Data Science - a Journey to Adoption

Trust

Decisions require trust. I often see colleagues sharing the same frustration - “business people must trust the data” and they don’t. I agree on the point that decisions with data are better. However, I also understand the problem that business people have with our data and models. I doubt that there is one data scientist that can earnestly say that his data is 100 percent clean and accurate. Let’s admit it – such thing doesn’t exist! And it doesn’t make it easier when you put on top a “magical” machine learning algorithm that is a black box even for data scientists themselves.

Very often data scientists ship their models or dashboards “as it is” and leave it for users to explore. Any business person will browse the model looking for something surprising. And after they find it, the first question is – “is the data correct?” Even if only 10 percent of the times it is a data issue and other 90 percent - a serious business insight, still the first impression will be “data is unreliable.” If you built the model, analyze the data yourself, as a business person would. Find the surprising facts, check them to exclude data issues and only then share the model and the key insights from it.

Model interpretation also contributes to building trust. Make sure that you develop tools that help to interpret your model’s decisions. For example, if you have built a recommendation engine, create an internal tool that explains what factors each product up in the list. It will also help in debugging.

Deployment

Very often data scientists build a predictive model in Python or R and get remarkable accuracy, but this code can’t run in production at the real scale. In the end, everything gets rewritten in Java or Scala, and half of the model gets scraped due to complexity.

Different problems come when you are optimizing some manual process. For example, marketing campaign optimization can still be done manually in your organization. You might think that a proper dashboard with bidding advice will be enough for deployment. In practice, most of your model improvements will come from using more features for targeting and faster updates of your bids. This will make manual optimization impossible and you will end up automating the whole process. It is very likely that developing a proper automation tool that manages your AdWords or Facebook ads through API will require more time and effort than building the model.

The importance of execution is becoming more and more apparent, specifically when both data cleaning and machine learning are getting excellent automated tools. What does it mean for the big picture? Many specialists are predicting that data science will be automated and the profession will go away. I think it is only partially true. There is no place for a silo Data Science department in future organizations. However, data scientists will not go extinct. They will march over the fence into domain specific roles. There is a huge need for marketing managers who understand data; product managers who understand data; developers who understand data; HR managers who understand data; and everybody else who understands data. Data Science department will disappear by taking over the whole organization.