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People Analytics: 10 Things You Need to Know

People Analytics: 10 Things You Need to Know

David Green, a People Analytics leader, award winning writer and speaker regularly posts the “Best HR Analytics” articles. Below is an excerpt by Josh Bersin, of Bersin by Deloitte.

I’ve been studying the use of analytics in HR and L&D for almost 20 years, and it has been a fun but frustrating space. While many have believed in data as a critical part of HR and L&D, most of the conferences I attended were filled with analysts, statisticians, and passionate professionals who struggled for resources and support.

Well, all that has now changed, and today “People Analytics” as a business discipline has arrived. Our research now shows tremendous growth in this market, and a significant shift away from measuring HR toward a real focus on using people data to understand and predict business performance.

To help you understand this change, let me share ten things driving this marketplace.

  1. Employee retention and engagement has become a high priority issue, driving the need to understand what drives the employee experience.

As critical roles in business become more competitive, the competition for strategic talent is intense. Our research (Deloitte Human Capital Trends 2016) showed that 86% of business leaders are deeply concerned about retention and engagement, 89% about leadership, and more than 84% about current workforce skills. And thanks to tools like LinkedIn, Glassdoor, and others, skilled professionals can find jobs easier than ever (the average worker now changes jobs every 4.4 years).

Given this challenge, coupled with shifts in the workplace toward younger and more diverse teams, companies are searching for ways to build a highly engaged workplace. It takes deep levels of analytics (plus a lot of local data) to understand why some people love their jobs, others want to leave, and some are on the fence.

  1. Organizations are redesigning themselves around teams, and need data to understand how people best work together.

The second finding from our research is the astounding fact that 92% of companies believe they are not optimally organized for success.

Why? Today, driven by digital technology, business moves too fast and people want to work in more dynamic teams. We work in a variety of teams and these teams change often. Some are customer projects, others are internal programs, and others are simply special assignments.

Today’s HR software gives us almost no visibility into these teams (this is changing), so we have to analyze relationships and use what’s called Organizational Network Analysis (ONA) to understand how work gets done.

ONA, a relatively arcane discipline used by organizational design experts, is now becoming mainstream. And guess who makes sense of the data? People analytics teams.

Research by Rob Cross, a leading researcher in ONA, found for example that highly connected people are among the least engaged in a company. So some of your most valued staff, the ones everyone talks with regularly, are often buried in the organization and are both underappreciated and over-worked. Only analytics will explain this to you.

  1. The People Analytics function now has a clear mission.

For many years, the terms “People Analytics,” “HR Analytics,” “Talent Analytics,” and “Workforce Analytics” have all been kicked around. These changing labels have been confusing, making it unclear what we are trying to do.

Well today we have more or less arrived: People Analytics means bringing together all the people data in the company to understand and address specific business problems: sales productivity, retention, fraud, customer satisfaction, etc. More broadly, it means understanding all the employee data you have and its impact on business performance.

There are quite a few implications of this redefinition of the role:

  • First we have to consolidate HR and people data in a meaningful way. This means bringing together all the system of record data and creating a data dictionary so we can accurately and reliably answer questions like “what is our turnover rate” and “how many contractors do we have?”
  • We have to look at many forms of HR data: tenure, salary history, job mobility, location, training history, performance rating, and more – which means we have to pull data from many different systems.
  • We have to consider psychological data like engagement, mood, and surveys. We need to look at organizational network data, data about the organization itself (who reports to whom), and understand the role of structure, location, and team size
  • We need to look at external data (or data collected during recruitment) like job history, schooling, experience, and educational history.
  • We have to be ready to look at new sources of data like location, travel schedule, commute time, and now even fitness, heartbeat, and more.

This means the people analytics function has to build an open and scalable infrastructure, and become very good at understanding data of many dimensions. The days of focusing primarily on building a retention model are coming to an end. People Analytics teams quickly find that employee retention is really a surrogate for dozens or hundreds of other issues: management, leadership, work environment, rewards, job fit, and more. And we now have retention predictors built into most core HR platforms.

