Performance management is undergoing some changes one of which is the push to mine data, apply metrics, and offer up analysis based on data and metrics. While this shift has the potential to reveal new insights, it should be approached with caution because your data and possibly some of your metrics may be dirty. Similar to the components of a clean diet, a clean analysis depends on identifying the best inputs, avoiding harmful ones, and having a comprehensive understanding of the outcomes you desire.
What is clean analysis?
A clean analysis is being mindful of the source and quality of your data, and the choice of metrics being used to analyze performance in your organization. Ask yourself the following questions when considering your analysis:
- Do you know where your data comes from?
- Is the data being input correctly?
- Are you gathering the right data?
- Does your data allow you to test correlations?
- Is the data being input consistently from department-to-department or site-to-site?
- Do you understand how raw data is being translated to metrics and then to decision-making?
Before you blithely answer the questions, apply some of your human resource investigative skills. Never assume. Ask to be shown how the data is input. Take a sampling of reports that you audit and validate against the original input. Question whether the metric you are using is the best one to predict the performance outcome you desire.
Remember you are analyzing data and metrics in a quest to find patterns and correlations that will assist you in enhancing your employee and business performance. Make a clean analysis a priority by being mindful of the source of the data, how and why metrics are being calculated, and what you are truly trying to shift by applying your analysis to performance management.