The Importance of “Merit” and Other People Data in Workforce Analytics

Data has become central to modern HR decision-making—from hiring and pay to promotions and separations. A data-driven approach reduces reliance on gut instinct, helps reveal meaningful patterns, and allows employers to clearly demonstrate how decisions were made.

With the current focus on merit-based decision-making, organizations may also need to show that employment decisions were grounded in objective, job-related factors. While identifying and maintaining the right data can be challenging, certain merit-based datapoints consistently provide the greatest value. This blog highlights the key areas where employee data matters most and outlines four critical datapoints that can strengthen HR decision-making.

Four Key Types of Merit-based Employee Data

1. Performance Data: There are countless types of data that reflect employee performance. There are quantifiable employee outputs (e.g., sales volume, units produced, error rates, project efficiency) that are highly objective and directly tied to the employee’s individual performance. There are also other measures of performance for less quantifiable employee behaviors, such as their effectiveness in supervising employees or in contributing novel ideas to a given organizational priority, such as the development of a new product. These types of performance data are often collected in performance appraisal forms completed by supervisors or managers.
   Potential Uses: Performance data is often used in making promotional and pay decisions. To the extent the performance evaluations are tied to critical job duties of an employee’s current role, they can serve as a solid justification for pay decisions because the employer can use the performance data to distinguish similarly situated employees, and corresponding pay differences, from one another. For talent management decisions like promotions, performance appraisal data that shows the employee’s readiness for the next level can be useful in supporting the validity of promotional decisions.
2. Previous External Experience: Employers can track both the quantity (years in previous jobs), level (E.g., individual contributor, management) and nature of work (e.g., job functions, content domains, etc.) that might justify why a given candidate was selected and their starting salary set at a particular level. Often starting pay decisions for new hires involve information about the candidate’s previous experience, particularly for management levels and above where applicants are joining the organization late in their career with significant prior experience.
   Potential Uses: When these data are not tracked and maintained, the employee may show up as an outlier in a pay equity analysis because their compensation is either overly high or low compared to their peers. With the data to support their pay decisions, employers are better able to evaluate whether an employee who appears to be an outlier is actually being paid fairly if external experience is taken into consideration.
3. Previous Internal Experience: Just as employers can use external experience data, internal experience within the organization, such as tenure, time in role, or experience on special or difficult projects/assignments, can also be helpful. Evaluating internal experience can speak to the ability of certain employees to take on new roles (promotions) or the need to provide higher pay than other employees because they can perform unique tasks.
   Potential Uses: Internal experience can make employees more suited to perform certain jobs, as special assignments or experience in certain departments/functions/units within the organization can help employees develop new skills and institutional knowledge. These new skills and knowledge may be considered when making both pay decisions and promotion decisions. Not only can they help explain historical decisions that have been made, but the data can be leveraged to identify employees who would be good candidates for open roles within the organization.
4. Education, Special/Unique Skills, Certifications, Licenses, and Training: Education degrees (both field of study and level) and special skills that make an employee particularly well-suited to perform a specific job may play a critical role in hiring, promotion, and pay decisions. However, because the information is often used as a final decision point to distinguish between only a handful of applicants or employees, the data are not often documented or tracked well. Despite their potential limited frequency, the importance of these data is notable. Some of these special or unique skills and training may include, but are not limited to, bachelors/masters/doctorate degrees, bi/multi-lingual, certifications (e.g., CPA, SHRM-SCP, or SQL programming language), expertise in large language models (LLMs) and other artificial intelligence, licenses (e.g., healthcare, law, trades), and many other special qualifications make employees who possess these skills more valuable to the organization (when these qualifications are job-related).
   Potential Uses: Similar to previous internal experience, employees who posses certain education degrees or unique skills, such as in the use of AI technology, may be a good candidate for jobs where those skills are important. Additionally, when there are situations where an employee with a certification or license can do more or higher-level work within the same job as others without that certification or license, there may be justification for a higher salary. These unique skills or characteristics can be important drivers of job performance and may justify pay differences or make the employee a good candidate for promotion.

Benefits of Tracking and Using Merit-based Employee Data

Using merit-based data reduces reliance on subjective judgments, enhancing fairness and transparency in decisions about promotions, raises, bonuses, and other employment decisions. More importantly, relying on data may increase the effectiveness of employment decisions because you can empirically test whether those data are linked to better performance or other outcomes. Maintaining records of skills, experience, performance, and other relevant data also supports compliance with labor laws and regulations. Below are some of the key benefits of maintaining and using merit-based data for employment decisions:

• Effectiveness: Structured data helps mitigate bias by providing consistent criteria for evaluating applicants and employees, which ultimately leads to more effective decision-making.
• Compliance Support: Collecting job-related and merit-based data beyond what is required is key to a quick and successful defense of employee life-cycle decisions. This helps the organization ensure consistent decision-making.
• Legal Defensibility: In the event of disputes or claims (e.g., discrimination or unfair promotion practices), merit data can provide objective evidence supporting company decisions.
• Targeted Development: Merit data enables HR and managers to tailor training and development programs to address skill gaps or build on existing strengths.
• Succession Planning: Merit data can be used to identify internal candidates with the right experience and skills to fill critical roles, reducing reliance on external hiring and minimizing transition risks.

These benefits can only be realized when organizations collect and maintain the necessary data. Setting up systems and processes to collect and analyze these takes time and effort, but the return on investment will be well worth it. Reach out to your Berkshire Associates consultant to discuss what kind of merit-based data you may already have, need to or want to collect, and what those data tell you about your employment decisions.