Experts estimate that poor data quality costs companies an average of $8 million per year, in addition to other non-monetary consequences. And even when purchasing the highest quality surveys available, errors in computing or analysis can result in subpar data that can hurt your pay structure creation.

Relying on Crunch Data

Current standards for acquiring market data involve working with a survey vendor who requests survey submissions from client companies. This information includes extensive documents with variables such as employee salary, bonuses, compensation and benefits. While combining this information from many different companies in the industry provides benchmarks and market prices, the more than 60,000 records that are analyzed at a time leave ample room for mistakes.

This remains the most effective way to gather huge amounts of data, even though the sheer volume comes with inherent risks. The best way to check for accuracy is to utilize additional software that can cross check information and examine data to piece out details that can be overlooked when accumulating large survey responses.

Varying Job Descriptions

Surveys that clump jobs too broadly together will provide misleading information. Questions that relate to ‘accountant,’ for instance, could get results on high level financial advisors together with glorified bookkeepers. Without looking out for these nuanced details, the data for that entire position will be too varied, leading to ineffective pay structures.

Over-Parsing Data

Just as answers relating to jobs that are too broad can provide misleading information, crystallizing data to the finest granularity with too many stipulations and filters gives you insufficient data to work with. Sample sizes that have been sliced and diced do not provide an accurate picture of the industry, and may be excluding responses based on largely irrelevant factors. Consider the importance of each stipulation and choose only the factors that will be most useful to you, disregarding filters such as age and degree if mostly unnecessary for the position.

Negative Impacts

Incorrect salary information affects: workforce morale, industry compliance, customer attrition, and undermines your company’s reputation. Suspicious numbers cause confusion and leave employees mistrustful of the business in other areas as well. And potential new hires are scared away by inconsistent practices and varying offers.

Poor information quality is a problem for more than just HR executives. Lack of data consistency causes waste in business processes and undermines customer service efforts as well. But as compensation affects every single level of the business, these decisions carry consequences that reach even further.

Don’t leave the quality of your compensation analysis up to chance. MarketPay’s globally recognized software has safeguards built in to help your business flush out the inconsistencies, building a better business into the very heart of your compensation structures.

SOURCE: //data-informed.com/dollars-depend-on-data-quality-with-compensation-analytics/