Why some data scientists should really be called decision scientists

In recent years, with the growing importance of analytics and big data, the term “data scientist” has earned some sex appeal. In fact, Thomas Davenport has gone so far as to call data scientist the “Sexiest Job of the 21st Century” [1].

Gartner describes the role as, “An individual that can work with data and analytical models to generate business insights that can be deployed across business processes.” Others have described data scientists as having a mix of computer science, machine learning, hacking and programming skills.

But having worked in the analytics and data-driven decision support area for many years, I believe that leveraging data effectively to enable better decisions requires more than just data sciences. Data science essentially refers to the application of math and technology on data to extract insights for problems, which are very clearly defined. In the real world, however, not all business problems are clearly defined. Many of these problems start off muddy. To help solve them, one needs to understand and appreciate the business context. It requires an interdisciplinary approach consisting of several different skills: business, applied math, technology and behavioral sciences.

I think about enabling data-driven decisions as a journey from data engineering to decision sciences:

• Data engineering is the application of technology to help collect, store, process, transform and structure data to enable it to be used for decision support.
  
  
• Data science is the application of math and technology to solve focus business problems. This involves analysis, visualization and algorithmic/mathematical computations to extract insights in response to clearly defined business problems, questions and hypotheses using clearly identified data elements. Data science integrates and builds on data engineering by adding the discipline of math.
  
  
• Decision science is the interdisciplinary application of business, math, technology, design thinking and behavioral sciences to enable better decisions. Decision science enables addressing business problems that are ill defined and shifting and where the factors affecting the problem are not completely understood. It facilitates the design thinking paradigm: Taking business problems that start off as a hunch or as mysteries to becoming heuristic, rules and judgment based, to becoming algorithm as one starts to see patterns, to becoming codified and tool-ified in parts before being operationalized in systems. Further it enables the on-going creation, translation and consumption of data-driven insights to help organizations make better decisions. Decision science integrates and builds on data sciences by adding the aspects of business context, design thinking and behavioral sciences.

Research organizations and industry pundits have declared a dire shortage of data scientists, which is bound to intensify in the future. While a lot of focus is on acquiring data scientists and filling that talent shortage, we believe that data science is just one aspect of the story. At the core, analytics is an enabler that helps organizations make better data-driven decisions, while decision science completes the equation.

But decision scientists are truly rare – much more rare than data scientists. They are the individuals who can artfully blend business, math, technology and behavioral science. They need to be both precise and good with communication – having the ability to synthesize and gain buy-in for new ideas. Their ultimate objective is not just to produce a working model, but to help businesses make informed, data-driven decisions. While data scientists are about creating analytics, decision scientists also help companies consume them.

At Mu Sigma, we work toward building the next generation of decision scientists. Our Mu Sigma University program transforms smart, motivated, entry-level professionals into decision scientists through a blend of classroom training and real-life projects. This helps to ensure that they have the requisite technical skills, plus other soft skills such as communication, presentation and consensus building.

Our aim is not just to build analytical models for clients and throw them over the fence, but to work as trusted advisors, helping clients institutionalize analytics and decision sciences. It is important that organizations traverse this journey from just data engineering and data sciences to decision sciences. Stopping mid-way in this journey will prevent them from realizing the full potential of data-driven decision-making.

If your organization employs data scientists and/or decision scientists, weigh in – can you see the difference between the two?