisingly smooth, although the amount of feedback naturally increased remarkably. Thus, we introduced some custom forms for collecting and storing feedback, which significantly improved the feedback loop.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"5. A Suite of Further Applications","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"At this point, we had a mathematical model of the core business. While still being iteratively developed, it was mature enough to yield more accurate insight than previous solutions. Having selected the initial problem carefully, we knew that the core model created opportunities for a suite of other tools and optimization processes to further improve the efficiency of business operations. Thus, together with Finnair, we planned several new MVPs that would build on this model. There were two motivations for moving forward with multiple applications: on the one hand, we wanted to quickly create more value where it was possible; and on the other hand, we wanted to lay out clarified directions for Finnair’s operational analytics by exploring the application space and validating our ideas.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The suite of applications developed was adopted to daily operational use smoothly, which was supported by training sessions for the end users as well as an on-going gathering of feedback. Sharing information about the tools for the organization and quickly incorporating improvement suggestions to the models enabled the inclusion of key people in the development efforts and fast extraction of value from the tools. This resulted in increased customer satisfaction as well as significant cost savings at Finnair. In conclusion, the five-step data science process described in this blog post has proven to be a valuable guideline as Finnair’s operational analytics area continues to evolve.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"authors":[],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"providing-business-value-data-science-finnair-five-step-model","publishDate":"2020-11-23","showRelatedArticles":true}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"tkwqWR8RXJGlzjfyYWaDo4","type":"Entry","createdAt":"2020-12-08T14:29:54.239Z","updatedAt":"2020-12-08T14:29:54.239Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"​Nitor invests in Artificial Intelligence","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"yIT7UV7fRq2t77qvxdrTY1","type":"Asset","createdAt":"2020-11-27T16:54:46.801Z","updatedAt":"2020-11-27T16:54:46.801Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"nitor-insight-2018-05.jpg","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/yIT7UV7fRq2t77qvxdrTY1/419f76a258461d9f1de097224763773e/nitor-insight-2018-05.jpg","details":{"size":361936,"image":{"width":2280,"height":1282}},"fileName":"nitor-insight-2018-05.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Nitor has founded a new business focused on Artificial Intelligence. Nitor Insight will offer research and consulting services on data science, AI, and machine learning. Nitor Insight started its operations in early 2018 and will be developed into a significant part of Nitor’s services and business.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[{"type":"italic"}],"value":"Nitor Insight’s launch has been prepared by Nitor’s Data Scientists ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Sami Airaksinen","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":" , ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Tuomas Ritola","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":", and ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Juha Syrjälä","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":" as well as Principal Consultant ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"},{"type":"italic"}],"value":"Otto Manninen","nodeType":"text"},{"data":{},"marks":[{"type":"italic"}],"value":".","nodeType":"text"},{"data":{},"marks":[],"value":"\n","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“We are extremely excited about launching Nitor Insight. AI and machine learning are an essential part of sustainable digital development. By broadening our offering, we can produce even better services for our current customers as well as target entirely new business areas,” ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"Matti Vilmi","nodeType":"text"},{"data":{},"marks":[],"value":", Nitor’s CEO, explains.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"AI, machine learning, and data science have become increasingly popular within the past few years and continue to be hot topics. Advanced technologies, increasing amounts of data, and the development of platforms such as Amazon, Google, and Microsoft, make the solutions an even bigger part of everyday life.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“Our customers handle large amounts of data, and machine learning is a way to create value out of this data. We have utilized machine learning throughout the years in making prognoses and recommendations within, for example, the media, finance, retail, logistics, and transportation industries. Lately, we have especially worked with different kinds of customer-service chatbots and other speech-related services,” Otto Manninen says.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Even though there is plenty of hype around AI, practicality and creating real value is a priority at Nitor Insight. Machine learning enables automation of solutions that wouldn’t have been sensible with traditional methods. People can focus on solving more important problems, aided by machines.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“The benefits are obvious: machine learning is now mature enough that it can be used in business-critical systems. Consumers are also increasingly starting to demand intelligent functions from all the services they use. And these are now often based on machine learning,” Tuomas Ritola explains.