Have you ever gotten the request, “English, please!” when talking about analytics? This episode is for you. In the 4th episode of our weeklong Sherlock Holmes series, Tom talks about various issues around interpreting data in the compliance space.
- Today’s podcast is informed by two articles from the MIT Sloan Management Review, “Is Your Company Ready for HR Analytics” by Bart Baesens, Sophie De Winnie and Luc Sels and “Why Big Data Isn’t Enough” by Sen Chai and Willy Shih.
- From the top, Tom uses the story The Adventure of Silver Blaze to introduce this week’s topic and some of the general points to follow. Some of these issues include understanding the absence of data and interpreting employee network dynamics.
- Big Data and data analytics are incredible tools in the context of compliance but they’re only a part of the equation. Compliance professionals need to understand both their limitations, proper usage and be able to interpret data by intersecting it with all kinds of organization information. This interpretive role is akin to Holmes understanding the lack of an event, a dog barking, as a significant factor.
- When interpreting and communicating data, simplicity is key. It’s easy to get sidetracked by the statistical performance of your analysis but at the end of the day, quality insights are your priority.
- Internal bias can sneak up on you if you’re not careful. To avoid making subpar inferences or conclusions as a result of working with the same datasets for too long, bring in the people who will be using the insights you provide. A second or third set of eyes can prevent a skewing your analysis and help you avoid creating problems that may not exist in the first place.
- According to the authors, “It’s important to recognize that the number of data points required for statistically significant results needs to increase as the number of variables grows. Otherwise, there will be a greater risk of false correlations.” Be mindful of sample size and variation in your interpretive efforts.
- One of the final areas that compliance professions should be aware of when working with data is systemic data collection bias. When collecting data from different sources, using different technologies, and at different times (for example), a lack of standardization can distort your results. In order to minimize error and keep your model updated, test it frequently and feed your results back into your collection and analysis efforts.
Is Your Company Ready for HR Analytics by Bart Baesens, Sophie De Winnie, and Luc Sels
Why Big Data Isn’t Enough by Sen Chai and Willy Shih.
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