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Compensation and Big Data – The Targeted Merit Budget

Big Data Merit Pay BudgetBig Data is delivering a more intelligent approach to merit budgets, but with a caveat.

Big Data is just that, Big and general, and far from perfect.

At one time or another, we’ve all been victims of computer algorithms that judge us unworthy of credit, or direct marketing to us based on algorithms that really don’t apply.

The same can happen if we set our merit budgets—using analytics– at the granular level. That’s why it’s important, once you’re using more Big Data, to leave some room for manager judgment.

 

Compensation and Big Data – Bringing Science to Pay

It’s a common belief in HR that compensation is both art and science. With the introduction of Big Data and data visualization, the art portion is becoming a much smaller part of the equation. This trend will profoundly impact the comp analyst’s job.

Say Goodbye to One Size Fits All

Here’s how merit budget presentations used to go:

“OK, after all of this beautiful data we’ve shown you, we recommend a merit budget of 3.25% across the board.”

Umm, no. You can no longer spread merit budgets evenly with a “peanut butter” tactic. The emerging focus on analytics and big data, along with more sophisticated technology, means that multiple solutions for merit budgets and other compensation elements will become the norm.

Technology is making it progressively easier to use analytics to evaluate relationships in your external and internal data. You’ll finally be able to confirm or disprove long held beliefs about the interaction of pay with other factors. One of the inevitable results of all of this will be merit budgets that vary significantly across the organization.

Takeaway: Don’t stop at a beautiful, intuitive presentation of data and conclusions. Follow up with targeted recommendations that make use of those conclusions.

Be Smart in How You Use Your Compensation Research

Most compensation plans do not give a manager a higher merit budget just because s/he has a performance distribution skewed toward higher ratings. In fact, you’re probably chuckling at the very idea. We don’t trust the managers not to manipulate the system.

But, what if you had other measures of performance that could not be so easily manipulated and you knew they positively influenced performance?

Would you use them in the creation of merit budgets? With the advent of big data analysis, that situation is very likely to arise.

If you found, for example, that merit increase size influences turnover among high-performing employees with five to ten years of experience, would you be willing to give managers of those employees a higher merit budget?

And, just as important, give less to the managers whose people were not in that category?

 

A Simple Compensation Example

Take the simple example of displaying the position to market of different job families. If you present the data in a bar chart, before and after the “all for one” merit budget, the chart will actually change very little.

The jobs significantly below market will stay significantly below market. Internal relationships among jobs will remain the same.

 

Current Position to Market

(PTM) By Job Family


PTM After “All For One” Merit Budget


PTM After Targeted Merit Budget
(Targeted by Job Family)

 

 

Merit based on performance ratings will help move salaries faster for the top performers, but the difference is often so small that it really doesn’t affect the outcome.

Takeaway: Let the data tell you the differences and use that information to develop a targeted merit budget to better align your PTM.