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"In addition to automating routine actions as well as supplementing current digital services, AI and machine learning offer many interesting new possibilities. Ideas move quickly from the academic world into productization and commercialization.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“The development of speech recognition and natural language processing has created opportunities for new kinds of chat-based services. The AI platforms and personal assistants from the big service providers will slowly change people’s habits and expectations of how to transact with businesses,” Juha Syrjälä says.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“I would estimate that the next big discussion topic after deep learning will be Generative Adversarial (Neural) Networks or GANs. These neural networks enable AI to learn how to create realistic photographs and other visualizations. It will be also interesting to see when Neural Processing Unit or NPU-accelerated devices become common and alter the current computation ecosystems,” Sami Airaksinen adds.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"With Nitor Insight, AI, machine learning, and data science are now a significant part of Nitor’s services. They are a part of Nitor’s sustainable digital development that already includes pragmatic architecture, design, large-scale agility, and technology services.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"nitor-invests-artificial-intelligence","publishDate":"2018-06-13"}}]}}]}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"5aTtoeg1PZl3ba2GC3rYfB","type":"Entry","createdAt":"2020-09-30T15:25:02.568Z","updatedAt":"2020-12-17T20:36:36.597Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":5,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"page"}},"locale":"en-US"},"fields":{"id":"advisory","title":"Strategy","slug":"strategy","blocks":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2t9l76tVQTxjZ8nYtvuAjc","type":"Entry","createdAt":"2020-09-30T11:43:42.842Z","updatedAt":"2020-12-15T19:32:52.940Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":5,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"hero"}},"locale":"en-US"},"fields":{"name":"Strategy Hero","title":"Towards sustainable digital development","additionalTitle":"Strategy","paragraph":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Turn change into opportunity with an adaptive strategy. By anticipating changes in the business environment and your customer’s expectations, we can develop digital innovations and flexible structures to help you fulfil your vision. A good strategy can make you the leader of today. An excellent one nurtures a culture of experimentation and fosters the resilience needed to lead tomorrow. ","marks":[],"data":{}}],"data":{}}]},"image":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"40HBJj6hFGFjqnDWY4OtnX","type":"Asset","createdAt":"2020-09-29T12:09:20.170Z","updatedAt":"2020-11-10T11:50:58.390Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":4,"locale":"en-US"},"fields":{"title":"Background","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/40HBJj6hFGFjqnDWY4OtnX/409c8498be67d0861c46a7b10312ca79/Background-2.jpg","details":{"size":160706,"image":{"width":1920,"height":1104}},"fileName":"Background-2.jpg","contentType":"image/jpeg"}}},"animationType":"strategy"}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2qE4hDQE4YRpNh8YMw1trv","type":"Entry","createdAt":"2020-11-11T06:55:33.142Z","updatedAt":"2020-12-17T16:48:33.289Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":8,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"expertiseBlock"}},"locale":"en-US"},"fields":{"title":"Adaptive strategy","paragraph":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"Resilience will see your business through to the future. But what builds business resilience? To put it simply, quick reflexes and customer-focused innovation. We can help you forge the path to resilience with an adaptive digital strategy that anticipates changes in the business environment and your customer's needs. You'll learn to develop flexible structures, identify opportunities and lead the path to change. In essence, an adaptive strategy will help you turn change into opportunity.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"references":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"29peF12XRxEQrStuyLAI70","type":"Entry","createdAt":"2020-12-17T16:48:21.918Z","updatedAt":"2020-12-17T16:48:21.918Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"namedReference"}},"locale":"en-US"},"fields":{"name":"Read more about the benefits of an adaptive strategy","entry":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"XrWuvr85jjDvBmqm6vU9a3","type":"Entry","createdAt":"2020-12-09T10:54:37.142Z","updatedAt":"2020-12-17T16:47:06.932Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":3,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"Predictive measures, innovations and agility are saving organisations","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"EwAsi5DRYjhJ7eY1v0t6R3","type":"Asset","createdAt":"2020-11-27T16:56:58.359Z","updatedAt":"2020-11-27T16:56:58.359Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"Placeholder.png","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/EwAsi5DRYjhJ7eY1v0t6R3/f761bc48bcb2b26b8f717c0afb62021e/Placeholder.png","details":{"size":82196,"image":{"width":1140,"height":641}},"fileName":"Placeholder.png","contentType":"image/png"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The past spring has revealed how agile companies can — or can’t — be. How can you develop your business, when an increasing amount of uncontrollable change factors are affecting customer behaviour, expectations and even the chances of accessing the services offered?","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"We are still weary after the onslaught of COVID-19 last spring, but positive expectations help us all move on. However, few organisations can afford to rely on glorious expectations alone. The past months have revealed how vulnerable, but also how agile companies are in terms of change. But most of all, the experience has taught us what it is we need to do right now in order to be successful in the future.