Although it’s fast becoming a hot position, ask different people what a “data scientist” is and you’ll get different responses. Invariably, you’ll hear buzzwords like Big Data, Hadoop and Cassandra, as well as technical terms like predictive modeling and regression analysis.

If you’re not familiar with these, the role may be something of a mystery, but it is an important and lucrative one at many tech companies.

If you’re not familiar with these, the role may be something of a mystery, but it is an important and lucrative one at many tech companies.

Increasingly, it’s also increasingly important outside of Silicon Valley behemoths like Google and Facebook, and for good reason. As detailed by AdAge’s Kate Kaye, major brands like Unilever, General Mills and Sony are building or expanding their data teams.

It’s not easy, however. As Kaye notes, “These makers of household brands are at varying stages of building in-house data analysis operations, but just about every type of company trying to harness unwieldy data sets is battling to recruit from a dearth of candidates to fill these positions. ”

So what does a data scientist do, why are they so coveted, and does your company need one?

IBM, a major player in the so-called Big Data market, explains:

A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.

According to IBM’s VP of big data products, Anjul Bhambhri, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”

While the sophistication of the data scientist, and the tools with which she plies her trade, may be foreign to most businesses, many businesses of all shapes and sizes have recognized the importance of data and analytics. Thanks to services like Google Analytics, many are already putting data and analytics to use.

Data scientist, or analytics expert?

That raises the question: is a “data scientist” simply and over-glorified “analytics expert.” While the difference may be, in some cases, semantic, a sensible way of differentiating the two is to identify some of the capabilities commonly associated with each:

Data scientists:

  • May be involved in the design and development of systems that collect and process large amounts of data using tools like Hadoop and programming languages like R
  • Need to have a deep understanding of statistics and probability
  • Are capable of designing and testing predictive models
  • Provide the greatest value by answering the questions “Where are we likely going?” and “What would we need to do to go somewhere else?”
  • Will realistically need to acquire a high level of domain expertise

Analytics experts:

  • Analyze smaller or more specific sets of data typically collected by third-party tools
  • Primarily use existing services and applications that provide visualizations of data collected
  • Do not require a formal scientific background
  • Are best capable of answering the questions “Where have we been?” and “Where are we today?”
  • Should have some domain expertise

In short, data scientists, to varying degrees, build the infrastructure of data collection and analysis and then mine that data to produce insights that can help understand where the business is headed. Analytics experts use existing tools to analyze sets of data defined and bounded by those tools to provide insight about where a business has been and where it currently is.

What do you need?

So which one do you need? For most SMEs, the data scientist is likely to remain out of reach. Demand exceeds supply, and the cost of a data scientist is too great. Furthermore, SMEs face technical challenges. For instance, many of these businesses simply don’t generate enough data to justify development using Big Data tools or to make modeling a reliable exercise.

That, of course, doesn’t mean that the rise of Big Data and the data scientist will pass SMEs by. Numerous companies are trying to build sophisticated platforms that will take turnkey analytics to the next level, potentially allowing companies to capture more data and visualize that data in ways that allow them to garner the kinds of insights that might otherwise be obtainable only through the work of a data scientist.

For some larger enterprises, data scientist is not a new role. For many others, the proliferation of technology components required to support data science, many of them free, has made data science a viable proposition. But for these companies, having a data scientist (or team of them) is just the start: equipping an organization to make data-driven decisions, and perhaps more importantly, to know when to make data-driven decisions, is the only way to make an investment in Big Data and data scientists pay off.