The Growing Popularity of Data Scientists
By now, everyone knows that the “Sexiest Job of the 21st Century” is that of a data scientist. The Harvard Business Review made that declaration in 2012 and the race to become (or rename) oneself as a data scientist was on at a feverish pitch. Bringing in data scientists with a strong educational and professional background is important, of course, but training the business in how to properly use the data scientist is critical as well. Otherwise, it would be like having a fancy new Tesla but not taking the time to learn how to charge it up.
So how does an organization get the most out of their data scientists? How do we alleviate the shortage of data scientists that threatens to stymie the business and societal benefits that these unique folks can bring forth? How do we ensure that data scientists are focused on helping the organization drive financial or business value–as opposed to publishing articles or speaking at conferences, a complaint that I have received more than once?
Let’s start that discussion with some basic but important definitions.
What Is Data Science?
To get more value from our data scientists, we first must understand “What is data science?” The best definition of data science comes from the book Moneyball: “Data science is about identifying variables and metrics that might be better predictors of business performance.”
That’s a very simple description, but let’s deconstruct it anyway.
• Identifying variables and metrics. The data science process must be driven by a creative and curious mind in order to identify and brainstorm the variables and metrics (data sources) upon which to focus the data alignment, transformation, enrichment, and visualization efforts.
• Better predictors. The focus of the data scientist is on predicting what is likely to happen and prescribing what actions to take (versus reporting on what has already happened). This requires a thorough understanding of the decisions that must be made in support of the organization’s business initiatives.
• Business performance. The key deliverable must improve business or financial performance in order for the data science work to be relevant and meaningful to the organization.
As detailed in Moneyball, the Oakland A’s discovered several variables that were better predictors of the value of a baseball player – for example, that on-base percentage was a better predictor of a hitter’s value than batting average.
Finding The Next On-base Percentage
Organizations must help their data science teams to find that next more predictive, on-base percentage kind of variable. And the key to doing this actually lies with the business users, not the data scientists.
Table 1 highlights the roles that business users and data scientists play in collaborating to fully exploit the power and potential of data science.
The Power of Business Decisions
To ensure that the organization is getting the most value out of its data science operation, focus on the business decisions. These decisions the linkage points between business users and data scientists (ensuring that everyone is focused on the same objectives), and it’s around these decisions that the collaboration between business users and data scientists can deliver the most business value
The decisions are key because:
• From a top-down perspective, decisions provide the framework around brainstorming the necessary variables and metrics (data sources), and also dictate your architecture and technology requirements.
• From a bottom-up perspective, the analytic models built from the different variables and metrics (data) create the analytic results (e.g., scores, recommendations, business rules) that will be applied to optimize the decisions that support the organization’s business initiatives.
No data science initiative should exist in a vacuum. The collaboration between business users and data scientists is central to optimizing business processes, uncovering new monetization opportunities, and realizing the most value from your big data analytics investment.