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"I have discussed with several business executives over the past weeks and followed general financial news, and I think it’s safe to say that many business fundamentals are in disarray. It is not just the service channels and production lines that have undergone transformation: customer choices, business models and their implementation have changed as well. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Business models, decision-making, talent flexibility and operational agility have been put to the test in many business sectors. As many restaurants had to close their doors, employees were transferred to grocery stores to support their online shopping and delivery service capacity. ABC service stations started delivering pre-ordered meals safely and easily straight to cars. The design and production processes of display furnishers were tuned to the production of plexiglass shields for the checkout counters of take away restaurants and retail stores. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The ability to innovate, experiment and quickly implement has saved many companies from dire straits.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Look ahead","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"In the midst of the pandemic we go from one phase to another and try to predict the next ones. We identify and assess different measures that we can adopt in order to prepare for the next phases and to overcome them. After the initial shock, a growing number of companies are getting prepared for the post-pandemic world. It will come, and although it might be difficult to see during the acute crisis, the seeds for future growth are sown in the midst of the present crisis. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Consequently, I would like to encourage leaders to cast their eyes over the crisis and to engage their organisations to do so as well. While managers are doing their best to minimise the current financial and personal losses, it is essential that they also take time for devising visions and plans for the future.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Tolerate uncertainty","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"While the past months have taught us a lesson or two, we still cannot foretell the future. But we are certainly better equipped to prepare for it. A growing number of companies are developing and implementing tools for understanding and predicting future customer needs and factors that drive change in their operating environment.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Scenarios are modeled and evaluated on the basis of data, customer insight, business risks and opportunities. By selecting the appropriate scenario, companies can direct their future business while finding their balance in the current storm. But you should not waste too much time on selecting the right scenario. If you wait for the fog to subside before you make your pick, you have already lost.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Develop talents","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"A successful leader dares to break normative leadership structures. The ability to focus needs to be coupled with the ability to be sensitive to the buzz surrounding our operating environment; it is important to recognise and evaluate the flickering signals. Our tendency to rely on what we know and trust has been put to question. We no longer draw strength from past success and the lessons learnt. We are refiguring our business position in uncertainty — and striving towards a goal that is likely to shift.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"An exploring, pivoting culture will be an integral part of the new normal — not just in service design and production, but also in leadership and strategy work. Fortunately, it is possible to develop both managerial and organisational resilience. It is also possible to utilise","nodeType":"text"},{"data":{"target":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"QEskfaQufwc7FyhMdGZMP","type":"Entry","createdAt":"2020-12-08T09:46:54.481Z","updatedAt":"2020-12-17T13:30:36.198Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":12,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["fi","en"],"title":"CIO - move from collecting data to utilising it","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"5NAZ0U8qKv6cSVfigvEp35","type":"Asset","createdAt":"2020-11-27T17:02:28.455Z","updatedAt":"2020-12-08T09:44:03.496Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":2,"locale":"en-US"},"fields":{"title":"Samuli Visuri sitting ","description":"Samuli Visuri, People","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/5NAZ0U8qKv6cSVfigvEp35/cdcaa6e505a839fe24d5a870c6a2f389/Samuli_Visuri_portrait.jpg","details":{"size":320976,"image":{"width":1792,"height":1008}},"fileName":"Samuli_Visuri_portrait.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The revolution in using data is based on the increase in computing power and open source development. The algorithms needed for data processing and predictive analytics are already freely available. Read the advice of Samuli Visuri, Nitor's analytics business director, on how to make the most of analytics.