The start of any year is the time to focus on trends. Predictive analytics is one of the trends that looks “hot” for 2012 in the world of human resources measurement. The promise of predictive analytics is enormous. For example if we look at turnover and were able to predict how much turnover was coming from where, and when, it would allow us to focus our recruiting resources far more effectively, reduce vacancy rates and better support the business. The ability to predict outcomes brings to HR the opportunity to be proactive and demonstrate the value that can come from well run people systems and processes.
Strategy is a future-oriented view of the organization and the ability to deliver predictive insight brings HR ever deeper into the strategic realm of the organization. However, as with many such trends, there is a lot of focus on the concept and the promise, and less on the mechanics of how you actually make it happen. I have heard and read a few thought pieces recently that are asking for good predictive numbers and decrying the more standard HR measures, such as turnover, because they are not predictive. This is not true. The ability to predict comes from having robust and deep historical data. There is no shortcut to predictive measures. Prediction relies on history, and without history, any predictive attempt will be shallow at best, and more likely wrong.
The value in predicting turnover is clear. Hiring resources are scaled to needs and are therefore efficient; key roles are empty for shorter periods of time, reducing productivity losses; panic hiring is reduced based on more realistic expectations, leading to potentially better hires. The question becomes, how do you know what your turnover might be without knowing what your turnover is now?
There are some good emerging models which look at several factors relating to each employee, such as length of employee commute, time since last role change, performance ranking overtime, etc., which are demonstrating good predictive function for voluntary turnover. These models have come from years of study of actual employee behaviour, and for them to work in each specific organization, they rely on being tested and refined for that specific context. For example, a commute of 30 minutes maybe too long for employees. In other geographies, a commute of 45 minutes may prove to be too long. The factor (i.e., the commute) is a valid factor, however, without understanding the history of how this factor plays out in your organization, any prediction is at best a guess.
This is our message for the start of 2012: prediction is a worthy goal. It can offer incredible value and should be a goal for any HR person or department looking to continue its shift into the strategic realm of business. However, it is not possible to jump from no measurements to predictive measurements. It is also not correct to decry the measures of the past. Yes, they only tell you what has happened; however, prediction is best done by understanding the patterns of the past and projecting them into the future. Without this deep historical picture, there is no ability to predict. Therefore, if you want to pursue the goal of prediction, your first step is to build your historical set of data. Without it your pursuit of prediction will have no future.
Ian J. Cook
- HR analytics process: Ask better questions - January 9, 2013
- People analytics for business: In high heels and backwards - December 12, 2012
- The winds of change: Making HR measurement happen - November 13, 2012
Ian J Cook says
you make a useful and valuable insight. One challenge with the in-house analytics are that they only tell you how your organization behaves. This can be useful – or it can be a disctraction.
We produce HR benchmarks on a quarterly basis – so there is a lag time but no longer than 8 weeks after the quarter is done. Anyone I have talked to who provides in-house analytics is always being asked for the comparative detail. Similarly in providing comparison data people often then want us to come in and deliver detailed drill downs in their own system.
All of this said the number is never the answer. Any predictive approach whether it be in marketing, finance or HR requires considerable thought, careful tracking of context and constant scepticism that past results will demonstrate future results. However uncertain this state is, it is ten times better than guessing.
Eric Patki says
Thanks Ian for sharing your thoughts.
Predictions about what future will unfold is old human curiosity, more so now it is buzzing in HR for past couple years as technology companies started to see fresh new pastures in this arena. I recently heard that T-Mobile has implemented SAP Workforce Analytics that uses cutting-edge HANA (in-memory database technology by SAP) in HR to have real-time reports so no need of waiting for HR to generate periodic HR Metrics – it is available to executives on their dashboard in real-time.
Talks of predictive HR metrics are everywhere these days and there is great merit in appropriately harnessing this tool. However, predictions based on patterns of past data could be illusive at times.
We must factor in errors that happen while reading patterns from historical data and relating them to new environment particularly in light of changed circumstances and realities of your situation. For example, if an organization is located far from labour markets and time to fill certain technical roles is longer than its industry peers that are mostly closer to lobour market, the metrics is not going to be showing real picture thus undermine HR’s actual efforts! Also, if the whole region is going through changes in demographics during a certain period (pool of available talent for a certain skills shrinking), it will give a distorted picture for comparison. Predictive power remains in HR Metrics that is based on sound judgment and comprehensive relevant data. It needs to be supplemented by compelling storytelling at right places and at right times. This will result in moving targeted HR programs that bring value to employees and organization.
Ian J Cook says
Hi Bruce – interesting comment. If your data shows random events then HR is having no effect and we know we need to change. Better to live with knowledge than to throw money away blindly – even if the answer is not what you were looking for.
I have seen plenty of academic research, that is peer reviewed and rigorous which demonstrates that HR makes an enormous difference. The reason so much funding is going into this area is because businesses are sourcing and finding their own value creating insights. You might enjoy reading this paper from the McKinsey Quarterly. Several examples of how HR data has driven significant value creation.
What if your “robust and deep historical data” merely depicts random events? Is HR Analytics looking for patters where none exist?