People Analytics Takes Off: Ten Things We've Learned

I recently had the opportunity to present the "State of the Market" at a major People Analytics conference here in San Francisco, and I turned it into a "top 10" list.  In this article I'll briefly review them and let you view the slides for more detail.

1.  People Analytics is even more important than we thought.

Our new research (Deloitte Human Capital Trends 2015) shows that 87% of business leaders are highly concerned about retention and engagement, 86% about leadership, and more than 85% about current workforce skills. Despite the vast amount of engagement surveys in businesses, Glassdoor ratings for the average company are 3.1 out of 5 (around a C), with almost a perfect bell curve distribution.  Companies desperately need data to figure out what makes people join, what makes people stay, who is likely to be most successful, and what we can do to build more leadership, customer service, and innovation in the team. All these problems can be directly informed by great People Analytics.

Fig 1:  Glassdoor company ratings, 20,000+ August 2015

2.  People Analytics will grow exponentially, but we are in the early days.

Our research (Deloitte Global Human Capital Trends 2015) showed that the maturity of People Analytics in HR has barely budged in the last year. Does this mean the market is stalled?

No, rather the opposite. Most business trends we study don't grow in a straight line - they grow in an "exponential curve." What this means is that in the early days it seems like nothing is happening - companies are experimenting, vendors are building tools, and only a few examples seem to keep coming up. Here is an example of a simple exponential curve:

Fig 2:  Exponential Growth Curve (compliments of Theemergingfuture.com)

As you can see, for many years the line appears to be flat - but then suddenly it takes off. (Genomic research went this way, as did internet bandwidth, memory density, and most other technological breakthroughs.)

In the case of People Analytics, companies are just now starting to figure this out, and we had almost 300 "Geeks" with us last week, telling me that this market is ready to hit the knee in the curve. In the next ten years I expect the market to start doubling at a bigger and bigger increment each year, until ten years from now this will become a very standard part of HR.

By the way, as I shared at the meeting, most other analytics disciplines in business went through a similar evolution.  Marketing analytics took about 20 years to mature - and today you can buy off the shelf analytics solutions that rival hugely expensive custom customer data warehouse  projects of only ten years ago.

Fig 3:  History of business analytics, Bersin by Deloitte

#3.  Most companies still don't really know what People Analytics really is.

Despite lots of great articles and references to the book Moneyball, most HR executives and leaders are still a little confused about what's going on here. Many think People Analytics is about computing retention rates and better identifying how many employees we have. Some think it's a way to measure the ROI of training or other HR programs.  Our research shows that it's much more - it's bringing together the people data in the company (and there is an ever-expanding amount and set of that data) to solve specific business problems:  sales productivity, retention, fraud, customer satisfaction, etc.

Fig 4:  Redefining People Analytics (Bersin by Deloitte)

"This is not your father's HR data warehouse" - and it's not "HR analytics" either - this is taking all the wonderful data we have about people and putting it to work on very specific business problems. As I explained in my talk, over time I don't think it even belongs in HR - it is likely to move into operations.  (We can discuss that in another article.)

#4.  Data Management remains the biggest barrier.

We had some amazing companies in this meeting (most of the leading Silicon Valley companies, many large financial institutions, manufacturers, and others) and most of them agreed:  their HR data was "bad."  HR and people data is inconsistent, unclean (not correct), out of date, and located in many places. Many large companies still don't really know how many salaried or contract employees they have at a given time, so these teams are dealing with a big data integration problem.

Fig 5:  Bersin by Deloitte Talent Analytics Maturity Model

Our research (Bersin by Deloitte High-Impact Talent Analytics research) shows that more than 80% of companies are stuck dealing with reporting challenges, and while this problem is starting to go away it still plagues most companies. Every company in the room talked about these challenges, and most agreed that there is a lot of "technical debt" to clean up in order to really scale their operations.  (But most agreed the are funding this work and will do it!)

#5.  Modelling is valuable but implementing models is key.

My fifth finding is that we all love models: a great model that predicts retention, a model that predicts the right paths to leadership, a model that tells us how much salary to pay high performers, etc. These models are incredibly valuable - but as the book and movie Moneyball pointed out very clearly, "having the answer does not make it happen."

The hardest part of People Analytics is implementing the changes recommended by the model- which means that People Analytics teams have to be surrounded by very good change management consultants.  I talked with a large company that discovered they were underpaying their high performers and overpaying their mid-level performers (the analysis proved this very accurately). But despite knowing this, the company had a 20 year culture of "fairness" and "equality of pay." It took several years of effort to teach managers (and the organization itself) that people were going to be treated more "unfairly" going forward, and it was ok to give someone a huge raise for high performance and a middling raise for fair performance. The HR team told me it was much harder to implement this than they ever imagined.

