In this era of big data—and as a term that gets thrown around freely these days, know that I'm using it here to denote the overwhelming amount of data that’s available in the modern age—quantitative data is beginning to face the challenges traditionally seen in dealing with qualitative data. Many firms can now track employees start and end times, hours spent using various programs on their computer, average days off by month, training classes attended in the last year, etc., but as with qualitative data, it's difficult to know which pieces of data are relevant and how they interact.
As with qualitative data (e.g. interviews, observations), the trick is to look for themes or trends and to assign instances to specific categories. For example, the qualitative measurements captured in exit interviews can be converted to quantitative data. In this instance, you aggregate themes or trends of why people leave from compiled interviews to build a classifiable list of occurrences. Family events such as a spouse taking a new job or employees leaving because of poor local school can be categorized as external factors beyond company control. These occurrences could then be separated out from factors that require action, although exceptionally large numbers of employees leaving for external reasons may bear re-examining.
Other occurrences can be grouped into internal, controllable factors, such as management conflicts, compensation or career advancement opportunities. If a specific trend or overwhelming majority of exiting employees are falling into one of these buckets, qualitative data will illustrate that trend, which can be supported with a few examples hand selected from the interviews.
The new i4cp report, Evidence-Based Human Resources in Action, discussed how one i4cp member company implemented these techniques, and the challenges they faced in doing so. i4cp members can access this new report from the i4cp website.
As organizations become more inundated with data that has become meaningless due to its sheer volume, we must find ways to compartmentalize it for more effective decision making. This report highlights some of the challenges faced with building a usable library of workforce data and the processes used to make that data core to organizational strategy development.
One of the biggest strengths of using qualitative research methods or immense data sets in a workplace setting is also one of the biggest drawbacks—complexity. Using data gathered from observations and interviews provides all sorts of contextual and emotional background to an issue, but that is precisely what can make it unreliable or unwieldy. The same is true for big data. Compiling the many disparate pieces of information into manageable categories is what will bring order out of chaos.