Awash in Data

Our Blogswap guest post today comes from Frank Mulligan of recruit-china.com. He presents us with some common sense thinking on data and metrics. Thanks, Frank!

One of the more useful elements of service offerings like Exit Interviews is the hiring metrics that repeated, measured operations will generate.

But which hiring metrics?

We can always look to the net for information but checking out articles on the internet is not necessarily of much help to us here. Over the past 4-5 years I have noticed a distinct bias in articles on the issue of metrics, and little agreement. Different individuals or organizations emphasize different approaches to metrics and there is a strong tendency to emphasize one metric over another. In some cases it seems to be because it fits the author's business model. In other cases it seems to be a desire to evangelize a particular metric, or respond to a competing article.

As someone who has a need for these metrics I just carried on regardless, pinching a bit from here, and grabbing something else from there. The different metrics that I discovered along the way each taught me something different. Some taught me the most important lesson, and that is the limitation of metrics.

So in the end I altered my approach.

What I have learned is that when the price of capturing a metric approaches zero all data is equally valid. I thought this many years ago as well but expressed it as ‘just grab everything and let’s see what we can do with it.' Once you have the data you can turn it into information or knowledge later but if you don’t capture it you cannot do anything.

Data Sources

To reiterate, you can’t have too much data. Information and knowledge are a different thing. Fortunately, most Applicant Tracking Systems (ATS) or Talent Management Systems (TMS) capture data as part and parcel of the way they work. For example, when a recruiter sets up a job requisition the date and time is captured. Equally when that same recruiter screens the first candidate the date and time is captured again.

So immediately you have the basis of a metric called Time to Screen. You may not see a need for such a metric but the data for it is there anyway. And the price? Close to zero. (For offline systems data is difficult to capture and does not comply with the ‘all data is free’ rule. That’s a pity but given that there are free ATSs online it is no excuse for inactivity.)

For other, more useful metrics, the process is much the same, and the cost is equally low. All that is needed is that you export the information to a spreadsheet and create the necessary graphs. If you feel a little nervous at the prospect of pushing your knowledge of Excel to the limit check out Statistical Analysis with Excel for Dummies. Most of the tools that you need for statistical work are already available in Excel.

The good news is that having the data that you need does not in any way constrain the way you work with it. You are in the fortunate position of knowing what your final outcome is supposed to be. With this final outcome in mind it shouldn’t be so difficult to take the data you have and make a start towards turning it into the kind of metrics that you can use to drive your business.

If you are in a cost-down, high-volume, mass production environment then Time to Hire might be a good place to start. Equally if you are unsure of the efficacy or diligence of your recruiter you might consider the Time to Screen metric cited above. Or you could, and probably will, create your own metrics.

This is only the start and you still have more work to do of course. Your end-game solution requires you to iterate, check the metric, modify actions and behaviors, and repeat the whole hiring process. Once you start on this path the only thing that you can do is learn, and learn, and learn. That can’t be a bad thing. So dive in the deep end.

The alternative is to let all that free data to go to waste.

Copyright 2006. Frank Mulligan.