By: Dr. Rick Brattin
Harvard Business Review once proclaimed the data scientist as the sexiest job of the 21st century. Bloomberg has said data scientists are the new superheroes. It’s no secret there is a shortage of data analytic skills today, especially with regard to big data. A quick Google search on “data analytics shortage” will find thousands of articles written on this topic. In typical supply and demand fashion, the job market is responding by pushing salaries to all-time highs. According to job recruiting site Glassdoor, the national average salary for a data scientist is $113,436, and many of their listings top $200,000 for experienced candidates.
Those of us who consider ourselves part of the data analytics movement are often quick to repeat stories like these, almost as a marketing campaign to recruit others into the fold. I believe, however, that we may be inadvertently sabotaging the long-term growth of our own field, with the sabotage in the form of discouragement and isolationism. We often paint data analytics as an exclusive club open only to those with the highest of statistical and technical know-how. This view fails to recognize that demand for the data analytics’ skill set extends well beyond the capabilities of a small group of elite data scientists.
To illustrate my point, I’ll use a popular framework of data analytic talent first proposed by McKinsey Global Institute (MGI), a research organization dedicated to understanding the evolution of the global economy. MGI segments data analytic talent into three groups: deep analytical, supporting technology and big data savvy.
The deep analytical group are people who conduct data analysis and have advanced training in statistics and/or advanced data analytic methods. Examples include actuaries, operations research analysts and statisticians. According to the US Bureau of Labor Statistics, actuaries are exam-certified professionals who analyze the financial costs of risk and uncertainty. Operations research analysts and statisticians typically hold advanced degrees in a quantitative field and solve problems in business, engineering, health care or other areas.
The supporting technology group includes people who focus on the technological aspects of data analytic solutions. Examples include computer scientists, software engineers, computer programmers and database administrators. These are people skilled at collecting data for analytics and writing software for conducting specialized analysis.
The big data savvy group includes people who demonstrate a keen understanding of their business and are capable of defining relevant questions that data analytics can answer. They need only possess a basic knowledge of statistics and/or data analytic methods. Examples include business and functional managers, engineers, market researchers and psychologists.
The data scientist role is most closely aligned with MGI’s deep analytical group. They have advanced understanding of statistics, technology and business. These are rare individuals, perhaps worthy of the hype and high salaries. But by focusing our attention on these few, we almost discount the contributions of the other two groups — especially the big data savvy group.
MGI agrees. They estimate a shortage of 1.5 million data-savvy workers needed to take full advantage of big data in the U.S. alone. These are not data scientists. They are managers, analysts, and researchers — roles held in business for decades. Forbes.com echoes this idea by including decision-making, problem-solving and the ability to analyze data in their list of the top skills employers want.
My point is this. The data scientist plays an important role in data analytics and big data, but that is not the only role. I believe the true potential of data analytics lies to a large degree within the collective hands of managers and analysts, those who understand their business and know just enough about big data to add real business value.
Dr. Rick Brattin is an assistant professor of Computer Information Systems at Missouri State University. He has 25 years of experience in data analytics, business intelligence, and information governance withFortune 100 companies. Email: email@example.com.
This article appeared in the December 17th, 2016 edition of the News-Leader and can be accessed online here.