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With regard to artificial intelligence and machine learning, there is a common illusion a single technology, software, or algorithm would help a company boost its business. As if there would be some sort of a black box, which digests all the data and spits out an operational guideline.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Sorry to disappoint, there is no black box. There has been no new mystical breakthrough in algorithm development, and no software can standardise data processing and predicting the future. 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","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. 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How can you develop your business, when an increasing amount of uncontrollable change factors are affecting customer behaviour, expectations and even the chances of accessing the services offered?","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"We are still weary after the onslaught of COVID-19 last spring, but positive expectations help us all move on. However, few organisations can afford to rely on glorious expectations alone. The past months have revealed how vulnerable, but also how agile companies are in terms of change. But most of all, the experience has taught us what it is we need to do right now in order to be successful in the future.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"I have discussed with several business executives over the past weeks and followed general financial news, and I think it’s safe to say that many business fundamentals are in disarray. It is not just the service channels and production lines that have undergone transformation: customer choices, business models and their implementation have changed as well. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Business models, decision-making, talent flexibility and operational agility have been put to the test in many business sectors. As many restaurants had to close their doors, employees were transferred to grocery stores to support their online shopping and delivery service capacity. ABC service stations started delivering pre-ordered meals safely and easily straight to cars. The design and production processes of display furnishers were tuned to the production of plexiglass shields for the checkout counters of take away restaurants and retail stores. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The ability to innovate, experiment and quickly implement has saved many companies from dire straits.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Look ahead","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"In the midst of the pandemic we go from one phase to another and try to predict the next ones. We identify and assess different measures that we can adopt in order to prepare for the next phases and to overcome them. After the initial shock, a growing number of companies are getting prepared for the post-pandemic world. It will come, and although it might be difficult to see during the acute crisis, the seeds for future growth are sown in the midst of the present crisis. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Consequently, I would like to encourage leaders to cast their eyes over the crisis and to engage their organisations to do so as well. While managers are doing their best to minimise the current financial and personal losses, it is essential that they also take time for devising visions and plans for the future.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Tolerate uncertainty","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"While the past months have taught us a lesson or two, we still cannot foretell the future. But we are certainly better equipped to prepare for it. A growing number of companies are developing and implementing tools for understanding and predicting future customer needs and factors that drive change in their operating environment.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Scenarios are modeled and evaluated on the basis of data, customer insight, business risks and opportunities. By selecting the appropriate scenario, companies can direct their future business while finding their balance in the current storm. But you should not waste too much time on selecting the right scenario. If you wait for the fog to subside before you make your pick, you have already lost.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Develop talents","nodeType":"text"}],"nodeType":"heading-2"},{"data":{},"content":[{"data":{},"marks":[],"value":"A successful leader dares to break normative leadership structures. The ability to focus needs to be coupled with the ability to be sensitive to the buzz surrounding our operating environment; it is important to recognise and evaluate the flickering signals. Our tendency to rely on what we know and trust has been put to question. We no longer draw strength from past success and the lessons learnt. 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","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. 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","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-3","content":[{"nodeType":"text","value":"Conclusions ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"When it comes to buying analytics, the choice is clear. You should choose a competent data scientist who understands the customer. At Nitor, we focus on resolving the customer’s problems in a business-oriented way, with tools best suited to the task at hand instead of relying on technology-driven solutions. If you would like to know more, we’d be happy to come and start charting potential development targets and creating business value for you.  