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Data cleanup done right
March 20, 2012
By Brian Kelly
When it comes to successfully launching a workforce analytics initiative and benefiting from this era of “big data,” the most common challenge that organizations seem to think they face is one of data quality. While I agree that all data is not created equal and some organizations have a higher quality of data than others, I fundamentally disagree on the premise that “clean” data is prerequisite to undertake an analytics initiative – an organization will never have clean or perfect data. In fact, I believe that launching analytics programs will actually
helpwith data quality and data cleanup. Pick up any HR-related publication or peruse any HR-related conference agenda and one topic you are sure to find is workforce planning.
Just think about it. How much engagement and participation do you get when you launch a data cleanup effort at your company? Do people know why you are doing it? Does a cleanup effort change their behaviors to provide better data?
The answer to most of these questions is a resounding “no.”
Why? Because without an over-riding purpose, data cleanup efforts often fail. This is why starting a workforce analytics initiative should not wait; in fact, the workforce analytics launch can
serveas the reason for a data cleanup effort! Only by shining the proverbial “light under the rock” on a compelling business issue at hand – and surfacing this data through an analytics program – does an organization truly understand the depths of its data quality issues and, more importantly, whether data quality issues matter. This way, you don’t need to waste time in trying to clean up data that no one cares about.
A simple test for whether or not an organization has enough data and enough quality data, for an analytics initiative is to ask one simple question: “Did your organization make its last payroll on time?”
If you can answer “yes” to this question, then you have enough data, and enough quality data, to start right now. Making payroll means that data exists for each employee such as start date, position, base pay, incentive pay (if any), etc. From there, you can decide which data to add to the analysis, you can evaluate the quality of that data and, if data cleanup is required, you can enlist the help of colleagues with a clear explanation and reason.
So, if you made payroll last month, what are you waiting for? Get started on your workforce analytics initiative today!