Metrics, analytics, predictive analytics and big data are all phrases that are used on a daily basis regarding measurement in HR. This issue for HR professionals today is, what do they all mean and how do we make sense of it all?
First, let’s set some context by defining a few terms:
Metrics are simply measurements. For example “Our average engagement level is 80%,” “Our annual turnover is 50%,” “Our average performance score is 60%.”
Metrics track activity, but don’t necessarily show a causal relationship. Metrics alone do not show what affects engagement, what causes turnover and what drives performance.
Human capital analytics examine the effect of HR metrics on organizational performance. In more general terms, analytics look for patterns of similarity between metrics.
For example, do high performers leave at a higher rate than low performers and if so, what is driving that turnover? Statistical tests are necessary to get to those answers through analytics.
By using analytics over time, you can become predictive. In other words, you can use data you probably already have to discover answers to questions like these:
- Which of my top performers are at risk for leaving the organization in the next year?
- Which HR initiatives (I.e. training programs, reward and recognition programs) will most impact the bottom line?
- Who will be the most successful employee in our organization?
Big data refers to a collection of data sets that are so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. It’s easy to see how in larger organizations with hundreds of thousands of employees and multiple HR data points how HR can find itself with a big data issue.
With all of this said where are most HR departments today in this measurement journey?
5 phases of the HR analytics journey
The journey to HR Analytics has been a long one for HR, with most companies just starting. Below are the phases of a typical analytics journey: (adapted from Steve Woolwine, Chief of Staff Talent and Human Capital Services, Sears Holding Group)
1) Justification — in this initial phase, HR metrics are tracked and have limited reporting. No actions are taken at this stage and data is still quite dispersed.
2) Measurement — in this phase, metrics are better defined. Reporting is now in the form of a dashboard/scorecard, and leadership may have some accountability to the metrics.
3) Effectiveness — at this stage, HR has more sophisticated technology and leadership is widely held accountable for results. Actions are beginning to take place because of the data, and KPI’s are tied to results. Analytics are now being discussed.
4) Value Creation — at this stage, decisions are being made based on analytics, and genuine insights are created. Predictive modeling begins here with an eye on future value creation from HR investments.
5) Impact — this is where the strategic HR professionals really want to inspire the company to be. At the impact phase, change is being created as a result of a predictive mindset, strategic goals are being achieved, and the culture has shifted from being performance based to analytics driven.
Your HR data is waiting to be analyzed
In my experience, when asked where most companies are in their metrics journey, phases 1 and 2 are the most common answers. Of course, there are pockets of analytics excellence from companies like Google, Sears, Well Fargo and others, but many HR Departments seem to be “stuck” in the early phases of the journey.
Getting unstuck requires a measurement focus that has a goal of understanding which HR activities impact the bottom line and which employee knowledge, skills and behaviors impact the bottom line.
The demand for data in HR has never been greater. The C-Suite’s desire to make data-based decisions that reduce risk is here to stay. The ability to gain a competitive advantage through our people is one that HR can impact by using data that is just waiting to be analyzed.
Start somewhere, solve a business problem using data…turn that data into a strategic HR weapon.
What are your thoughts on the state of HR analytics today?