#6.  Tools and platforms are here, but there are no Unicorns or Gorillas yet

As with all emerging markets, there are dozens of new tools, technologies, and platforms now available to help you analyze people data. I wont list them here, but suffice it to say, none are Billion dollar companies (Unicorns), and none are Gorillas (read Geoffrey Moore's Crossing the Chasm to understand that concept).

As Moore's book points out, when the market gets hot (we enter the Tornado), the smaller companies that don't have clear "market leadership" get shaken out, and big companies (like the big ERP players) take over.  Almost every major ERP software company is investing in this area (Workday, Oracle, SAP, ADP, IBM, Ceridian, Ultimate, Saba, Cornerstone, and more) - and there are dozens of tools companies (text analytics, retention analytics, sentiment analysis, and even companies that analyze your physical location, your heart rate, and your exercise).  One vendor even analyzes your emails, to see your pattern of communication, helping with what is called Organizational Network Analysis.

All these smallish companies are doing great things, but as the market matures (and it will in the next few years), they'll either "get big fast" or "get bought."  So my warning to buyers is just "beware" and to vendors:  find your "bowling pin" (the part of the market you can hit) and knock it down.  Don't try to be all things to all people, when the market heats up you'll be glad you own some part of this huge market.

#7.  The Geeks will rule this world, but not alone.

This is one I really want to emphasize:  while we need data scientists and statisticians to analyze and build models, the real discipline of People Analytics is very multi-disciplinary. Successful teams include process people, consultants, OD experts, I/O psychologists, visual designers, as well as core IT people. You'll need all these people coming together to make your team work - and yes, this will become a new Center of Excellence in HR - one that must report to the CHRO, not be buried in the HR technology team.

Fig 6:  The People Analytics Center of Excellence (Bersin by Deloitte)

#8.  We will have to deal with many new types of data, and new ways of analyzing it.

The days of analyzing payroll, HRMS, and time and attendance data are over. That isn't nearly enough. Today we have to look at employee engagement survey data, email history data, employee badge and sociometric data (even voice patterns and who you have lunch with), and all the data which will come from wearable devices. Remember your employees are walking around with video cameras and GPS devices all day, so much of the data we will look at over time will be based on location, time, and visual identity.  (One vendor just announced a lathe that identifies its operator by face, and then automatically readjusts its equipment for the person.) 

9.  Security and anonymity will become your middle name.

Thanks to the various credit card and other security breaches we have witnessed recently, employees and legal departments are very nervous about the analysis we do. HR departments and People Analytics team have to take a crash course in data security, privacy, and identity protection. People are beginning to get worried about what this data is being used for.

10.  We have to work together - we are all learning now.

My final point is this:  while much of what we do here will become proprietary competitive advantage to our companies, right now the more we share and learn the better we all are.  Just as the human genome was decoded through the work of many academics sharing information, so must we all share what we are learning so we can move this industry past the "hobby phase" to true industrial scale.

While more and more companies are starting to keep their People Analytics findings confidential, most are sharing what they've learned - because it's not the "findings" that make your company unique, it's what you do about them.  

We learned from HIQ Labs, for example, that employees are 150% more likely to quit if a peer leaves the company than if a manager leaves the company.  This seems like a somewhat obvious finding - so let's share it. The real key is what will you do about it!

I think People Analytics is one of the most exciting trends in business, and we are here to help you (and your HR teams and leaders) understand it, leverage it, and make sure you're taking advantage of it in the coming years.  I welcome your comments and feel free to share your stories.

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About the Author: Josh Bersin is the founder and Principal of Bersin by Deloitte, Deloitte Consulting LLP, a leading research and advisory firm focused on corporate leadership, talent, learning, and the intersection between work and life. Josh is a published author on Forbes, a LinkedIn Influencer, and has appeared on Bloomberg, NPR, and the Wall Street Journal, and speaks at industry conferences and to corporate HR departments around the world. You can contact Josh on twitter at@josh_bersin and follow him athttps://www.dhirubhai.net/in/bersin . Josh's personal blog is atwww.joshbersin.com .

Other Recent Articles by Josh Bersin:

• Are you an Overwhelmed Employee? What to do about it.
• Why People Management is Replacing Talent Management
• Putting the Strategic into Strategic HR
  
• The Alliance: A Manifesto for 21st Century Talent Management
  
• Interview with Reid Hoffman about Talent in the Networked Age
  
• Why it's time to Redesign HR
  
• How to Build The Irresistible Organization
• Learning to Be Yourself
• The Secret to Effective Motivation: Purpose
  
• The Myth of the Bell Curve
• What's Good for Women is Good for Business
• How to Make Work Fun
• Why we Do Need The HR Department
• Are Performance Appraisals Doomed?
• How I Hire: Trust Your Gut
• The MOOC Market Takes Off
• Employee Retention: The Time is Now
• The Datafication of HR
• Quantified Self: Meet the Quantified Employee