Putting Big Data in the Driver’s Seat (Part 2)

 In Part 1 of this two-part series, I talked about the difference between a paper map and a GPS system. Both will get you where you need to go, but one is cumbersome and time-consuming to use, while the other guides you turn by turn to your destination with significantly less effort on your part. Used effectively, Big Data is the GPS system of the recruiting process.

Now that you have such a powerful tool at your disposal, the next thing to do is ask yourself: Where am I going? In the immortal words of that great sage of baseball, Yogi Berra, “If you don’t know where you’re going, you might not get there.” What seems at first like a ridiculous statement actually contains profound cautionary wisdom for those who want to use Big Data to their recruiting advantage. If you have not clearly thought out the end goal (destination) of your Big Data initiative, you are highly unlikely to arrive at any meaningful result.

So, what is your destination? This depends entirely on where your organization is going.  Are you opening a new factory? Has the company identified a gap in skills that must be quickly bridged? Have your workforce planners determined the number of new workers needed to meet current growth trends? With an understanding of the need of the hour for the organization, recruiters can then plot out their leg of the journey. That means knowing what data to start collecting so it can be analyzed.

For instance, do you need to increase the size of your workforce? You will need to factor in the historical attrition rate and the typical time-to-hire. How many other employers are also looking for these positions? What’s the unemployment rate for these positions?  What is the available pool of talent for these positions? Can these new employees work remotely? What’s the hiring funnel for this position historically? Are there current employees on-site who can do the job? What sources have worked in the past? These are the kinds of various data you’ll need. Delving into data and connecting previously unconnected data sets enables you to find trends, which enables you to predict how to best attract and select your candidates.

Remember, “Big Data” for recruiting is scaled to the size and needs of the organization. We’re not talking about billions of records here; rather, it’s a process of gathering data from all your disparate HR systems so you can see connections you could not have otherwise seen and make fact-based decisions.

Let’s revisit the IBM example from Part 1 in a bit more depth now. Big Data can be put to work to help determine how many employees are needed now and forecast how many will be needed in the future, and that’s what IBM recently did. The company created a Big Data mash-up of internal data from two sources: its HR database and its global services billing database. IBM’s Big Data consultants were able to detect dozens of patterns that would not have been detectable otherwise. This helped IBM forecast shortfalls in critical talent areas so that they could create a strategy to meet projected revenue growth. Consequently, they began the process of training or hiring the talent needed to fill projected gaps. IBM also could see via this analysis where they had talent overages and trimmed accordingly.

IBM offers here a clear example of Big Data-driven predictive analytics. The analysis of its data enabled IBM to predict the number of new workers it would need in a key area, and the company then devised a strategic plan to meet that need. While it’s important to note that any kind of predicting inherently contains the possibility of being wrong, it’s equally important to realize that predictions based on sound data analysis are far more likely to lead to good outcomes than those based on assumption, tradition or gut feelings. The map doesn’t know that the bridge close to your destination is washed out; the GPS knows, plans an alternate route and factors in traffic patterns to calculate how long it will probably take to arrive.

It’s clear from the data sources it chose to analyze that IBM had a clear destination in mind. In effect, the company typed in the “address” of its destination into its Big Data GPS: find out what people are buying and if we have enough talent to meet demand. Turn by turn, analysis of its data led IBM to its destination. Big Data can do the same for many recruiting destinations. Pick the one that’s right for your organization and use Big Data to steer you toward the results you need.