  1. The People Analytics profession is now growing rapidly. Staff skills and expertise are available.

Our research (Deloitte Global Human Capital Trends 2016) showed that the maturity, investment, and skills of People Analytics in HR has rocketed ahead.

Today we have reached a point where many companies now have cloud-based HR platforms and they can access reliable data in a relatively easy way.

  1. Data Management still remains a challenge.

I’ve talked with many dozens of companies in this area, and almost all agree that their HR data is “bad.” HR and people data is often inconsistent, unclean (not correct), out of date, and located in many places. Many large companies still don’t even know how many salaried or contract employees they have at a given time, so these teams are dealing with a big data integration problem.

  1. Predictive modeling is becoming common, but really learning how to apply this information is very tricky.

We analysts 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. Some of the most difficult challenges in People Analytics is implementing the changes recommended by the model. This means your analytics team must be surrounded with good change, HR, and leadership consultants.

One might think we can take useful data and simply share it with business leaders to get results. In fact, this can be very risky. Let me share an example:

Imagine you have a model that predicts leadership and you tell a manager that he has someone who is a HIPO (“high potential”). Will that manager now act biased toward this person and assume everything they do is right? What happens to the other people in the team who may also be management potential?

While People Analytics models are useful and often predictive, they are never 100% right in all cases. Think about using people analytics data like the “ice warning” light in your car during cold days – it tells us to be vigilant, slow down, and watch for problems. But it should never be used as a signal to “do something immediately.”

  1. Tools and platforms are here

As with all hot new markets, there are dozens of new tools, technologies, and platforms available to help you analyze people data.

Seek out tools and platforms that are easy to use, extensible in their design, and can easily accommodate many forms of data. Remember that no vendor will give you everything you need, so look for a “toolset” you can use for many purposes.

  1. People Analytics is not a small, central team any more: it must extend into the organization.

When I started studying analytics, I would often find one person in the company who had this job. Today the People Analytics mission is so big and broad that you have to think of it as a Center of Excellence. It is both a central group that understands data management, statistics, visualization, and reporting – and also a set of embedded business partners who can help line managers learn to use the insights you find.

The real discipline of People Analytics is multi-disciplinary. Successful teams include process people, consultants, OD experts, I/O psychologists, visual designers, as well as core IT professionals. You’ll need all these skilled people to work together to drive results, and I believe they should report to a senior executive (CHRO or head of operations), not be IT or HR Technology.

  1. Build an open, extensive architecture to be ready for growth.

We have to think of People Analytics in an expansive way. The days of simply analyzing payroll, HRMS, and time and attendance data are over.

We really should look at employee engagement survey data, email history data, employee location and sociometric and all the data which will come from wearable devices. You have to think broadly in your analytics architecture. The more sources of data you have, the more likely you are to find the right solution.

  1. Security, privacy, and confidentiality is now a critical issue.

Finally, we have to put security and confidentiality on the front burner. Our research seems to show that most employees (72% according to Conference Board) understand that their employer is capturing data about their activities at work. The real issue you face is how can you manage this data carefully and securely, without alarming employees or committing a breach of trust?

My discussions tell me three things are important:

  • First, tell people that any data you collect will be confidential and only used to help make their work life better. Let them “opt-in” wherever possible and don’t give employee-activity data to managers without very careful thought.
  • Second, make sure your data team is well trained in the issues of Personally Identifiable Data and you have security measures in place to make sure the data stays private.
  • Third, avoid using “anonymous” data in any feedback or analysis you do. While anonymity may make some sense on the public internet (although it is becoming less common every day), it only gets you in trouble at work. This tells people that when they fill out a survey or make a comment to someone else, the data is kept confidential but you do know where it comes from. Again this stresses the ethics and expertise of your internal team, but it’s the right thing to do.

People Analytics has finally become one of the most exciting trends in business, one which all companies can adopt.

Josh Bersin’s article can be accessed in its entirety on David Green’s blog post – https://www.linkedin.com/pulse/30-best-hr-analytics-articles-2016-david-green

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