Data has become an integral part of Human Resources decisions. Leveraging data to make informed decisions is a major concern for HR professionals and leaders today. The days of making decisions based on “gut-feel” are over. With the right data, HR teams can gain insights into their working environment, job seekers’ preferences, employee experience, remote work trends and more. This information helps them play an active role in providing employees with the best possible experience.
Here are some internal data sources HR can use to inform decision making:
- employee surveys – these provide key metrics on how your employees are feeling
- engagement levels
- suggestions to improve
- performance & talent metrics – Do you have a clear view of your talent?
- performance ratings
- identification of high potential (HiPo) employees
- distribution of ratings
- recruitment data – useful for assessing the effectiveness of your Talent Acquisition team and the candidate experience
- time-to-hire,
- cost-per-hire
- quality of applicants.
- employee turnover rates – are you retaining the people you want to keep?
- short term tenure / new hire failure rates
- average tenure
- voluntary vs involuntary turnover
- Employee demographics – these help measure the impact of D&I strategies or to help target HR policies and benefits appropriately
- Age
- Gender
- Location
- Ethnicity
A good HRIS system will capture the above, but many organisations overlook their HRIS a valuable source of actionable data, viewing it more as just a place to store employees’ personal details and salary. Some organisations use supplementary tools to track some of this data, especially if their HRIS does not offer full MI functionality. In absence of any software platform, even a spreadsheet will be of some use in tracking key metrics. Regardless of which approach you use to capture your employee data, analysing it correctly can give valuable insights into whatever employee related problem you are trying to solve.
Let’s consider a simple example of trying to improve retention rates and the steps you might take.
- Extract the data from your source and use data analysis tools and techniques to identify correlations between different data points. As an example, you may find that there is a positive correlation between employee satisfaction and retention rates.
- Interpret the findings and draw insights from them. For example, if you find that there is a strong correlation between employee satisfaction and retention rates, you may conclude that improving employee satisfaction is likely to improve retention rates. (I know this is a no-brainer, but bear with me for the purposes of this example!)
- Develop a data-led strategy based on the insights gained from the analysis. For example, you may decide to implement initiatives to improve employee satisfaction, such as increasing compensation or offering more opportunities for career development.
- Monitor progress regularly and make adjustments as needed. For example, if the initiatives to improve employee satisfaction are not having the desired impact on retention rates, you may need to reassess your strategy and make changes.
Previously, HR may have had a hunch that retention was related to employee engagement, but now data can be used to back up that hunch and also to monitor the improvements. This becomes especially important if HR needs to request budget from management to implement the changes. Business leaders are likely to be more responsive to requests backed by hard metrics.
Retention is only one example. Another one is flexible working. Many organisations are struggling with the right amount of flexibility to grant employees (if any). By leveraging relevant data points, HR managers can gain insight such areas as how flexible schedules impact productivity and attendance, and tailor policies that cater to both employers’ needs as well as those of their staff members. A potentially positive effect on engagement metrics might also be measured, as more flexible working may better better equip employees to accommodate personal reasons or family commitments. Thus, if data can show more flexible working does not impact productivity and also boosts engagement, then a strong business case for change can be made.
Data analysis has become an essential part of Human Resources decision-making processes. By leveraging data from various sources, including employee surveys, performance metrics, recruitment data, turnover rates, and employee demographics, HR managers can gain valuable insights that help them understand their organisation’s working environment and make data-led decisions. The use of data helps HR managers play an active role in providing employees with the best possible experience, and also helps in identifying and addressing challenges such as employee health, retention rates, and engagement levels. By utilising data-driven solutions, HR managers can make informed decisions and develop strategies that cater to both the employer’s and employees’ needs. Overall, the use of data in HR decision-making helps organisations improve their performance, retain top talent, and ensure the best possible employee experience.
