Five HR Analytics Terms You Need to Know

I love big data. I love it for many reasons, but, as I’ve said before, one of the main reasons is the way it’s “raised the profile” of HR and its importance. The sheer volume of information HR analytics can bring to the table has moved HR practitioners from an “out of sight out of mind” back room business function, to a major player when it comes to company goal setting and overall planning. Today we use HR analytics for everything from determining passive and active candidates; assisting with onboarding, training, and engagement; and predicting retention, attrition, and performance rates.

That said, the sheer volume of data available today for HR professionals to work with can feel overwhelming, and at times, paralyzing. Not only do we have mountains of data to interpret, but the data is constantly evolving, shifting, forming, and reforming as we learn about the newest technologies, which actively measure even more employment-related functions.

The key to getting your arms around big data and analytics is to do your research and start to understand it. And to do that you must become familiar with its “lingo”.

Five HR Analytics Terms You Need to Know

Before 2011, if you Googled “data scientists jobs” you would be lucky to find more than a handful of listings. That has changed, dramatically. In fact, by 2015 the demand for data scientists had surpassed the demand for statisticians. But if you’re not lucky enough to have a data scientist on your team, fear not. Knowing the following five terms will lead you one step closer toward all the benefits big data and analytics has to offer.

Data mining. Try and wrap your head around this number: Around 2.5 quintillion bytes of data are created every day. Clearly, it can’t all be analyzed. There’s not enough manpower on earth to get that job done. And that’s where data mining comes in. Akin to “panning for gold,” data mining involves sifting through raw data, and finding where patterns emerge. Analysts convert those patterns into tangible information, which then allows for relatively accurate prediction making about real life behaviors or events.

Machine learning piggybacks nicely off of data mining, as it’s often used to make that mining job just a little easier. Just as it sounds, computers can “learn” from the data ingested, helping to translate the data into recognizable patterns. You will sometimes see the term Artificial Intelligence used instead of “machine learning,” as AI is what provides computers with the tools they need to absorb and sift through new information.

Hiring channel mix modeling. There are myriad channels available today for talent managers looking to recruit, including—but not limited to—advertising, employee references, recruitment consultants, and social media. Hiring channel mix modeling allows HR pros to make use of previous data on all of the hiring channels employed in the past, and clearly map out which ones were most useful, allowing for more efficient, streamlined human resources departments and optimized hiring expenditures.

Cost modeling. Cost modeling helps those in the C-Suite understand many HR-driven costs. These include hiring and onboarding costs, the time estimated for an employee to reach full productivity, salary and productivity ratios, overall productivity, and employee turnover costs.  Cost Modeling can provide a rich “dollar value” picture of hiring and retention plans for a given year, and allow you to quantify costs associated with certain activities and processes (like mistakes made in hiring, voluntary turnover, etc.) 

People analytics. Simply put, people analytics involves combining all of the employee data in your organization—and using that data to understand and help predict potential business problems, issues like sales productivity, retention, fraud, customer satisfaction, and more. It effectively helps measure the success of both human resources practices and learning and development programs, and eventually (as new apps are developed) will begin measuring the value of different roles, leaders, and other business investments.

While “gut instinct” is always a good thing to have, long gone are the days when that and a fancy resume were the only things helping HR practitioners make hiring and other decisions. Now, big data and analytics can help HR teams run in tandem with those from other key departments, as well as play a significant part in helping their organization achieve success when it comes to business goals and strategies.

What do you think? Are there other HR analytics terms that you’ve encountered? Has your company delved into big data and all its potential? I’d love to hear your thoughts.

A version of this post was first published on Converge.xyz

Photo Credit: hocmba@gmail.com via Compfight cc

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