Has The People Analytics Hype Bubble Burst?

Aug 29, 2016


For several years, pundits and analytics vendors have been pitching that CHROs need to jump on the people analytics bandwagon. Big data evangelists warn us companies who don’t will soon be on the scrap heap of history.

Deloitte’s 2015 Global Human Capital Trends report gloomily told us people analytics was “stuck in neutral,” only to do an about-face a year later. In the 2016 report, Bersin and his colleagues seemed giddy with excitement that the percentage of companies able to develop predictive analytics models had doubled to 8 percent. To us, that means 92% can’t.

Still, the drumbeat goes on. Companies that haven’t jumped head first into analytics are tagged as laggards, unsophisticated, and primitive.

What is Really Going On

There may be excellent reasons HR hasn’t jumped into the fray. Little of it is lack of desire.

Before we get into the reasons, let us explain that some in the industry draw a sharp line between business intelligence and talent analytics. We don’t, for two reasons: practices are merging into end-to-end solutions, and talent analytics is essential to business success.

From our corner of the world, there are five factors holding HR back:

  • the failures of speculative analytics,
  • normal risk aversion,
  • questionable metrics,
  • lack of organizational readiness, and
  • and the hard work in analytics.

D14_HCM_People Analytics_LP

Speculative Analytics

Much of the early work in analytics was speculative. In 2013, we talked with an analytics practitioner who assured us that the way to excellence was for companies to gather all their data into a massive repository. He would then apply sophisticated statistical modeling to find “the story the data is telling them” to divine the path to talent strategy.

That method takes an enormous amount of resources as it tries to find patterns in chaos, and, in the end, any conclusions are as speculative as the method. We can understand why HR organizations would be reluctant.

Risk Aversion

Many companies are cautious about taking part in the expensive, high-risk leading edge of technology. They consciously follow a strategy of waiting until the bleeding on the edge stops before they consider new methods, understanding Moore’s law and the effects on pricing and capability. They will join in when things settle down.

Questionable Metrics

Business leaders have a hard time connecting what HR measures and what matters to the firm. CEOs care about performance, but as the recent performance management movement shows, they have little faith in what reviews measure. In learning, few measure past Kirkpatrick levels 1 and 2.

Organizational Readiness

HR organizations are not ready to take analytics on, and upskilling takes time. Organizations don’t have the expertise or processes in place to answer the questions their CEOs want answered.

In “analytically challenged” organizations, business leaders rely on intuition. They have built successful careers and companies on gut instinct and see no reason to change.

Analytics is Hard Work

Analytical tools are becoming ubiquitous. Most talent management software suites have some analytical capabilities built-in, including the ability to bring in operational data from other systems. It won’t be long before correlation analysis and probability models will be mainstream.

But software is the least concern we have for talent analytics. Of more importance is the ability to apply scientific methods to the inquiry. We don’t mean that HR needs to become data scientists. What practitioners need to learn is the discipline of using the scientific method to drive their analytical investigations.

But even more important than the methods is the cultural change required to make use of people analytics. Convincing gut-instinct CEOs to trust their data is only the beginning.

Analytics Has Come of Age

One of the stumbling blocks for analytics adoption has been that business intelligence tools were built for IT. They require substantial expensive up-front data modeling effort, and the tools are difficult to use. Gartner’s Magic Quadrant for Business Intelligence and Analytics Platforms report tells us we have reached a tipping point where ordinary business users will take charge.

Gartner report shows us that analytics solutions have proliferated and matured. Most have become end-to-end analytics solutions, and many are having success with inexpensive mashups and open-source software. Talent management platforms like Workday, SumTotal, and SuccessFactors offer embedded analytics suites built for ordinary business users. According to Gartner, by 2018 most business users and analysts will have self-service tools to prepare data for analysis.

Analytics technology is ready. Now it’s up to talent managers to create data-driven cultures.


1.  Ransbotham, Sam, David Kiron, and Pamela Kirk Prentice. “Beyond the Hype: The Hard Work Behind Analytics Success.”

2. Parenteau, Josh, "Magic Quadrant for Business Intelligence and Analytics Platforms." Gartner, Inc. February 4, 2016. 

Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.


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