Buzzword Alert! HR Analytics and Big Data – How to separate reality from the buzz

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Buzzword Alert! HR Analytics and Big Data – How to separate reality from the buzz

Guest post by Sandi Krason of HRMS Solutions. Sandi has over 20 years experience supporting and servicing HR technology needs of mid-market organizations, ranging from product development, support, training, sales and marketing. With a unique perspective into what end users actually need, she has deep insight into the world of HRIS systems. 

There is so much buzz around “big data” these days, you might think HR departments everywhere are diving in and embracing HR analytics, right? Big data is geared to help predict which recruiting and succession plans are most successful –  with little human involvement in the decision-making process. I would argue that the big data adoption is far from reality, and only the very large or very progressive companies have acquired the ability to truly perform meaningful and accurate HR analytics.

Based on the success these companies, mostly enterprise-level, have realized when they achieve meaningful HR analytics, not to mention the advancements marketing, sales and product management departments have achieved as a result of big data analysis, the movement is only going to get stronger. Thus, let’s get familiar with some of these concepts.

HR Analytics – Can the analysis of data really lead to better decisions about the people we employ?

HR analytics correlates business and employee data to show the impact HR has on an organization to then create strategies to enhance outcomes. (source: Techopedia)

As HR professionals, we’ve been trained to engage in live interactions with employees in order to promote a healthy, productive workforce. Does all that just get tossed out the window in favor of more spreadsheet analysis? Here’s an example of one company, Xerox, who might say, “yes!”

Xerox uses online evaluations that include personality testing, cognitive-skill assessment, and multiple-choice questions based on real-life scenarios. Responses are combined with other information about the candidates, then each is given a red, yellow or green rating. This study was detailed in an article which appeared in The Atlantic, titled “They’re Watching You at Work,” written by Don Peck. According to Peck, the testing resulted in a 20% decrease in attrition and an increase in promotions. Impressive! The results are hard to ignore.

Big Data – what does it really mean?

The “big”  in “big data” refers to the actual amount of data. According to, the term “big data” refers to data sets, typically consisting of billions or trillions of records, that are so vast and complex that they require new and powerful computational resources to process. But many argue that it isn’t just about the volume of data, but also the velocity and variety of data.

Despite the definition, for me, it is still difficult to match the concept of big data with the traditional role human services professionals play in the workforce. But as I read Peck’s article in The Atlantic, it began to make more sense. Peck describes video games that are used to test applicants’ decision-making skills and algorithms that assess the quality of developers by searching the web for any open source code they have written, their participation on developer social forums, and even their preferences for websites not specifically related to programming.

According to the article, it is even possible to identify undervalued programmers based on the level of their activity on professional networking sites. Combine the developer’s high activity level with the fact that several other employees have recently left the organization at which the developer is employed, and the company’s stock price is dropping, and the developer’s receptiveness to a new job offer seems more likely. The ability to take all of the data gathered from these various resources and process it in a variety of ways is an example of big data at work.

Predictive Analytics – not just the “crystal ball” of the future

Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. It forecasts what might happen in the future with an acceptable level of reliability and includes what-if scenarios and risk assessment. (source:

Just as retailers can use big data analysis to pinpoint pricing sweet spots and forecast demand, HR teams can now determine which traits and behaviors best predict an employee’s leadership potential. Now let’s take a closer look at the reality and myths of big data and HR analytics.

Talent analytics leading the way

In The Atlantic article, Peck argues that big data analytics can make HR’s decision-making process more objective. He points out that “our biases are mostly unconscious, and they can run surprisingly deep.” I think we can all agree that reducing, or even eliminating, practices that can lead to unfair and, very possibly, unwise hiring decisions can only be seen as a move in the right direction.

While you or I may question the fairness of replacing human decisions with machine decisions, Peck argues that the trend is pushing us “toward a labor market that’s fairer to people at every stage of their careers.” As examples, he notes that the value of an Ivy League education may, in some cases, be overrated, and some companies are acquiring data that shows no relationship between success and any college education. Sophisticated screening algorithms may make it possible to pinpoint excellent job candidates despite the fact that they “fell off the career ladder” during the recession or struggled in school.  In fact, at Google, GPAs are no longer a factor for anyone who has been out of school for more than two years.

I’m all in favor of getting rid of old stereotypes in favor of a more fair and efficient system, but not every company has the resources of Google, and smaller companies should be careful not to base decisions on inadequate or untested data. Instead, companies with limited resources may choose to apply predictive analytics to one or two areas (several are listed below) in which quality data can be gathered, tested and analyzed.

Beyond Recruiting: The potential impact of big data

Big Data

Experts argue that HR analytics will eventually have a significant impact throughout every aspect of HR. Big data methodologies are predicted to help HR leaders see clear trends and patterns regarding the following:

  • The real impact of pay increases – determine where will salary investments will have the most impact.
  • Factors that predict turnover – identify reasons for high turnover to bring about the right changes and enable quicker response.
  • Factors that predict retention – enable smarter development of retention strategies that offer high value and impact. In the study cited above, Xerox also learned that previous experience had no impact on productivity or retention, but the distance the employee had to travel between home and work had a strong impact on engagement and retention.
  • Performance predictors – reduce costs associated with bad hires and build a happier workforce by getting more people into better-fitting jobs.
  • Most effective training delivery methods – break out the data by age, by workgroup, by position.
  • Training ROI – measure the impact of training on the company’s bottom line.
  • Fraud and accident predictors – reduce loss by focusing on identified patterns.
  • Workforce behaviors that lead to success – improve talent management.
  • Leadership identification – impact long-term corporate performance by accurately identifying high-potential employees.

Current state of affairs

For those of you like me who have been in the workforce as human resource or HR technology professionals for many years already, such dramatic changes to our profession can seem scary. Most of us are not trained as analysts or statisticians. But according to Salesforce CEO Marc Benioff, what we need, rather than higher-level technical and analytic skills, is a new generation of tools that will enable us to easily organize, view and interpret the data. Benioff recently commented that the revolution in data science will fundamentally change how we run our businesses. When asked what this means for an executive without a background in computers, Fortune magazine quoted Benioff as saying:

“Based on the simple fact that there’s just a huge amount more data than ever before, our greatest challenge is making sense of that data. We need a new generation of executives who understand how to manage and lead through data. And we also need a new generation of employees who are able to help us organize and structure our businesses around that data. When I look at the next set of technologies that we have to build in Salesforce, it’s all data-science-based technology. We don’t need more cloud. We don’t need more mobile. We don’t need more social. We need more data science.”

Regarding how confident he was that non-experts can consume this data? he responded that “the whole concept of data science is that the software becomes the expert, and you as the average user are able to understand what’s going on.”

With promises and predictions like this in mind, human resource management systems (HRMS) and human capital management (HCM) vendors, including FinancialForce HCM, which is built on the Salesforce Platform, are adding more and more powerful data analytics capabilities to their software.

If you are ready to begin moving your company down the path of HR analytics adoption, Josh Bersin of Deloitte has developed an easy to understand HR Analytics Adoption Model that steps you through the evolution from highly scalable reporting systems to advanced analytics, risk mitigation and models.

A good first step is to make sure you have the right HRMS or HCM in place. The information collected and stored within the database will serve as a foundation for better decisions, improved processes and an easily validated ROI from human resources.

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