Leveraging Data the Goldilocks Way, Part 2: Making Meaning from the Metrics

Last week, in Leveraging Data the Goldilocks Way: Part 1 — Measurement That’s Just Right, we explored how to right-size what we measure so that data collection serves a purpose, not a spreadsheet. This week, we move to the “E” in the MEL process: Evaluation.

If measurement tells us what happened, evaluation asks what it means.

For many organizations, this is where momentum stalls. You’ve gathered data, maybe even received a consultant’s impressive report, and now you’re left wondering: So what do we do with all of this? It’s a common — and completely understandable — moment of overwhelm. The truth is, data alone isn’t the finish line. It’s just the raw material. What matters is how you make meaning from it. That’s where evaluation comes in. Done well, it doesn’t add another layer of work — it creates clarity. It helps teams move from “we have data” to “we know what it’s telling us.” Evaluation is about slowing down just enough to connect the dots — to transform information into understanding, and understanding into confident, sustainable decisions.

🧭 Before You Evaluate — Make Sure the Data Make Sense

Before you jump into interpreting results, pause and ask a simple question: Does this data actually make sense?

It’s easy to assume that once data is collected, it’s automatically reliable. But that’s rarely the case. Sometimes numbers are incomplete, categories change halfway through a project, or data gets processed in ways that distort what really happened. If we don’t catch those issues early, we end up telling the wrong story — and making the wrong decisions. Think of this step as your sanity check before you start drawing conclusions:

• Does it pass the sniff test?: If your volunteer hours suddenly doubled in a month, is there a clear reason why?
  💡 Try this: Look at year-over-year or month-by-month patterns. If a number jumps, ask what changed operationally.

• Is something missing or oddly shaped?: Gaps, missing surveys, or “too perfect” patterns are red flags.
  💡 Try this: Scan for blank fields, missing time periods, or exact round numbers — all clues that something may be off.

• Have definitions shifted?: “Participant” might mean something different now than it did last year.
  💡 Try this: Revisit how each key term was defined in past reports before comparing results.

• Could processing have changed the story?: Combining or averaging data can unintentionally blur what matters.
  💡 Try this: Ask whoever manages the data what transformations or filters were applied — and why.

• Are you seeing the whole picture?: Numbers only tell part of it; look for signals that confirm or challenge them.
  💡 Try this: Pair quantitative data with at least one qualitative source — staff feedback, client stories, or external benchmarks.

The goal isn’t to be a statistician — it’s to stay curious enough to make sure your data reflects reality.

🪞 Watch Out for the Stories You Bring to the Table

Once your data looks sound, the next challenge is what happens inside your head. Evaluation isn’t just about data — it’s about interpretation. We all bring expectations and experiences to the table, and that’s where bias creeps in. A few of the usual suspects:

• Confirmation bias: Seeing only what supports our belief that the program works.
  💡 Try this: Write down what you expect to find before reviewing results — then see where reality diverges.

• Anchoring bias: Letting early results shape later interpretation.
  💡 Try this: Review new data on its own before comparing it to past performance.

• Availability bias: Letting one powerful story overshadow broader trends.
  💡 Try this: Pair every anecdote with one piece of quieter evidence.

• Groupthink: Going along with the room’s consensus.
  💡 Try this: Assign a “devil’s advocate” at each meeting whose job is to ask, “What might we be missing?”

These aren’t flaws — they’re human tendencies. The key is to notice them and create small habits that keep curiosity alive.

🔍 Bringing It All Together

Evaluation is where data becomes meaning. It’s the pause between measurement and learning — the moment we step back to make sure we’re not just counting what happened, but understanding why it happened and what it tells us about where to go next. When we approach evaluation with curiosity instead of certainty, we move from “proving success” to building understanding.

• 🌱 A strong evaluation process produces real knowledge. Knowledge that’s been tested, questioned, and verified — not assumed. When done well, evaluation:

  • Connects data directly to purpose and strategic goals.

  • Clarifies what is working — and why.

  • Challenges assumptions and checks for bias.

  • Encourages reflection before action, replacing urgency with clarity.

  • Strengthens trust across teams and stakeholders by making conclusions transparent and evidence-based.

Example: A youth leadership nonprofit celebrates that 85 % of students report increased confidence. Instead of stopping there, the team compares this year’s data with prior results and facilitator notes. They find confidence rises most when students lead group projects. That insight leads to a redesign emphasizing hands-on leadership practice. The data didn’t just confirm success — it revealed why it worked.

• ⚠️ A weak evaluation process carries real risks. It may create more confusion than clarity — and over time, it can erode trust in both the data and the decision-making process. When evaluation is rushed or superficial, it often:

  • Stops at the numbers without testing what they mean.

  • Confirms preexisting beliefs instead of challenging them.

  • Misses warning signs or unintended outcomes.

  • Produces reports that look impressive but don’t guide action.

  • Leaves teams disengaged, doubting whether measurement is worth the effort.

Example: A community arts organization proudly reports a 60 % increase in event attendance. But no one asks who those new attendees are. Later, they learn it’s the same loyal patrons attending multiple events — not a broader audience at all. The data looked good but offered no real guidance.

At the end of the evaluation process, you don’t just end up with data — you end up with knowledge. Knowledge that has been tested, refined, and grounded in reality. Knowledge you can trust when making decisions, communicating results, and planning what comes next. Next week, in Part 3, we’ll explore how to turn those insights into action — how organizations can move from reflection to growth, and from information to impact.