With a background in Civil Engineering and Econometrics, Paul Koot was team captain of Notilyze’s winning Hackathon team in 2020 and 2022. In his role as data scientist he has gained extensive knowledge of machine learning models in manufacturing projects and text analytics. In this article we ask him about analytics leadership and what this means from his experience.   1. How would you define analytics leadership? Recently I encountered the following quote about leadership from Dwight D. Eisenhower: "The essence of leadership is to get others to do something because they think you want it done and because they know it is worthwhile doing." I would say the following for analytics leadership: The essence of analytics leadership is to get data to do something because the customer thinks data can do this and because you know it is worthwhile doing. What I mean by this is the following.   First  A first condition for a data analytics project to succeed is that a customer should be convinced that data can help with this problem. Most of the time, this is (at least partly) the case, as the customer would not have approached you (the analytics leader) in the first place. However, it is not apparent that all stakeholders in your project are aligned on what value data can bring to the table in solving business problems. As an analytics leader, a very important part of the process is to make sure all stakeholders acknowledge the business problem and acknowledge data can help solve this problem.   Second Then, the second part is critical. You, as an analytics leader, should realize that it is worthwhile spending effort and time in using data to solve this problem. And more importantly, you should find a way data can tackle this problem efficiently. You, as an expert, should clearly separate the best solution from the solution the customer thinks they need while keeping track of the customer’s business knowledge.   This business knowledge is essential for a solution that will not stop after finishing the Proof of Concept phase and really adds value after implementation. In tackling the problem efficiently, it is important to realize that data analytics must always be the means to an end, not the goal itself. Realizing this helps to separate the customer’s problem from the technology hypes they think they should also start using. This means you can sometimes solve the problem more fundamentally than the customer initially thought (you can read more on how to do that in this article). However, reformulating the business problem might lead to another iteration of aligning all stakeholders to acknowledge this. In my opinion, an analytics leader is capable of doing so.   2. What different roles do you see in a team of data scientists? I see three essential roles at the core of any project. The first is the business translator, who has the skills to understand the business problem and explain to the customer why that is the business problem. The latter is important; if a customer does not agree with your problem definition, they will never agree with your solution. Also, the business translator splits the problem up into different more minor problems. Each problem can be tackled separately, leading to actionable insight.   The second role is the data engineer; this person assembles, transforms and cleans the data. The ETL process (Extract, Load, Transform) is often not visible in the final product, but it is at the core of almost any solution. Data engineers deliver the Analytical Base Tables (ABTs).   The third role is the data scientist, who translates the data into actionable insights. Often multiple iterations exist between a data engineer and data scientist, as decisions on how to show results on a dashboard affect the most efficient way to store things in the ABTs. An analytics leader could fulfill any of these roles, but naturally, the role of business translator comes with most of the customer interactions and therefore would be the most obvious role for an analytics leader. But note that one person can have multiple of these three roles, depending on the project size.   On the other hand, there could be more roles than just these three. To meet the customer’s wishes, there could be software developers who deal with integrating the solution in the existing architecture or creating a new tailor-made application. UI-designers ensure the interface is user-friendly and point out essential insights. And cloud engineers ensure this all happens within the cloud in a scalable and secure way.   3. What is your experience how a Data Scientist should work best with the stakeholders? At the start of a project, the full focus should be on the business problem; a data scientist should try to understand what is at stake (this often is more than ‘the problem’), who has to give a go/no-go, who experiences the problem, who needs a solution (not always the same person!), who will work with your solution. Every hour of this orientation will save multiple hours in the following steps. After this first assessment, the data scientist must reflect critically; do you agree with the initial problem statement, or should we focus on other things first? If the latter is the case, the next step is to convince the stakeholders the focus should be on something else. Point out what you see as the problem. Only when everyone agrees on this, you can move forward with proposing your solution.   4. What are the required key skills a data scientist should have to be successful in the job? That depends on the setting. A solo data scientist solving a problem needs different skills than a data scientist who is part of a dedicated scrum team with all kinds of roles. But skills that are always useful: 1) be critical of yourselves on why you are doing things, as you might be the first one asking that question; 2) keep in mind the operation phase when developing a first version; 3) math/statistics OR domain knowledge: you need to be able to add a critical side note either from the business case perspective or the data/algorithmic perspective. Both of these knowledge fields need to be in a project, so if you don’t own them, make sure you find someone else as your counterpart. ... View more

