To navigate data discrepancies in evaluating training program ROI, start by identifying the source of inconsistencies, whether from data collection methods or reporting errors. Cross-check data across sources and engage stakeholders to ensure alignment on metrics and definitions of success. Standardize the KPIs used across departments to ensure consistency, and adjust for external variables that may have impacted results. Finally, document your findings and resolutions to maintain transparency and prevent future discrepancies, ensuring accurate and credible ROI evaluations.

To deal with data differences, I would compare information from different sources to find the issue. For example, if attendance and performance data don’t match, I’d check records and feedback to understand what’s wrong before deciding.

Data doesn’t have to divide—it can drive smarter decisions. In a past evaluation, I faced conflicting data on training ROI. Instead of choosing sides, I dug into the root cause: different teams used varied metrics. I called a meeting to standardize our measurement criteria, ensuring we were all evaluating the same indicators. Then, we cross-checked the data against actual business outcomes—sales increases, retention rates, and team productivity. By aligning on consistent metrics and focusing on results that matter, we moved from confusion to clarity. ROI isn’t just numbers—it’s the story the right data tells.

It's really important to figure this out or else you're likely to be faced with validity concerns moving forward on anything and everything you do, even when there aren't discrepancies. And definitely do not hide them! Drill down into the data you decided to use to assess this program. Is it primarily impacted by the program? Or are other things influencing the numbers? Are the data calculated or directly generated? If the former, look at your calculations carefully for errors. Keep asking why until you get to the root cause of the discrepancies. You'll need to either be able to articulate why the discrepancy is rational or how you fixed it.

Data discrepancies can often be resolved by verifying the authority of the data. For example, when collecting feedback or conducting a needs analysis, always check who has shared or filled in the data and how it was developed. When it comes to ROI on training, it’s quite simple to measure the impact of training through data.