Regular compensation analyses can provide valuable insights to trucking company leaders looking to remain competitive in today’s job market. A successful analysis relies on a large number of data points collected from numerous sources. The quality of data that you select can significantly alter the quality of the results. Here are seven factors to consider when gathering and selecting data sources for your compensation analysis.
Before you start gathering or analyzing data for your market analytics, it’s important to establish the criteria you will use to validate data. This helps to ensure that all data collected will meet the criteria that fit your organization’s needs. There are five main traits that classify high-quality data — accuracy, completeness, reliability, relevance, and timeliness. If your data meets all of these criteria, it’s safe to assume that the data comes from a reliable and reputable source.
- Accuracy – Is the information correct?
- Completeness – Does the data tell the whole story? Is there information that is missing?
- Reliability – Does the data contradict other collected sources?
- Relevance – Is the data relevant to your company size, operating area, and niche?
- Timeliness – Is the information current or outdated?
Some sources of data are more trustworthy than others. The best quality data will come from government websites (like the Bureau of Labor Statistics), educational institutions, and industry trade associations. The more data you can collect from these types of sources, the more accurate your analysis will be. When selecting a source, ask yourself if the organization or entity has a direct benefit from the results of the data. If the answer is no, the information is more likely to be unbiased.
Old and outdated information can lead to incorrect assumptions during a compensation analysis, especially in situations where wages or unemployment are changing rapidly. Always verify the date that data was collected to ensure it is current. Never assume that nothing has changed since your last compensation analysis. Always take the time to refresh your data sources with each quarterly compensation analysis.
Also, be mindful of changing data sources as some organizations may use a slightly different methodology to calculate the same metric. For example, the Bureau of Labor Statistics might calculate the unemployment rate differently than a trade organization. This difference could cause variations in the results of your updated compensation analysis.
Raw data sets are best because they have been unaltered and less likely to be biased. Be cautious and scrutinize statistics and figures that don’t show the underlying data. It’s possible that the publisher selected certain criteria or excluded important data points to get the findings to match their desired result. This is often the case with free surveys where the origin of the data is unknown. Raw data sets allow your team to come up with their own conclusions.
Large data sets tend to be more accurate because they represent a larger population of data points. However, less can be more when it comes to data. Be cautious not to have so many data sets that it becomes difficult to analyze. This could slow down the analysis process or cause confusion. It also increases the chances of finding conflicting information.
Outliers in the data (both high and low) can skew the results of your analysis. There are a few methods to eliminate or reduce the impact of outliers. Most people will simply delete the outliers altogether. Other data analysts prefer to replace the value of the outlier with the mean or median of the entire data set. The approach is entirely up to your team, but make sure that you use the same approach for all data sets used in the analysis.
To conduct a comprehensive analysis, you will need to gather information from the market and your competitors. Hiring a consultant to conduct market condition surveys is a great starting point. You can also gather data from competitors’ job postings.
Some competitors may hide this information, however, this may become easier to validate in the future as some states are mandating wage and compensation transparency. For example, the State of California will require employers to start publishing pay ranges on all job postings starting in 2023. Other states that have similar rules include Colorado, Connecticut, Maryland, Nevada, Rhode Island, and Washington.
When comparing your compensation ranges to your competitors, make sure that you are comparing apples to apples. Validate that the pay ranges are appropriate for the job title and that there aren’t hidden factors like compensation combined with benefits.
At Inflection Poynt, we understand the importance of using high-quality data when performing a quarterly compensation analysis. By working with us, our clients can feel confident that they always have access to the latest and most accurate market data gathered from dozens of high-quality sources. Contact us today to see how your organization can benefit from our innovative compensation analysis platform.