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Surviving the quick turns of last spring has given us the strength, ability and tools to design and decide how to be relevant in the future. Currently we are dismantling silos and breaking locked-in value chains and ecosystems. ","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Realignment and new partnerships will invigorate many. Flexible processes and the ability to react and decide quickly enable you to produce added value for customers, whose behaviour is still guided by a growing number of change factors, some of which are hard to predict. It is time to move on and to build the new normal proactively.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Nitor helps in the management of digital transformation, in the development of agile strategies and growth, in planning sustainable architecture solutions and in renewing organisational resilience. 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","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 1: Be impatient ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In order to reap benefits from analytics, you do not need large projects spanning many years. Although it’s tempting to start by planning a big project with all the data neatly available to the entire organisation in a structured form, it is more fruitful to take a different approach. How can we quickly benefit from our data, and how can we use it to accelerate our business? Modern technology is modular and, when properly designed, also flexible.  It is easier to plan an organization-wide project, once there is an understanding of the benefits to be achieved. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 2: Tolerate disorganised data ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"One often hears we cannot do this until the data is in better order. We need precise specifications. Our data contains errors. These must be fixed first. It is, of course, useful to get the company’s data sets organised. However, this should not slow down development, as an experienced professional will be able to correct erroneous data in no time. A proficient expert can write algorithms, which for example reconcile different data entry practices and estimate missing data based on some other characteristics. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 3: Simplify and succeed ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" Business is fundamentally about predicting the future and taking action accordingly. The point of artificial intelligence and machine learning is not in the use of brilliant algorithms, but rather in the ability to simplify key business questions into mathematical models. The winner is the one who can formulate the right abstraction for the key question – in a way that it can be answered.  ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Rule no. 4: Acquire expertise that will benefit you immediately ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Almost without exception, the best option is to quickly implement a Proof-of-Concept or a smaller solution, which yields immediately measurable business benefits. This is, of course, easier said than done! However, an experienced professional is, together with business experts, able to find a suitable starting point. A consultant is not the best to advise on how to do business. But she knows the possibilities of using data and is able to harness large amounts of data in such a way it is useful in decision making and running business. ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":" ","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-3","content":[{"nodeType":"text","value":"Conclusions ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"When it comes to buying analytics, the choice is clear. You should choose a competent data scientist who understands the customer. At Nitor, we focus on resolving the customer’s problems in a business-oriented way, with tools best suited to the task at hand instead of relying on technology-driven solutions. If you would like to know more, we’d be happy to come and start charting potential development targets and creating business value for you.  ","marks":[],"data":{}}],"data":{}}]},"authors":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6LCYDgXGe75zxb2BEb9Mp3","type":"Entry","createdAt":"2020-12-08T09:46:11.310Z","updatedAt":"2020-12-17T21:54:04.673Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":4,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"person"}},"locale":"en-US"},"fields":{"name":"Samuli Visuri","jobTitle":"Managing director, Analytics services","email":"samuli.visuri@nitor.com","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2ezVsrZfIaOggjXWq56qMy","type":"Asset","createdAt":"2020-11-27T17:22:24.531Z","updatedAt":"2020-11-27T17:22:24.531Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"Samuli Visuri profile blue cropped","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/2ezVsrZfIaOggjXWq56qMy/f6b0a87fc8bfc8e22b192c793adc1a04/Samuli_Visuri_profile_blue_cropped.jpg","details":{"size":1787811,"image":{"width":2000,"height":2000}},"fileName":"Samuli_Visuri_profile_blue_cropped.jpg","contentType":"image/jpeg"}}}}}],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"2dQLj8osKori0q3pY8NTmn","type":"Entry","createdAt":"2020-10-28T20:52:06.201Z","updatedAt":"2020-10-28T20:52:06.202Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Analytics","slug":"analytics"}}],"slug":"cio-move-from-collecting-data-to-utilising-it","publishDate":"2020-05-27","showRelatedArticles":true}},{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"uVrVvtieWJKXzDXiRrfx8","type":"Entry","createdAt":"2020-12-08T08:36:19.128Z","updatedAt":"2020-12-08T08:36:19.128Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"article"}},"locale":"en-US"},"fields":{"lang":["en"],"title":"DNA, a