Comparing Analytics and Data Science

Not too long ago, The Harvard Business Review dubbed data scientist “the sexiest job of the 21st century.” What’s more, McKinsey reported that the United States alone could face a shortage of 140,000 to 190,000 by 2018. In short, interest is exploding.

Brass tacks: These days, analytics and data science are all the rage, but why now?

Many reasons spring to mind. Credit the rise of Big Data and its proselytizers—i.e., the likes of Nate Silver, Moneyball pioneer Billy Beane, and powerful, data gobbling companies such as Netflix, Google, and Facebook. If it seems like the geeks have inherited the earth, you’re right. These days, data-oriented practices are even challenging orthodoxies in game shows such as Jeopardy! This is great news if you’re studying analytics—or thinking about it.

There’s no shortage of myths at play here, but let’s cut through the hype. What exactly is a data scientist anyway? How does one differ from a business analyst? For answers to some of these questions, check out the infographic below:

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Traditional analysts tend to be entry-level employees, often with undergraduate degrees in business, economics, or a related field. While on the clock, they have historically analyzed structured datasets via Microsoft Excel (think Pivot Tables) and, to some extent Access. They have also worked extensively with many of the different stakeholders within the organization.

By way of contrast, data scientists typically have done graduate or even post-doc work in probability, statistics, data modeling, and/or mathematics. They handle fundamentally more complicated data—i.e., the unstructured kind. To this end, they have had to master applications such as R, Hadoop, or another NoSQL tool.

Equipped with profound knowledge and commensurate computing firepower, data scientists can answer critical questions such as “What can I do with all this Big Data?” Rather than just describing what happened in the past, they can go one step further. They can build elaborate models that potentially predict what might occur – and why.

Truth be told, the delineations between analysts and data scientists can get a little blurry. This begs the question, how can you tell the difference between the two?

Consider the following general rules of thumb. First, data scientists are more likely to work on macro-level problems such as healthcare (see video below). Analysts work with stakeholders to identify data sources and build the analytics models in a particular business domain. It’s important to note that organizations might not need to hire data scientists. Excel, Access, and their ilk can unearth fascinating business insights with structured datasets.

Second, most businesses analysts traditionally haven’t been expected to do data mining and run regression analyses. Interestingly, more progressive programs realize the increasing importance of these skills. For their part, most data scientists cannot just get by in Excel. The best analysts and data scientists, however, have one thing in common: they are extremely curious. Both excel at data discovery, which is a major emphasis of my book The Visual Organization.

Make no mistake. Irrespective of moniker, the ability to analyze—and make predictions based on—increasingly complex datasets will be invaluable for the foreseeable future. The era of Big Data has most certainly arrived.

Phil Simon is a sought-after speaker and recognized authority on technology, trends, communication, and management. He advises companies on how to optimize their use of technology has written seven books, most recently Message Not Received. His contributions have been featured in Harvard Business Review, CNN, Inc., The New York Times, Wired, NBC, CNBC, Wired, The Huffington Post, FoxNews, abcnews.com, forbes.com, BusinessWeek, and many other high-profile media outlets. He holds degrees from Carnegie Mellon University and Cornell University. He lives just outside of Las Vegas, NV. Stalk him on Twitter at @philsimon.