Aging Is for Wine & Cheese, Not Salary Data

The market is shifting more rapidly than ever before, and with this comes the pressing need to keep up with compensation data. Even data that is only six months old may not necessarily provide an accurate picture of the current trends within your industry or near your location. Missing out on cutting-edge methods puts you at risk of making ill-informed decisions for your business. No company wants to admit that their comp strategy may be lagging behind, but it is becoming more and more apparent that some changes may need to take place in order to keep up with the pace of the market.

Modern Technology To Sourcing Salary Data

One area of Human Capital Management that’s made major strides in technology recently is in compensation management. Now’s a great time to reevaluate how modern your approach is to sourcing salary data.  Here are a few questions to ponder to help you get started.

How old is the data that I am working with?

The traditional way to gather market data about compensation is to buy an annual survey from a consulting firm. These surveys typically take anywhere from three to six months to complete, and are often only updated annually. Needless to say this creates a bit of a gap between when data is collected and when data is delivered to customers. When the results finally make their way around to you, the data has been artificially “aged” by compensation professionals to bring it up to speed with the current market rates. Does this produce the most accurate salary data? Probably not.

Consider shopping around for market data collected using a more modern approach, where data is collected more broadly and regularly, with no need to age the data or make other adjustments. An additional consideration that is often overlooked is the relevance of the data in terms of employee location. Typically, surveys are only conducted from major metropolitan areas, therefore traditional salary reports are not likely to give you a complete picture of how you should be compensating your employees if you need market data for a locale that’s not a major city. You may need to consider using information that has been gathered in closer proximity to your employee’s location.

Is the data relevant to my location?

The basic way of conducting traditional surveys involves selecting large companies in major geographic locations to provide information about their employees. This strategy is useful when it comes to gathering information for companies located around urban locations. The city-oriented surveying creates a lot of extra work for HR professionals in other locales. You must adjust the data you receive to meet your unique needs, such as differences in cost of living and available talent in a rural versus a metropolitan location.

Companies using modern collection techniques are paving the way when it comes to creating better strategies of collecting compensation data. Online surveys can be conducted from all locations, covering a broad range of geographical areas while at the same times pinpointing accurate data points. For companies located in rural areas, this means they are more likely to be able to benchmark against accurate salary matches and won’t inadvertently underpay or overpay employees.

Are the right skills represented in the data?

Depending on the product your company produces and sells, you may require employees to have an unusual mash up of skills, knowledge, and capabilities. A traditional compensation report may have information about pay variances, different years of experience, certifications, and skills; but never in combination. If the employee’s assemblage of qualities is considered unusual, it is likely that they will not be covered in a traditional report, or at least not in with the combination you need.

Companies that use data from a modern collection method don’t need to make adjustments for combinations of skills, locations, abilities and more, simply because they draw from a larger pool of entries which allows for the inclusion of a greater amount of data points. The combined effect of all the compensatory factors is already inherent in the data. A large base of input can be useful when seeking out unusual qualities, such as a special security clearance, highly technical education, or managerial experience.

When making your compensation calculations in a more traditional fashion, you need to take in to consideration how many variables you want to include. If you decide that you want to include more variables, then your salary decisions will start to become more competitive. Keep in mind that the consideration of more items means that you will need to spend more on salary calculations. Calculating salaries from a traditional survey can consume a lot of your time and resources. You must sort through job titles like “Applications Developer I” or “Applications Developer II” and then find the job description that best fits your employees. If the employee has qualities found in several different job titles, you must then do some calculations to blend the data from several positions to come up with a base salary number.

While all these factors make an excellent case for making the switch to providers who use online surveys, the most important reason that you should reevaluate your salary data source is competitive advantage. In this volatile market, getting pay right to compete for talent is not easy, yet is crucial to your business. Certainly there are still benefits to using traditional survey methods but when it comes down to it, aged data is out and fresh data is in.

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Karaka Leslie

Karaka Leslie is one of the Product Managers at PayScale, a compensation data and software company. Karaka started her career as an agency recruiter, helping companies find the best talent. Since then, she has worked with numerous businesses to assist them with building a solid case for good compensation planning. Outside of compensation, she also founded a Community Giving group to help connect professionals to local community organizations focused on education and career services.


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