The area of HR analytics, talent analytics, or as it is now called “people analytics” has been around for a long time. If we look back in time, ten years ago companies tried to build ”HR Analytics” systems (typicall called HR data warehouses) to help companies look at simple metrics like “total headcount,” “time to hire” and “retention rate” and clean up their messy, often inaccurate people data. Quite a few companies built these databases, but they were primarily used to be a single system of record across the many HR platforms in place.

During the last ten years we watched the discussion with HR stay very tactical, focused on operational reporting and simply fixing the mess of incompatible HR systems we have. There were many HR and learning analytics presentations and a few conferences, but most of the focus was helping technical practitioners improve their reporting systems. The idea of predictive analytics was little more than ROI studies to look at whether a training program worked.

See: What is the Future of Predictive Analytics in HR?

Suddenly around 2011, with the focus on Big Data, we sensed a shift in the market. To understand how well predictive analytics was taking hold, read this early research on “Big Data in HR” and developed a maturity model (it was published in the Fall of 2012). They discovered a world of “ Haves” and “Have Nots.” A small number of companies were investing heavily in predictive people analytics, but most were barely getting started.

The whole idea of our focus on “Big Data in HR” was to help HR organisations realise that they, too, could enjoy the wave of interest in Moneyball and Big Data. HR is not as interesting a topic as homeland security or cyber warfare, but it is a big area of spending, so there is a lot of opportunity in this huge data set. In the end, the world of “People Analytics” was born.

Today, while the topic is hot, HR teams are just starting to get good at analytics. The problem has not been the concept, but rather the focus. We spent far too much time trying to measure HR and L&D spending, and figure out which HR programs were adding value.

While that seems interesting HR managers, typically business people just don’t care. What they want is information that helps them run the company better: “Get me the right people into the job, make them productive and happy, and get them to help us attract more customers and drive more revenue. I don’t care if your L&D program has a 200% ROI or not.”

According to a report, entitled High-Impact Talent Analytics, established the first-ever research-based maturity model for analytics. It showed that there were a small set of companies (less than 5% of the market) that were way ahead of the curve. These advanced companies were looking at people-related data in a very strategic way, and they were making far better decisions about who to hire, who to promote, how much to pay people, and much more.

Since then, interest in this market has exploded. As Josh Bersin states, “I mean like an atomic bomb. Everyone is now talking about it, and the whole concept has changed.”

“At this point, entering 2015, I believe “The Geeks have Arrived.” Statisticians, mathematicians, and engineers have entered the people analytics space,” claims Bersin.

The practitioners, who are among the leaders in this space, were all experienced in bringing together data, cleaning it up, and doing all types of analysis. Of course, their companies have various issues with data quality, systems, and infrastructure – but they, as a group “get it.” They understand the potential, they understand the problem, and they have the skills to get work done. Also, they are not just analysing HR issues, they are analysing the business.

While most HR organisations are still struggling to clean up their data and build their teams, the momentum is coming on strong. Yet, technical talent has now figured out that the old-fashioned backwater HR department may be one of the most exciting places to work.

See also: HR Professionals: Are you ready for People Analytics?