Curiosity Driven Data Science

Any time you examine data analytically you exercise your curiosity.    And that curiosity is naturally driven by your own or business goals.  So it makes sense to attach the goals more systematically to the analysis, whether it is as simple as a regression,  or complex as a trained neural net.  Just make sure you do exploratory examination of your results.

Curiosity-Driven Data Science      By Eric Colson in HBR

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 empower your data scientists to come up with things you never dreamed of.

First, some context. I am the Chief Algorithms Officer at Stitch Fix, an online personalized styling service with 2.7 million clients in the U.S. and plans to enter the U.K. next year. The novelty of our service affords us exclusive and unprecedented data with nearly ideal conditions to learn from it. We have more than 100 data scientists that power algorithmic capabilities used throughout the company. We have algorithms for recommender systems, merchandise buying, inventory management, relationship management, logistics, operations — we even have algorithms for designing clothes! Each provides material and measurable returns, enabling us to better serve our clients, while providing a protective barrier against competition. Yet, virtually none of these capabilities were asked for by executives, product managers, or domain experts — and not even by a data science manager (and certainly not by me). Instead, they were born out of curiosity and extracurricular tinkering by data scientists.

Data scientists are a curious bunch, especially the good ones. They work towards clear goals, and they 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: “Is there a better way that we can characterize a client’s style?” “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 testing and learning. .... "