How To Encourage Creativity in Data Science


Data science is evolving, into much more than pounding out math-driven algorithms on a set of data and then stumbling upon an answer. Amassing, structuring, and analyzing data will often yield interesting results and create meaning from it, but if it’s irrelevant to your business, it’s not exactly useful. Instead, what’s more interesting to us, is getting information out of data that is insightful and powerful enough to propel your business forward. We consider true predictive analytics as valuable only when it will help you to see around corners and make the best possible business decisions, ahead of your competition. But to get there, it’s creativity that is at the forefront of any predictive analytics approach.

The “science” of data science requires forming questions and an unstructured thought process to establish a creative hypothesis. Data analysis should be applied after this step. Asking questions and arriving at a thesis or concept first and then looking at what can be done with the data, is typically going to result in getting the most value from the data, and hopefully, business-advancing results. This can be challenging for some data scientists who may be more accustomed to linear thinking and less prone to letting their minds wander to apply a creative hypothesis.  

Confessions of a Fintech Chief Data Scientist takes a look at how businesses can make sure their data science team is evolving in a creative direction. The author, Justin B. Dickerson offers the following excellent advice to FinTech companies looking to get the most of their data scientists.


Business Acumen

The most creative data scientists aren’t necessarily the ones with Ph.D’s and a solely comprehensive data science knowledge base, says Dickerson. Backgrounds in business are extremely valuable in understanding industry and business challenges in order to translate analytics and data into the most value to a company. Discerning what business problems are important to solve and the ability to identify creative ways a company can leverage data is crucial for success. Focusing technical efforts in the right direction is easier when there is an understanding of how businesses operate and how solving a problem can impact the company.


Less Is More

Data science is an expanding and in-demand field, resulting in many organizations feeling the frenzy and assuming a larger data science team will equate to increased value for their business. And, let's face it, competition for the relatively small pool of data scientists is intense and there is a tendency for companies to over-acquire this much hyped talent. However, overdoing it lends itself to the risk of group think. Dickerson recommends a smaller, more diverse team to promote collaboration and often better results.


Cross Training is Key

Dickerson says that even experienced data scientists benefit from cross-training with other departments. Encouraging knowledge sharing and allowing data science professionals to spend time in other areas of the business can improve analysis. Allowing for space to spend time with others in the company, like ride-alongs with commercial sales reps, will yield a better business and industry understanding and spark creativity more than keeping the technical team isolated.  


In this technical space, the role of creativity should not be overlooked, and business understanding will certainly help with creative thought process beyond structured data science. As Creativity in Data Science puts it, “you can’t be creative on the piano if you don’t have some familiarity with piano.” 


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