Anthony Onesto explains why bad HR data costs organizations about $15 million a year. He walks through seven concrete steps: centralize your data, audit it regularly, standardize how people enter it, train staff, use AI to flag problems, assign someone to own data governance, and keep monitoring.
About $15 million per year (cited to Gartner).
Ernesto. And here's my latest AI and HR today. Drop. One of my subscribers to the newsletter emailed me and said, hey, I get the importance of AI, but what if my data is wrong? And I started to explore what they call Gigo or garbage in and garbage out. Which means that if you are entering or there's wrong data in, you're going to get garbage out. So that's GI Geo Gigo. It just rolls off the tongue. Folks. One of the things that I want to look at is, you know, having data quality issues. Does that have a business impact? And in fact Gartner poor quality will cost organizations on average of $15 million a year. And this is a real problem in HR. So I wanted to provide some guidance around how to avoid gigo. You're not going to be able to avoid it completely. So one data centralization. I've talked about this quite a lot. Too many HR systems out there. There's not one system that does all of this. Even at the enterprise level. There might be some systems that claim they do all of it, but really there's not one system that you have a centralized data and synchronization. So you have to be weary about all these different HR systems. You have to do regular data audits, conduct periodic audits to identify and actually correct the errors, standardize your data entry processes, train and create awareness with your team members that it's not just about data entry, that the correct data going in is, is going to be amplified if it's not correct. When you have AI advanced analytics tools, there are things that hopefully with AI can surface some of these data quality issues. So it's, you know, fourth wall. Bring having AI to instruct how your data is incorrect or wrong. Having a data governance framework, right? So outline the roles within your department, who is in charge of making sure the data quality is set up correctly and then continuously monitor. Now you're not going to solve for Giggo. Garbage in, garbage out. You're going to reduce the amount of risk. And by implementing these simple steps, it starts a great foundation that you could then overlay on top of and have trust in what you're getting around your data, your people data. And that helps you drive decision making. Thanks for listening, we'll see you next time.
