When you’re presented with data — whether it’s in a dashboard, a slide deck, or a detailed report — it can feel overwhelming to know where to start. The numbers, charts, and metrics all compete for your attention, but not all of it is equally important. So how do you cut through the noise and focus on what matters most? Let’s walk through some practical steps to help you make sense of the data in front of you and turn it into meaningful insights.

Here’s the good news: you don’t have to be a data scientist to get better at this. With a little practice and a clear framework, you can build confidence in your ability to read and interpret data — no matter your role or organization.

Key Takeaways

• Data usually leaves us with initial conclusions and more questions — it’s important to dig deeper.
• Always challenge yourself to come up with multiple explanations for what you’re seeing.
• Context is key. Look for internal trends and external benchmarks to contextualize what you’re seeing

Step-by-Step Guidance for Understanding Data

1. Start with the basics: what are you looking at?

Write out the key metrics you see in the chart, table, or graph. For each one, note the current performance. This might seem unnecessary, but clearly documenting the metrics helps you stay focused and prevents you from overlooking anything important.

2. Check the foundations

Before jumping to conclusions, make sure you understand the context and quality of the data:

• Timeframe: Over what period has this data been collected? Is it a snapshot or a trend over time?
• Scales: Does the graph’s axis start at 0? If it’s a percentage, does the axis go all the way to 100%? If you’re comparing multiple graphs, are the axes consistent?
• Calculations: If the metric is calculated, do you know the denominator (e.g., what’s being divided or compared)?
• Comparisons: Do you have data for another time period or from a peer organization or location for context? Make sure you’re clear whether comparisons are framed in terms of percent or percentage point shifts and beware of apples and oranges comparisons.
  • Example (percent vs. percentage points): If satisfaction rises from 40% to 50%, that’s an increase of 10 percentage points. In relative terms, it’s a 25 percent increase (because 50 - 40 = 10 and 10 ÷ 40 = 0.25).
  • It’s usually simplest to interpret data using percentage point changes, though people often talk about percentage point (pp) change but inaccurately say “percent.”
• Sample size: How many data points are reflected? Smaller numbers mean proceed with caution. Might still be interesting and certainly better than nothing, but be skeptical about whether you have the full picture.
• Data quality: Are you aware of any missing or incomplete data? What about potential for duplicates or incorrect data collection? How was the data cleaned or processed?

3. Reflect on what stands out