Choosing your business’ first successful data science use case
It is no secret that data science is on the rise. While the biggest hype is arguably behind us, data scientists are in high demand and data science seems to be here to stay. While the applications of data science are being picked up rapidly by firms in the tech industry, there seems to be a lag in other domains. That said, there is clear potential for value creation in these other areas as well. While (in my estimate) only a small portion of companies is today effectively using data science to drive decisions, most are aware of the developments and are at the very least exploring what could be in it for them.
0. Understand what data science isMake sure that you understand what data science is and what it could potentially mean for you and your business. A basic business-level understanding of data science is essential, as it will allow you to discuss potential projects in the context of their proposed value offering. There are quite a lot of books on the subject, one of the business-focused ones I can recommend is Data Science for Business. Another interesting page is the Awesome Data Science Ideas list, which has gathered quite a few business relevant examples on data science use cases.
1. Assemble the right project teamThe right team is key (isn’t it always?). The right team in this case is one that is multidisciplinary. It needs someone who has a thorough understanding of the business, someone with decision power who will make the difficult decisions when they come up, someone who is able to make the project visible within the company, someone to follow-up on the project status, someone with good knowledge on the company’s data resources and last but not least a strong data scientist. While not all roles have to be distributed over separate people (starting small can be a good thing), all roles are valuable.
2. Selecting the right caseFor a company to gain a good understanding of the value that data science can offer, a first use case should have a clear impact on value creation or at the very least show a potential to do so. In this phase, there are four questions which I generally use to estimate the potential success of a first use case.
- Do we expect the case to have an impact on value creation (and aid decision making)?
- Are the results translatable to other departments, allowing to stimulate engagement and induce excitement around the topic of data science within the company?
- Do we have the basic necessities to start working on this case (availability of domain experts within the company, the right data, etc.)?
- Is the objective of the case defined in such a way that the output of the case is actionable? In other words, will it be able to solve a business problem in the short to medium term? In the long run this correlates strongly with value creation.
3. Invest in the caseOnce a case is selected we need to make sure that the data scientist has the basics worked out. This means that he needs easy access to the data as well as input from business domain experts. It is important to support him in such a way that he is able to focus most of his time on relevant exploration and modelling. Allow him to work with the tools he prefers, as he will perform best when using these. As it is doubtful that you would have this person in-house when you are starting out on first project, there is a good chance that you will have to look outside of your company for data science consultants. This is a landscape that is somewhat difficult to navigate. There have been a huge amount of developments in the data science world in terms of both theory and tools. This has made the big players in the consultancy world to have had a hard time keeping up, and consultants working only with a specific proprietary tool often lag behind the state-of-the-art. Too often people with a very limited resume are presented as senior data scientist. Be critical about resumes, press for (more) detailed descriptions of the projects they’ve done and judge them based on their actual experience with data science.
4. Explore quickly and fail fastAs already mentioned, exploration is an essential part of any data science project. During this exploration phase, the data scientist tries to better understand if it is possible to find a solution to the problem at hand. If these initial explorations lead you to conclude that the resulting model will most likely have a below-par result, you should either adjust the objective based on the new knowledge that was gained or provide/invest in the correct resources which do enable you to attain the original objective. This makes for a specific environment where failure is possible and well-founded (intermediary) conclusions should be cheered on, even if those conclusions are found to be negative.
5. Regular status updates and adjustmentsThroughout the project, make sure that all parties are involved and up-to-date on the status of the project at all times. This helps increase the feeling of involvement, allows input on potential further explorations and enables shifts in focus if initial explorations give voice to concerns about feasibility.
6. Report on the findingsGive attention to success! Make a detailed report on the findings and share it in combination with a more digestible format such as a small article or poster. Share the story of what data science allowed to do more quickly or more accurately than before. Before sharing these results always ask yourself “What are the new insights you gained?”, “Will new actions be taken based on these new insights?” and “Do you see a path for data science in your company?”. Make sure to include the answers to these questions in your story telling.
ConclusionWhile the above steps are by no means a certain route to success, they are intended to give a few pointers based on the experience we’ve gathered over the years. Feel free to reach out if you have any comments or questions!
by Bart Smeets email@example.com dataroots is a data science consultancy company