What’s not Big Data to HR

There have been millions of articles on how HR would apply Big Data in operation. It brings endless possibilities to create fabulous future. However, when it comes to practice, the theories are not as good as they sound like. Human Resource professionals might get lost in this situation.
In this article, I am going explain what’s not Big Data to HR. Hoping it could help HR professionals to find some clues.


What’s NOT: Collecting Human Resources Data

Believe or not, every HR Generalist is required to master Excel functions. As a previous HR generalist, I was told to study Excel in-depth before launching HRIS system. Even after we had a decent HRIS system, Excel was still frequently used since we were asked to provide various data for analysis purpose.

Attendance, retention, training hours, engagement survey score, headcount, productivity, hiring hours, performance scores……

There is a long and endless list. But, this is not Big Data. Why? The answer is simple: Those data are not able to create value to the organization?

Could you answer below questions which the stakeholders care most with those data?

Say, are those data able to tell you how much expense is require to spend on human resources if the sales double?

Are those data able to tell you what kind of employees you should hire to support the new strategy?

Are those data able to tell you how well the new compensation system works to encourage productivity?

Hardly, right?

So what’s Big Data to HR.

To answer this question. You need to know what’s the problem you are facing.

One common talent problem, for example, may be sales productivity. What factors contribute to a predictable high-performing sales person? Every company would like to understand this better. And once you understand these characteristics, how can you better source, attract, and hire such people? Another may be turnover. What factors contribute to high turnover in your company and in particular groups?

These questions are worth millions of dollars to answer. A company I just met with developed a predictive analytics model for turnover in their restaurants. Did they use Hadoop or parallel databases? Nope, they used Excel. But they had a very smart statistician working with a very senior manager to come up with the hypothesis. And from there they explored all the possible data elements that might contribute to the answer.

Be careful you don’t start by only looking at data. It leads to lots of money spent, systems built, and often little or no return.

Over time your analytics platform will grow. But first focus on one or two key problems, so you can develop the credibility and skills to scale.