The Datafication of Human Resources

The Datafication of Human Resources:

Is Human Resources a data science? Or rather is it a discipline of experience, judgement, and “gut feel?” Now is the time to ask ourselves this question.

I recently attended a conference where a speaker mentioned that 57% of GDP is spent on payroll. Do we have any idea how to really understand and optimize this multi-trillion dollar investment? Unfortunately the answer is no.

I have the opportunity to meet with HR and talent leaders around the world every day, and most of them ask for help with modern best-practices, innovative ideas, strategies for recruiting, leadership, and succession, and understanding how to better optimize their HR structure.

But more and more they are asking us for help with data. What should I be spending? How can I better leverage the data I have? How can we use data to hire the right people, help people perform better, help people progress in our organization, and overall improve business results?

Our research shows that after decades of work building HR data warehouses and trying to develop good reporting tools for HR, our industry is making a seismic shift toward data science... and in a positive way - positive for employees, managers, and the business.

This is not traditional "HR analytics," this is bringing together people data into a new "people intelligence" process to understand how our organizations really work. What drives high-performance sales teams? Who will be our best leaders? How can we change behavior to improve customer retention? What drives turnover?

The History of Data in HR: From Time and Motion Studies to Psychological Testing

The Human Resources profession actually has very rich history in the use of data to make decisions. Back in 1911 an engineer by the name of Fredrick Taylor started the whole industry with his book “Principles of Scientific Management,” a book which looked at the science of physical labor. Taylor pioneered time and motion studies, learning for example that laborers who carry 75 pound billets of pig iron are less productive than those who carry 50 pound billets. His work, while demonized by many, actually analyzed the data behind physical work and gave us a whole new science which was later called industrial engineering. (By the way, Taylor was a big fan of employee engagement, its worth reading his book.)

A few years later another Hugo Munsterberg, a student of Taylor, extended these ideas into the world of psychology. Munsterberg, in his book Psychology and Industrial Efficiency, pioneered the idea that it is not only the physical strength of a worker but also his or her psychology (intelligence, memory, attention, skills, nature) that defines business productivity. This breakthrough idea (which today seems obvious) started the industry of worker selection, testing, and job analysis. He actually simulated the job of a trolly car driver to understand how to optimize their decision making, comparing this job to that of a ship captain. This early work was the beginning of what was later called the “assessment center.”

In World War 1 the US army started large scale testing (a test called the Army Alpha) and started the explosive industry we now call the “assessment” industry. In the same period, a student of Sigmund Freud by the name of Carl Jung developed the concept of “social intelligence” and furthered the science of work to teach us that it is not only our individual skills that create productivity, but also how we get along with others and how our personality fits into groups. Jung is credited with the original work of the Myers Briggs test (MBTI), which today is still the most widely used assessment product in the world.

Today of course there are hundreds of tools used to try to assess, test, and profile people and we collect data about everything from education to emotional intelligence. In fact there is a rebirth of testing and assessment vendors coming to market, trying to leverage our “social footprint” and understand what all our tweets, facebook posts, LinkedIn messages, and various other postings on the internet mean about us as job candidates. (This is particularly useful when hiring software engineers, who post a lot of their work on the internet.)

Along Comes HR Technology

While all this science of “people” and “work” was done over the last 80 years, another wave was taking place: the implementation of HR technology. It was only in the 1970s that computers were powerful enough to hold HR data and in the last 40 years we have seen HR technology shift from mainframe HR and payroll systems to client/server HR software now to web and cloud-based software which manages HR, payroll, and nearly every talent process.

The analytics marketplace was behind for years, and while companies implemented all these systems they never really figured out how to take all the “human data” we were collecting (including data in applicant tracking systems) and squeeze it into these old fashioned HR systems to make sense. So, naturally, the field of “HR analytics” focused on doing fancy reports about how many employees we have, our compensation bands, and other tactical but important operational things.

The real breakthrough which led to the datafication of HR we see today was the advent of the “talent management suite” which started in 2005. The first company that tried this was Authoria (now part of PeopleFluent), but dozens of other companies now play in this market and there is more than $4.5 billion of talent management software sold today. The unique breakthrough of the talent management suite, which makes it transformational for talent analytics, is that these systems now store a wide range of data about people and how they work on an ongoing basis.

Talent management systems (and new integrated HRMS systems) know your performance history, your education and background, all your prior job assignments, your leadership roles, your training, the tests you took (usually), and now even your social interactions while at work. So these systems are a gold mine of information to help us understand what drives performance, engagement, leadership, and collaborative work.

Today: BigData in HR - The "Haves" and the "Have Nots"

Which leads us to where we are today. Now, for the first time in the history of HR, we have data about people, data about the organization, and a huge range of tools to help us combine and correlate this data with business data (sales, customer retention, error rates, etc.) to apply data science to HR. While most HR teams are not at all ready for this revolution yet, the time is here – and our research shows that 14% of the companies have totally “unlocked this key” and they are understanding their workforce like never before.

We are now in a world of "haves" and "have nots." Companies who have reached level 3 and 4 in our research (the top 14%) are generating 30% higher stock returns, twice the recruiting effectiveness, and more than twice the ability to develop leaders than their peers. But the rest of us, the 86%, are stuck in a reporting mess.

Fig 1: "Under the water" with Talent Analytics

As this image shows, companies have to invest in data infrastructure to reach the "promised land." And it goes beyond only investing in technology: these top companies have built what we call a "Talent Analytics Function," a new team dedicated to this entire area.

The Talent Analytics Center of Excellence or Team must be a multi-disciplinary group. Our research shows that this group needs a variety of skills: technical data skills, statistics, business understanding, and performance consulting.

Fig 2: The Talent Analytics Team

When these skills are brought together, the organization can move down the learning curve to understand the power of the data in your organization. This team also brings together the typically isolated groups like "recruiting analytics" and "learning analytics" and "engagement measurement" and "compensation analysis." Together these people can look at all the important data in the company and understand how it relates to business outcomes.

Now is the Time

Our research shows tremendous interest and investment moving into this area. More than 60% of the companies we surveyed are increasing investment and trying to build a strategic solution to collect people-related data and analyze people investments well.

Can Human Resources be a data-driven team? Absolutely yes. The companies we talk with in the "haves" of this market are generating amazing returns on this insight.

Why Talent Analytics is Good for People

Let me make one final point. People often get nervous about companies using data to make people-related decisions. Ultimately this is a good thing - if an employer can better find the right candidate, that person has a better job and a better career. If a manager can understand what drives customer retention, he and you as an employee can modify our work environment to make customers (and employees) more happy. If the organization builds a model to understand turnover, they can better train managers and improve the work environment to make work more fun and enjoyable.

While many of the examples we cite in our research talk about business returns, the real value of "people data" is helping people and their organizations learn how to improve their engagement, performance, and results. And who wouldn't like to be happier, more productive, and fit better into our jobs?

What is your organization doing in this area? What are your challenges?

I look forward to hearing from you.

Josh Bersin writes and researches corporate talent, learning, leadership, and HR best-practices around the world He is Principal, Deloitte Consulting LLP and founder of Bersin by Deloitte. You can follow Josh here or on twitter josh_bersin.

Photo: Monkey Business Images / Shutterstock