Data science can enable wholly new and innovative capabilities that can completely differentiate a company. But those innovative capabilities aren’t so much designed or envisioned as they are discovered and revealed through curiosity-driven tinkering by the data scientists. So, before you jump on the data science bandwagon, think less about how data science will support and execute your plans and think more about how to create an environment to enable your data scientists to come up with things you never dreamed of.

Data scientists are a curious bunch (especially the good ones). They have clear goals and are focused on and accountable for achieving certain performance metrics. But they are also easily distracted, in a good way. In the course of doing their work they stumble on various patterns, phenomenon, and anomalies that are unearthed during their data sleuthing. This goads the data scientist’s curiosity: “If we modeled clothing fit as a distance measure, could we improve client feedback?” “Can successful features from existing styles be re-combined to create better ones?” To answer these questions, the data scientist turns to the historical data and starts tinkering. They don’t ask permission. In some cases, explanations can be found quickly, in only a few hours or so. Other times it takes longer because each answer evokes new questions and hypotheses, leading to more tinkering.

Not only does data science enable rapid exploration, it’s relatively easier to measure the value of that exploration, compared to other areas of the business. Statistical measures like AUC, RMSE, and R-squared quantify the amount of predictive power the data scientist’s exploration is adding. The combination of these measures and a knowledge of the business context allows the data scientist to assess the viability and potential impact of a solution that uses their new insights. If there is no “there” there, they stop. But when there is compelling evidence and big potential, the data scientist moves on to more rigorous methods like randomized controlled trials or A/B Testing. They want to see how their new algorithm performs in real life, so they expose it to a small sample of clients in an experiment. If the experiment yields a big enough gain, they’ll roll it out to all clients.

The key here is that no one asked the data scientist to come up with these innovations. They saw an unexplained phenomenon, had a hunch, and started tinkering. They didn’t have to ask permission to explore because it’s relatively cheap to allow them to do so. Had they asked permission, managers and stakeholders probably would have said ‘No’.