This article was updated on July 16, 2018.
Money matters. While other forms of compensation — promotions with more autonomy and responsibility, health-related perks and a solid work-life balance — make a difference in the decision of current employees to stay the course or new hires to sign on, salary often remains the go-to reason for dissatisfaction for both employees and employers. Employees want to know they're valued and HR doesn't want to overpay. But how do HR departments meet the challenge of benchmarking compensation? It can start with big data. Here's a look at what you need, how to use it and what the future holds for the future of compensation competition.
Setting the Stage
Before you roll out any big data solution, it's critical to address the issue of reasonable use. As noted by Entrepreneur, there's already concern over firms using health data to predict potential employee medical issues and theoretically use this data to terminate "at risk" workers before they cost big money in health premiums. While this may never come to pass, the perception of diabolical big data use is enough cause for concern. As a result, HR departments need to be transparent about any data collection effort — what data is being collected, how is it being used and how is it being anonymized to limit the chance of a privacy breach.
When it comes to sourcing compensation data, you'll need details about all in-house salaries and how they ramp up over time. Better to be upfront about this policy rather than waiting for staff to address the matter themselves.
So what's the trouble for organizations that hope to benefit from benchmarking compensation? Missing data can be the biggest problem, but according to Forbes, it isn't so much down to the numbers of who earns what and why, but rather the "deep data" that connects isolated information points. For example, Forbes notes that while standard big data analysis suggests that employees who use the bathroom more than four times a day are more likely to quit their job than similar, restroom-restrictive colleagues, deeper analysis using industrial-organizational (I/O) psychology suggests that stress is the root cause of both employee turnover and frequent bathroom trips.
How does this apply to the HR compensation scenario? It means numbers in isolation often aren't enough. What type of experience do particular compensation levels demand, how are they related to the overall culture of the corporation and how much room is left at the top-end? Salary ranges on their own are useful but incomplete since you're not just hiring a number but an individual who may significantly outperform the basic value of their salary — deep data can help you single out these top performers and ensure they're given ideal compensation.
As noted by the Society for Human Resource Management, it's no longer enough for HR managers to simply leverage metrics such as cost-per-hire or time-to-fill. Instead, HR leaders are tasked with analyzing meta-metrics such as return on workforce investment and specific opportunity costs tied to workforce processes. Fair compensation is the foundation for any of these advanced analytics — if current employees and future prospects feel they aren't paid fairly, it doesn't take an in-depth I/O study to see where they're headed.
The result? Benchmarking compensation demands the right data. Right now this could mean using existing company salaries as a baseline and then crawling the web for other offerings. This could take the form of competition job posts, discussions on social media or conversations with prospective hires who have been through multiple interview processes. But if HR can instead rely on a centralized data system from a vendor that can also provide data to benchmark across industries, they can track and compare this data in real-time to provide salary information on a per-job basis. They can then leave behind the guesswork and move forward with data-driven and actionable intelligence.
In the future, HR leaders should also begin to consider the role of social trends and sentiment analysis. For an example, take a look at the rapidly-growing need for InfoSec professionals. The market supply can't keep up with employer demands, making these IT experts a hot commodity. Organizations ahead of the game will benchmark higher to bring in great talent, and then use trend and social data to help ensure new InfoSec pros are given the compensation they deserve. In other words, benchmarking compensation isn't always about hitting the right number but rather finding the right data and using it to inform salary policies that put your company on the leading edge of your industry.
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