What is a typical sequence in data-driven decision making?

• A. Set goals first, then collect data, then adjust instruction.
• B. Collect data, analyze, identify trends, set goals, adjust instruction, and monitor progress.
• C. Monitor progress, then collect data.
• D. Analyze data but do not use it to inform instruction.

Answer: (B)

Data-driven decision making relies on an evidence-first cycle. Start by collecting data on how things are currently performing so you know where you stand. Then analyze that data to uncover trends, patterns, and gaps. With those insights, set clear, measurable goals to target improvements. Next, adjust instruction or interventions based on what the data indicates will close the gaps. Finally, monitor progress to see whether the changes are producing the desired results and to inform the next cycle of action. This sequence—collect data, analyze, identify trends, set goals, adjust instruction, and monitor progress—embodies the disciplined use of evidence to drive decisions.

Starting with goals before data risks targets that aren’t grounded in evidence. Monitoring progress without data provides no baseline or proof of impact. Analyzing data without using it to inform instruction misses the essential action step that closes the loop and moves practice forward.