Big Data in Human Resources: A World of Haves And Have-Nots

Author

Bersin, Josh

October 8, 2013

I’ve written several times about the Datafication of HR and BigData in Human Resources, explaining the tremendous business opportunity companies have to leverage their employee data to improve operational performance.

This week we introduced research conducted over the last two years and the results were astounding: while more than 60% of companies are now investing in BigData and analytics tools to help make their HR departments more data-driven, there is a huge chasm between the “haves” and the “have nots.”

The Chasm Between Analytics Leaders And Everyone Else

Using our research methodology (which reached around 480 large organizations), we found that only 4% of companies have achieved the capability to perform “predictive analytics” about their workforce. (Understanding the drivers of performance and retention, using statistics to decide who to hire, analyzing how pay correlates to performance, etc.)  In fact, in our research only 14% have done any significant “statistical analysis” of employee data at all.

What are the rest doing?  Dealing with reporting. These remaining 84% on the other side of the chasm are still dealing with data management and reporting challenges, trying to get out from under the burden of ad-hoc reports to deliver standard operational metrics.


Fig 1: Bersin by Deloitte Talent Analytics Maturity Model

Analytics Leaders Gain Tremendous Returns

The research also showed that these leading companies generate high returns for their hard work: their stock market returns are 30% higher than the S&P 500, they are twice as likely to be delivering high impact recruiting solutions, and their leadership pipelines are 2.5X healthier.

In addition, these HR teams are four times more likely to be respected by their business counterparts for their data-driven decision-making, giving them true potential to help change the business.

Talent Analytics Is Much More than Big Data Tools and Statistics

The research also shows that while tools are important, the leading companies have invested in other things:  sound data management which delivers quality data, business consulting capabilities to focus on the right problems, strong relationships with finance and operational analytics teams, and visual design and communications skills. These are all critical skills, in addition to statistics, data, and math.

In fact most HR teams tell us they can find statisticians fairly easily (I/O psychologists study statistics) but have a hard time finding project managers, people who can combine “data” and “business,” and people who can translate a “finding” into a program or solution that drives business change.

Functionally, high performing analytics teams have multi-disciplinary skills. These include business understanding, consulting skills, data visualization, data management, statistics, and executive presence. The analytics team not only has to diagnose and solve business problems, but often confront executives with surprising or new news.

One of the biggest challenges to BigData analytics in companies we’ve talked with is getting people to change their behavior once they have the data. Most managers have years of “belief systems” and “experience” that holds them back from using the data science we provide.

An Example: Paying People To Drive Performance

Let me cite one of the examples from the research. One company studied the turnover and retention behavior of employees based on pay raises. Their traditional approach was to pay based on a normal curve and give top performers slightly higher raises than second-tier performers, they received slightly more than the next group, and so on.

It turns out, as much of our other research shows, that this “normal distribution” curve of pay is a big mistake. What the research found was that companies in the second and third quintile of performance (good solid performers) would stay with the company even if their raise was as low as 91% of average increases in their job class.  So these folks were being overpaid.

On the other hand, people at the top of the performance curve would leave the company unless they received 115-120% of the average pay increase for their job class, indicating that the payroll money should go here.

As most managers know, top performers out deliver mid-level performers by a wide margin, so paying top people “much more” is a huge advantage if it prevents them from leaving.

In this particular case the findings did not solve the problem. Even after being informed, managers continued to pay their people the old way (belief systems die hard and managers don’t like to make waves). So the company had to roll out a massive training program and a new tool set for compensation distribution based on the data science, essentially over-riding typical manager thinking.

Only 14% Of Companies Are Gaining Such Benefits

These kinds of benefits are everywhere to be found in companies. We’ve seen dozens of such stories about applying data science to HR and in every case the return on investment is high.

Unfortunately, because so many companies have not invested in this area yet the vast majority are unable to achieve these results.

So the bottom line of this research is simple: the war for data is on.

If you are not investing in an integrated analytics capability within HR and creating a BigData solution about your own internal people and payroll, you’re going to fall behind.

The companies that unlock some of the secrets of their own people performance will greatly outperform their peers.

You can read more about the research here.

You can follow me to stay up to date on trends, research, and news in all areas of HR, leadership, and talent management on twitter at @josh_bersin.

For more information on Bersin by Deloitte, please visit http://www.bersin.com .
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