GPTW Winner, Set the Bar for Agile Sky High","mainImage":{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6hcWQEEC2lhQpVJ9sd6Ha","type":"Asset","createdAt":"2020-12-08T08:22:02.301Z","updatedAt":"2020-12-08T08:22:02.301Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"locale":"en-US"},"fields":{"title":"DNA headquarter","description":"Outside teleoperator DNA's headquarter, company flags","file":{"url":"//images.ctfassets.net/w58q9wcmirx2/6hcWQEEC2lhQpVJ9sd6Ha/12266d1101d728f0dd8c1d071b7a00ad/Blogi_dna_ketteryydentila_analyysi.jpg","details":{"size":923137,"image":{"width":2400,"height":1350}},"fileName":"Blogi_dna_ketteryydentila_analyysi.jpg","contentType":"image/jpeg"}}},"lead":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The Finnish mobile network operator DNA has done a fantastic job over the years in making its IT functions more agile. The agile development methodologies fit in well with DNA’s bold and experimentative culture.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"DNA had already discovered the benefits of agile methodologies through faster and smoother development, but they also noticed that it’s nowhere near enough to stop the agile transformation at the product development level. The best possible results are achieved when agility is adopted by the entire organization – including top management.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"To find out how to fully utilize the benefits of agile, DNA’s HR Director ","marks":[],"data":{}},{"nodeType":"text","value":"Marko Rissanen","marks":[{"type":"bold"}],"data":{}},{"nodeType":"text","value":" asked us to check the status of agility in their organization with the help of our Current state analysis.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"“When the development needs were brought into the open, it helped DNA to take the next step towards bringing agile forward, with a strong mandate from the management,” Rissanen says.","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"What does the current state analysis entail? ","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In the Current state analysis, we sink our teeth into topics such as structures around decision-making, the flow of information, optimization, teamwork, learning, and customer orientation. The analysis aims to map out the broad agility status as well as the future possibilities of an organization to support enterprise agility.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Finding out the status of agility in product or service development isn’t the only goal. We also aim at including different operations throughout the organization as widely as possible.","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Who can benefit from the current\nstate analysis?","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"We have executed the analysis, for example, in a big organization in the catering industry. What we noticed was that the traditional project-based way to operate is way too stiff for fast-paced changes such as food trends. This demonstrated nicely that the ingrained concepts in the restaurant business can also be challenged with the help of agile analysis.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"We can confidently say that the Current state analysis suits organizations which already utilize agile methodologies as well as those who are still considering to introduce agile transformation into their everyday way of working.","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"The results of the analysis are refined together","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"The results of the analysis are processed together with the staff and management in order to get everyone on board right from the start. When the results are formulated as concrete actions and next steps, it also helps to create a common vision of agile. This plays a big part in launching the change at an organizational level right away.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"With the State of Agile analysis, the organization is able to receive an unbiased outsider view on its status. Our strong background in research and vast experience in agile transformation allows us to see under the surface – and dive into the root causes that aren’t always visible in the day-to-day operations. This is how we make sure that the development targets are correctly selected instead of risking focusing on more shallow goals, that would need more profound attention in the long run.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"The analysis is always performed by two coaches to ensure the best possible quality. Two sets of eyes can see better, and working in pairs offers more depth to the analysis – working together is also a basic element of agile methodologies, so we are setting an example too.","marks":[],"data":{}}],"data":{}},{"nodeType":"heading-2","content":[{"nodeType":"text","value":"Hear more about the current state analysis","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"If you want to know more about how to have an insight about your current state of agility, get in touch with our experts!","marks":[],"data":{}}],"data":{}}]},"authors":[],"tag":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"6ibUTJBrlcSrTM4JftTnPF","type":"Entry","createdAt":"2020-09-29T20:55:45.997Z","updatedAt":"2020-10-28T20:50:57.948Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":3,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"tag"}},"locale":"en-US"},"fields":{"name":"Agile","slug":"agile"}}],"slug":"dna-gptw-winner-set-bar-agile-sky-high","publishDate":"2020-02-11","showRelatedArticles":true}}]}}]}}]}},"entryHrefsByLang":{"fi":"/fi/palvelut","en":"/en/services"},"preview":false},"__N_SSG":true}