  As a data science consultant, I have experienced that a project often starts with a lot of stakeholders who have an abundance of cool ideas and a lot of enthusiasm to start using data. However, this enthusiasm can quickly vanish after realizing that the first, second and maybe third Proof of Value (PoV) all have one factor in common; a customer (or his/her manager) concluding with one of the following remarks: ‘but what can we do with this insight?’ or ‘but now my problem is still not solved, because…’ or even worse: ‘we cannot do anything with your analysis’.   Although the idea is cool, the solution does not offer any new value to the company. In fact, Gartner (2019) predicted 80% of analytics insights would not deliver business outcomes through 2022.   A good thing is that a data scientist can play a big role in reducing the risk of such an event happening. In this article, I would like to focus on a great tool that can avoid a disappointing PoV: First-principles thinking. I will do this by elaborating on two use cases that I have been working on: a life-science manufacturer that wants to improve its sustainability and an organization that wants a better internal search system to find their documents. Before I do so, I will quickly go over the basics of First Principles. First Principles thinking The basic idea of First Principles is to distinguish the most fundamental building blocks of a problem, by answering the question: what are the basic elements of the complex problem that you encounter? By identifying and addressing these basic elements, it is easier to find creative ideas to solve the complex problem as a whole. As systems become more complex and interrelated, recognizing these building blocks of such a problem will not get easier. However, this same trend underlines the importance of using First-principles thinking. One of the strategies to detect these basic elements, is asking ‘Why’ five times. For a more elaborate discussion on this First Principles framework, see https://fs.blog/first-principles/ .   Sensor data in Manufacturing  I will now describe the first use case, which was a life-sciences manufacturer who wanted to do ‘something with his data to develop a more sustainable business process’. His data consisted of multiple sensors in a distillation process, manual quality samples and a number of gas meters. Two paths that were discussed initially were: Create a simulation of the distillation process to assess any hiccups in the production process; where is energy lost? Build a Machine Learning (ML) model to explain/forecast the gas usage of the process.   Both paths sounded very reasonable. However, some important missing sensors caused the first path not to be viable and data for the other sensors was too scarce to build a complicated ML model with any significant result. So we asked ourselves; ‘why are we doing this exercise?’ Basically the answer was to save energy. Then we asked ourselves ‘why we actually want to save energy?’ Well, to become more sustainable and to save costs, as the gas prices have risen with a factor 10 over the last year. ‘Okay, but why do we think saving energy is possible in this process?’ Actually this was an interesting question, as we discovered there had not been done any previous research regarding the energy usage; so this would be the first time to connect energy usage to the business process. So we started with the very basics: we looked at the products created in this line (in total 7 products) and excluded all of the sensor data that was not related to Product B, which was produced almost 70% of the time. We recalculated the energy usage to an energy usage per hour and we joined this information to the manual quality measures which includes the yield of the process. We then divided the yield by the energy usage and we saw something that stood out: at some point in time there was a period with a decrease in energy usage of 18%. However, this came with a decrease in yield of only 5%. This means that almost 20% of the energy costs could be attributed to only 5% of the yield. This might have been profitable when gas prices were 10x lower, but this definitely appeared to be a first insight that management started to discuss. Note also that we started with two paths that were not viable because of the fact that we missed some important sensor data and we ended up with a very valuable, actionable insight by not using any sensor data at all. Actually, for the other part of the data we also looked at only one of the seven products, reducing the amount of data used by another 30%.   Text analytics to create an internal search engine A second customer wanted an improvement of their internal search engine. This search engine was used to check historical communication with customers. The organization had a large corpus of documents and to answer questions of customers these answers needed to be in agreement with earlier replies. In the current interface the documents were returned only if a 100% match could be found between the search string and text in the document; no NLP was used and e.g. typos or verb conjugations were not detected and dealt with. Another part of their search engine was a classification of the documents into different categories, which could be utilized as an initial filter when searching. Therefore, part of the PoV was to see how this classification (labelling) could be done using ML. However, during the process we got stuck at an accuracy of around 60%. To get some better idea on possible ways to improve this performance, I brought up the question at the customer when and why they started with this labelling. After some follow-up questions it became clear that labelling was particularly done to overcome the problem that searches were done in two languages. As the perfect matching did not work in that case, labels had been invented (1a, 2a, 2b) and translated to both languages. In this way, there was some way to filter the database on a subset of data based on the labels, which were language-agnostic. So it became clear that with introducing NLP and some translating behind the search string, we could get rid of the whole labelling. This labelling purely was a means to an end, and should not be at all the goal of our PoV. Conclusion I think both of these cases show an important caveat of most data scientists. As problem solvers, we as data scientists often start too soon with answering the ‘how’; how can we solve this problem? What ML model should we use? What technical solution is the best? Which database should we use? How do we connect to the data sources? How is the interface going to look? Instead, we should take more time answering the ‘why’; why does a customer wants to answer this question? Why would it help his/her business? Why can’t this be solved without a complicated ML model? Why do we need data to solve this issue? Asking the ‘why’ questions will help us getting closer to the basic elements of a business problem and therefore helps in avoiding creating ‘wrong solutions’; i.e. solutions that do not answer the core problem and therefore do not deliver any value. ... View more

Team Name Team Notilyze Track IoT Use Case Providing analytical insights to a Food Manufacturer, leading to waste reduction Technology SAS ESP, SAS VA, Kafka Region EMEA Team lead Paul Koot Team members @fbtenkate @fleurstip @TheNoNinja @Noortje @QuintenVD @colinnugteren  @PaulKoot  Social media handles https://www.linkedin.com/company/notilyze  Is your team interested in participating in an interview? Y Optional: Expand on your technology expertise   Jury Video Pitch Video   ... View more