Human capital analytics, a field that is top of mind for talent acquisition leaders, has the potential to bring tremendous positive change to the space.
The ability to analyze workforce data allows companies to see what talent they have, where they have gaps, what their best performers look like, and who’s at risk of leaving. This level of data has the potential to revolutionize hiring and employee engagement.
There is just one problem. To be effective, workforce analytics tools need consistent, reliable data sets to answer important questions like, ‘who are our best performers?’ and, ‘what skills does our workforce lack?’
Unfortunately, most companies’ human capital data is a mess.
While 71 percent of companies say people analytics is a high priority in their organizations according to research from Deloitte, only eight percent report having any usable data, and just nine percent say they have a good understanding of which talent dimensions drive performance in their organizations.
The problem with workforce data is that, like any legacy database, it is often cobbled together with no real rules or structure. Over time, the addition of new systems, new staff, and mergers and acquisitions result in a hodgepodge of data storage techniques that make the resource impossible to study.
When companies lack any formal rules for how human capital data is captured and managed – and most of them do – it creates chaos in the database. For example, a common issue in companies is that each department defines its own job titles, job descriptions, and pay scales, which means there is no way to compare, like roles or salary trends. Similarly, some departments may be more consistent at capturing data than others; and data may be stored across multiple databases in varying formats and silos depending on who is recording it and for what purpose. This lack of consistency makes it impossible to conduct any meaningful analytics.
To fix this problem, business leaders have to take three steps:
- Establish rules for human capital data capture. The best place to start collecting data is during recruiting to establish a baseline for on-the-job performance. Collecting data from resumes and pre-employment assessments will help you track recruiting trends, and figure out which candidate profiles deliver the best results. Once candidates are hired, define formal rules for data capture. For example, establish naming conventions for job titles, set standard salary ranges, establish ranking/scoring for skills assessment, and track promotions over time. This structure will make it easy to analyze trends and predict future needs.
TIP: Establishing validation rules that require data meet certain standards before the system accepts it will ensure it is captured in the right format every time. - Clean your existing human capital data. To build a foundation for good human capital analytics, you have to fix the problems in your current data set. That means applying the new rules to clean, fix, and amend all existing data, so it aligns with those requirements. This will be a time-consuming and cumbersome job, that may require outside assistance. But the sooner you start fixing the data, the less time and effort it will require.
- Educate data collectors. Make sure everyone involved in hiring and management understands and adheres to the new data rules so that all future employee data is captured in a consistent format. The value of human capital analytics is only as good as the quality of the data analyzed. If you want these results to be reliable, you have to start with clean data.
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Even if you don’t have the resources to clean your existing data, setting rules for new data capture will help you better understand whether your recruiting efforts are working. It will also quickly highlight where you have gaps, and what assessment tools and talent management strategies are driving better outcomes. The sooner you start capturing consistent information about employees, the sooner you can analyze the workforce trends and build a better workforce for the future.
