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.