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The system allows users in different organizations to apply for and authorize access to different materials. The solution is implemented in an agile way in Clojure and published as open source. Nitor has been CSC's framework contract supplier since 2017 and won the tender for the further development of the REMS system also for the years 2020–2022.","nodeType":"text"}],"nodeType":"paragraph"}],"nodeType":"document"},"text":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"The IT Center for Science, or CSC, is a Finnish center of expertise in information technology owned by the Finnish state and higher education institutions. CSC provides internationally high-quality ICT expert services and is known, for example, for its computing services and ","nodeType":"text"},{"data":{"uri":"https://yle.fi/uutiset/3-10818421"},"content":[{"data":{},"marks":[],"value":"supercomputers","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":". Together with CSC, Nitor is renewing the center's REMS system utilized by, for example, Findata and THL Biobank.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"What is the REMS system used by researchers?","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"REMS or Resource Entitlement Management System has been implemented as part of the international ","nodeType":"text"},{"data":{"uri":"http://www.elixir-finland.org/elixir/"},"content":[{"data":{},"marks":[],"value":"ELIXIR","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" project, which brings together data and tools from the life and health sciences across Europe. The aim of the project is to create a research infrastructure that will make it easier for researchers to find and analyze data and share their knowledge, implementing best practices in the field. REMS provides researchers from different organizations access to data and materials, and the application process can be handled entirely within the service.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Built in the Clojure programming language, ","nodeType":"text"},{"data":{"uri":"https://github.com/CSCfi/rems/"},"content":[{"data":{},"marks":[],"value":"REMS is an open source product","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" with CSC and Nitor as the main implementers. From the beginning, two of Nitor’s experienced software architects, ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"Markku Rontu","nodeType":"text"},{"data":{},"marks":[],"value":" and ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"Joel Kaasinen","nodeType":"text"},{"data":{},"marks":[],"value":", have been involved in the project. Clojure and open source mean a lot for both of them.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"italic"}],"value":"“I think it’s important for public systems to be open source. It's also great to get to show an example of an extensive system made with Clojure as well as to help others by developing open source components. Unlike many other modern languages, Clojure was originally built for heavy production use and is well suited for programming a system like this,” says Markku.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"REMS includes an interface through which the license application circulates from the applicant to the owner of the material or their representative for approval. In addition, it provides an extensive interface that shares information on applications and licenses granted and acts as an integration point with other licensing or processing systems. REMS can be flexibly applied to meet the needs of the organization using it. The system is utilized, for example, by the data permit authority Findata and the biobank of the Finnish Institute for Health and Welfare (THL).","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"The solution implemented with an agile team adapts to the needs of different users","nodeType":"text"}],"nodeType":"heading-3"},{"data":{},"content":[{"data":{},"marks":[],"value":"Passionate Clojure experts from CSC and Nitor worked together to build a system that adapts to the needs of different user organizations. The suitability of the new system was validated with a proof-of-concept implementation, on top of which new features have been built piece by piece according to the needs of user organizations. REMS was first adopted by the ","nodeType":"text"},{"data":{"uri":"https://www.kielipankki.fi/"},"content":[{"data":{},"marks":[],"value":"Language Bank of Finland","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" where the system has been in production since 2018.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"italic"}],"value":"“New user organizations always bring new needs to the system, which can be a big challenge. Our project has gone through a few extensive rewrites, such as a significant simplification of the processing model. However, pure functional code, good coverage of automatic tests and the expressive power of the Clojure programming language have kept the code mass under control,” says Markku.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Nitor has been the supplier of the REMS system since 2017 and won the tender for the further development of the system also for the years 2020–2022. The remote work recommendation, which began in spring 2020, did not slow down the agile team.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[{"type":"italic"}],"value":"“The success of a project is almost always the result of effective teamwork. For example, we've been working remotely with CSC developers since the beginning. Remote work is well suited for an open source project and our experience has, of course, facilitated the overall transition to the new normal. In addition, good product management is needed for the overall management and especially to adapt to new needs, and for this thanks go to the CSC product owners. 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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":[{"sys":{"space":{"sys":{"type":"Link","linkType":"Space","id":"w58q9wcmirx2"}},"id":"4HggeauRL52hsDHWlMCn2O","type":"Entry","createdAt":"2020-11-27T17:24:31.401Z","updatedAt":"2020-11-27T17:24:31.401Z","environment":{"sys":{"id":"master","type":"Link","linkType":"Environment"}},"revision":1,"contentType":{"sys":{"type":"Link","linkType":"ContentType","id":"person"}},"locale":"en-US"},"fields":{"name":"Eero Lihavainen","jobTitle":"Data Scientist","description":{"data":{},"content":[{"data":{},"content":[{"data":{},"marks":[],"value":"","nodeType":"text"},{"data":{"uri":"https://www.linkedin.com/in/eero-lihavainen-b1706a121/"},"content":[{"data":{},"marks":[],"value":"Eero Lihavainen","nodeType":"text"}],"nodeType":"hyperlink"},{"data":{},"marks":[],"value":" is a full stack Data Scientist who strives to combine strong theoretical foundations with software engineering best practices in his work. 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Nitor too, has experts familiar with Sisu from their time as students.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“It’s exciting to be involved in developing a system used by thousands of people every day,” says Nitor’s Software Architect ","nodeType":"text"},{"data":{},"marks":[{"type":"bold"}],"value":"Erkki Pulliainen","nodeType":"text"},{"data":{},"marks":[],"value":".","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"Lifecycle thinking is a core principle in developing Sisu, achieved using the modern DevOps model. Continuous integration and delivery together with advanced runtime monitoring have been key focus areas from the start. Sisu is offered through a SaaS model, providing vast integration interfaces to enable data migrations and real-time data utilization. During the past couple of years, Nitor has also worked with Funidata and its customer universities to employ user-centric design principles with an increasing focus on usability.","nodeType":"text"}],"nodeType":"paragraph"},{"data":{},"content":[{"data":{},"marks":[],"value":"“Usability testing, iterative design, and joint development have played a key role in helping both the development team and the client to better understand how to make Sisu as good as possible. 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