The Science of Compensation & Big Data is Sexy

Jessica Miller-Merrell, blogging4jobs

Uncovering the History of the HR Algorithm

The term HR algorithms was all the rage when Google announced it was turning to a mathematically sound and logical approach to analyze HR data. Google is renowned for blazing the trail of innovation, and if any company was going to crack the code of better understanding and even predicting employee trends and turnover, it was Google. The internet search engine giant announced it was inputting employee reviews, promotion histories and pay and other similar data into a mathematical algorithm in an effort to mitigate “brain drain,” the disengaged employee and turnover. That was May 2009. 

A flurry of internet activity quickly followed as analysts, bloggers and HR consultants authored articles and posts. Everyone in the industry collectively kept on eye on Google. Could they crack the code to improve and simplify the process and predict the seemingly unpredictable? It was exhilarating and scary all at once.

Since the flurry of activity 3 ½ years ago, little has surfaced about the success or failures experienced through Google’s turn to the algorithm answers. And, while the crickets don’t mean Google wasn’t successful (obviously, the search engine giant is strong and growing), they do mean that algorithms are not a one stop solution. The human element is still and always will be a critical component in any HR process. By strategically organizing and analyzing big data from your own company with algorithms, you can drive productivity and standardize salaries and pay bands with consistent job qualifications, geographic regions and job titles. These efficiencies will save you time and money and impact your company’s bottom line.

These efficiencies analyze, collect, and extract information from large quanities of information or big data. The term big data in HR evaluates reports, numbers, candidate applications—really any type of human capital information—to determine a correlation or related data that might intersect, enhance, or influence one another.  

Big Data is Sexy 

Where does one find, gather, and organize the data when it comes to big data? I liken big data in HR to my master bedroom closet. My closet, like big data, is a mess. Shoes are everywhere, clothes cover the floor, and I can't find a thing to wear. On the surface, big data is one hot mess. It's out of control and like my closet, I don't know where to begin. That's when it pays to call in the experts to organize, sort, and clean up your mess. Making your hot mess into a sexy and together look showing skin and giving you insights into data correlations like compensation planning in all the right places. 

When is it the right time to look at your company’s internal compensation structure? If you are asking the question, the answer is now. Your company should be continually examining internal data from turnover rates, promotions, performance reviews and salaries. This data should be examined alongside employee satisfaction surveys, turnover rates and productivity. External factors—looking at industry changes, pay scales, geographic factors and more—should be the third variable in examining HR big data. By continually pulling new and fresh data, versus historical data, you will have a better picture of the current state of your employees and can even predict turnover trends. 

How to Develop Algorithms with Big Data

Big data can be overwhelming and it can be easy to get stuck in auto-pilot mode with the regular demands of your job, and in responding to the next fire emergency. But, it's important to make time for it. By inputting big data and developing sexy algorithms to understand trends, reestablish pay scales and pay bands, you are establishing an environment of increased productivity and efficiency. Because the science of compensation is bringing sexy back. 

Jessica Miller-Merrell, SPHR is a workplace and technology strategist specializing in social media. She’s an author who writes at Blogging4Jobs. You can follow her on Twitter @blogging4jobs


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