Which step is part of applying data-driven decision making in L&D to improve programs?

• A. Rely on gut feeling
• B. Wait for annual review
• C. Collect baseline data
• D. Skip experiments

Answer: (C)

Data-driven decision making in L&D starts with establishing a baseline. This baseline provides the concrete reference point you need to judge whether a training program actually makes a difference. By collecting current performance and learning indicators before any intervention—such as assessment scores, completion rates, time to competency, or on-the-job performance metrics—you can set realistic goals and design ways to measure impact. Then, when you roll out a program, you compare post-implementation data to the baseline to see what changed and by how much, which helps you determine effectiveness and where to refine.

Relying on gut feeling skips the objective evidence that shows what’s actually working. Waiting for an annual review is too slow and may miss timely insights for improvement. Skipping experiments eliminates the opportunity to test what works best and attribute outcomes to the program rather than to other factors. Collecting baseline data is the essential first step that makes any subsequent data-driven improvements possible.