Getting Value From Your Data Scientists

Simply hiring expensive data scientists isn’t enough. To create real business value with data scientists, top management must learn how to manage them effectively.

Data scientists are enjoying a heyday. No longer languishing at the periphery of organizations, these quantitative analysts today are recognized as highly skilled specialists trained to take on the most significant (and often the most complex) business challenges. They are the latest examples of “numbers people,” whose direct lineage goes back to the 1940s, when Ford Motor Co. recruited a team of statistical “whiz kids” (including Robert S. McNamara) from the U.S. military. The recent emergence of the digital enterprise has created a seemingly insatiable management appetite to amass and analyze data. This demand coincides with the rapidly decreasing cost of storing those data and preparing them for analysis, along with the growth in computing power to support the application of increasingly sophisticated techniques for extracting meaning from complex data.

How Data Scientists Differ from Analysts

Today’s data scientists are often singled out as a breed apart — and for good reason. They tend to be better programmers than most statisticians and better statisticians than most programmers. Moreover, they must learn to acquire and manipulate both numeric and nonnumeric data to solve a changing menu of business problems.

To better understand what distinguishes data scientists from other quantitative analysts (and what it takes to get the best out of data scientists), we recently surveyed more than 300 analytics professionals working in different types of companies (ranging from startups to large multinationals) in a variety of industries in the United States to learn how they viewed their work and their place in the organization. About one-third described themselves as data scientists; the rest identified themselves as analysts. (See “Data Scientists and Analysts: What’s the Difference?”) We found substantial differences between the two. For example, data scientists were far more likely to view their work as critical to better business outcomes. Almost all data scientists surveyed — 94% — said analytical abilities are a key element of their companies’ business models and strategies, compared with only 65% of those who identified themselves as analysts. Similarly, 96% of data scientists said their analyses are used to make key decisions, versus 77% of quantitative analysts.

Data Scientists and Analysts: What's the Difference?

View Exhibit

Data scientists differ from other types of analysts in significant respects. Based upon our research and survey, some of the most typical distinctions between data scientists and other types of analysts are highlighted here.