Data Fluency Series: How to Tell Good Data from Bad Data

This is the first episode in a new series on Data Fluency. Knowing how to tell good data from bad data is key, because it will affect you whether you are an HR practitioner, team leader, or individual. 

Bad data is a pervasive problem in our world, and especially in the world of work. It’s a problem that we have copious amounts of useless data, but the fact that we’re treating this bad data as though it’s good data – as though it will validly predict things like performance – is nothing short of a disaster. People are being promoted and fired because this faulty data that actually tells us nothing about our employees.   People are being promoted and fired because of faulty data. That has to stop. Share on X

This data series is for everyone. It’s for HR leaders looking to provide their company with the right data to make the best decisions. It’s for leaders and managers who are being asked to rate or rank their employees, when those ratings mean nothing. And it’s for individual contributors – any person with any job in which there is a performance review – because you need to know how to ask the right questions to challenge the status quo. Don't be a casualty of bad data - Data fluency means knowing the difference between good data and bad.. Share on X

When it comes to data there are three important words to remember:

1. Reliability: Is the person a reliable rater of what is being measured?
2. Variation: Does the tool reveal the true range that actually exists?
3. Validity: Does what we’re measuring matter? Does it validly predict something else in the real world?

Most HR tools fall short of at least one of these criteria – and many fall short on all three. The next time you are asked to participate in any sort of performance rating, opinion survey, 360-degree survey, or any other source of people data, ask yourself if it complies with these three standards. Odds are, it won’t.

Check back next week for more information on how to become data fluent, and the impact of bad data on our businesses.