Clearing the Air on Big Data in Recruiting

Clearing the Air on Big Data in Recruiting

Mike Roberts

recruiting with big data analyticsThere’s a lot of curiosity as well as uncertainty in the recruiting field around the ideas of big data and analytics—and rightly so. Pundits have been talking for years now about how the duo will help talent acquisition teams transform a bunch of traditionally disparate, disconnected data sources into unprecedented forms of intelligence. However, few use cases have yet to emerge.

Interestingly, KPMG recently asked almost 400 C-level and HR leaders for their thoughts on this topic. More than half of the survey takers remain skeptical about the potential for big data and analytics. However, 80% of those same respondents expect their organization to “either begin or increase the use of big data and advanced analytics over the next three years.”

Despite a majority not knowing exactly how they’ll use these types of emerging technologies, the mix of KPMG’s numbers above indicates HR leadership expects big things from them in the near future. While recruiters should really be getting comfortable with metrics and basic analytics now, this post will take a more forward-looking view of the world of big data and advanced analytics.

We’ll clear a few things up by providing some background on what exactly big data is, and then drill down into the future of next-gen advanced analytics technologies in recruiting.

Defining Big Data for Recruiters

There’s a common misconception that big data is simply an unusually large set of data. If size were the only factor, then people wouldn’t even be discussing big data today, because modern analytics tools would be able to handle it. In reality, size—or volume—is just one component of the big data equation. And there are two additional components that make big data unique: velocity and variety.

Let’s take a look at what each of these components (the “3 Vs”) mean:

  • Volume: As mentioned, volume has to do with the size of data sets. When we talk about big data, we’re typically talking about huge data sets—with transactions ranging from terabytes to as big as petabytes (which is 1,000 terabytes).
  • Variety: Data comes in many forms, or varieties. When we talk about big data, there are two high-level varieties of data you should know: structured and unstructured. Structured data has a data model and can be easily sorted and analyzed (think about exporting data from your Applicant Tracking System to a spreadsheet). Unstructured data, however, has no data model (think about an export of the last 1 million tweets mentioning your company).
  • Velocity: At speeds far greater than ever before, data can come from so many different sources today. Velocity has to do with how quickly that data is being transmitted and received. Typically, large volumes of data can impede the speed in which these transactions happen.

It’s these factors combined that make up the concept of “big data.” Some experts even talk about two additional V’s—value and veracity. Fact of the matter is, the complexity of these components is too much for traditional analytics solutions to handle. So when KPMG’s survey showed that HR leaders expect to “begin or increase the use of big data and advanced analytics,” that’s because making use of all this data is no easy task.

The Challenge of Actually Using Big Data

A second point that should be made is everyone’s talking about using big data, but less are discussing the robust analytical tools required for transforming that data into actionable intelligence. The big data market is—in reality—relatively new but maturing rapidly. Just a thought a decade ago, analyst firms now peg the big data analytics market size as large as $125 billion.

With this market maturity has come significant reductions in the resources required to make use of big data. At the same time, this has made access to big data analytics tools easier and has even created new job functions. This is why we seem to be approaching an inflection point where broader adoption—at a much quicker rate—in talent acquisition (and all business functions) is on the horizon.

Examples of Big Data Use in Recruiting

By now, many people have come to learn why companies have been slow to take advantage of big data. But with the quickening market maturity, more than ever today they’re starting to ask how it can be used. Although the complexity of the data sets may seem a bit daunting for recruiting organizations, some innovative companies are already using it. The insights that could potentially be gleaned are not something to ignore.

In our next blog post, we’ll dive into a few use cases for big data in recruiting, but to set the stage a number of examples will be shared below. These are examples of how statistical modeling could be applied to help recruiters do their jobs more efficiently and predictively:

  • Better predict hiring needs and move beyond “just-in-time” recruiting
  • Optimize hiring funnel flow performance to fill positions more efficiently
  • Identify correlations between recruiting performance and business performance
  • Understand the connection between brand perception and recruiting performance
  • Identify the characteristics of candidates that lead to quality and retainable hires
  • Better understand business units/divisions/hiring manager needs for competencies and skills
  • Correlate applicant source with long-term employee performance

The main thing to keep in mind is that insights gained from big data will come from the analysis of massive unstructured and structured data sets. So when we share examples like “Identify the characteristics of candidates that lead to quality and retainable hires,” these characteristics could be found by looking at personality and competency assessment data, performance reviews, broader departmental performance, and so much more.

The first step toward making any data-driven decisions is to get a better understanding of analytics in recruiting in general. To learn more read our whitepaper, Analytics in Talent Acquisition: The Hype, the Reality, and the Future.

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