In this episode, David and Dwight dive into the critical and ever-present issue of poor data quality in HR and its cascading impact on the organization. They break the problem down into 3 key areas: recruiting, artificial intelligence, and pay transparency. They explore how recruiting often serves as the flawed entry point for employee data, discuss the dangers of training AI on biased information (which can lead to discriminatory practices like ageism), and examine the new data governance challenges posed by emerging pay transparency laws. [0:00] Introduction Today’s Topic: The Impact of Poor Data Quality on HR Today  [5:24] How does poor data quality in recruiting create downstream problems? Recruiting is the first ingress of data into an organization, and it's often the least well-managed. Poor data can also originate from the company side, with issues like inaccurate job descriptions or incorrect FLSA classifications. [12:08] How can biased data lead to discriminatory AI in the hiring process? AI agents used in video interviews can reject qualified candidates based on flawed or biased training data. Companies that fail to address AI bias on the front end risk facing expensive lawsuits and will be forced to fix their systems later. [19:32] What are the data governance challenges presented by pay transparency laws? Pay transparency laws in several states require companies to disclose pay ranges for open roles also mandate that companies keep detailed records of salary structures, grade ranges, and hiring data for extended periods. Many organizations may need to update their data governance strategies to ensure they are retaining the necessary historical data to comply with these regulations and withstand potential audits. [24:31] Closing Thanks for listening! Quick Quote “As the demographic bubble for Gen X gets larger, companies can't ignore [AI bias in recruitment] anymore. They have to take it on, and that means they have to start training their artificial intelligence to not filter out all of us grays.”