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These three approaches suit team briefings and presentations where people understand the figures but struggle to connect them to their work. None guarantees motivation, and none should be used to disguise uncertainty.
Inspire with data by making meaning concrete
Duarte describes using story techniques to make data more meaningful. The practical challenge is choosing a comparison or example that clarifies without distorting.
Before adding an emotional frame, confirm the measure, period, denominator and source. A vivid comparison built on an unclear number makes the presentation more memorable for the wrong reason.
Ask what you want the audience to understand, not merely what you want them to feel.
1. Translate scale into a relevant comparison
A large number can be difficult to interpret. Compare it with something connected to the audience's task: workload per shift, time per request or the share of a relevant population.
Use a comparison you can calculate and explain. Avoid spectacular equivalents that look impressive but have no decision value.
Hypothetical example: A process produces 240 duplicate entries in a month. Rather than comparing that number with an unrelated object, explain how many require manual review and what information the team needs to assess the burden.
Do not convert every minute into a financial saving unless the assumptions are valid. Time avoided does not automatically become cash saved or additional output.
Check the conversion with someone familiar with the work. A mathematically correct comparison can still be operationally unrealistic.
2. Connect the result to a contribution people recognise
Show what people did that plausibly contributed to the finding. Use verified actions and give credit accurately.
Hypothetical example: A team has documented that more requests arrive with complete information after it introduced clearer examples in the form.
The presentation can recognise the colleagues who wrote and tested those examples. It should still acknowledge other changes that may have influenced the result.
Avoid turning one employee into the sole hero of a shared effort. Ask who designed the change, who implemented it, who maintained it and who supplied feedback.
Recognition can be specific without being absolute: “Your examples helped make the instructions clearer” is different from “You single-handedly transformed service quality.”
3. Show a next action within the audience's control
A meaningful finding can create frustration if people have no authority to respond. Connect the data to an action they can reasonably take.
For a frontline team, that might mean using an agreed field or escalating an unclear request. For a leadership group, it might mean funding a trial or resolving ownership.
State what support is available and what remains outside the audience's responsibility. Do not use an inspirational message to transfer a structural problem to individuals.
Hypothetical example: If delayed approvals are concentrated at one decision point, asking staff to “work harder” does not address the finding. The appropriate action may belong to the approval owner.
Keep the emotional frame proportionate
It is acceptable to express concern, appreciation or curiosity. Explain what in the evidence prompts that response.
Avoid implying that a disappointing number reveals a person's commitment or character. Measures describe defined activity; they rarely explain motivation on their own.
Do not use a moving customer anecdote to establish the prevalence of a problem. Pair it with suitable evidence or identify it as an illustration.
If the data is uncertain, let the audience hear the uncertainty before asking for commitment.
Use a short preparation worksheet
Answer five questions before presenting:
- What exactly does the number measure?
- What comparison makes it easier to understand?
- Which contribution can we support with evidence?
- What action is within this audience's control?
- What limitation must remain visible?
If the answers require speculation, gather more information or narrow the message.
The worksheet can also reveal that no inspirational framing is needed. Sometimes a neutral explanation is the most respectful and useful choice.
Check whether your comparison creates a false impression
A relatable comparison should preserve the meaning of the original measure. Converting an annual total into a daily figure may help, but only if the distribution and working period make that interpretation sensible.
Do not suggest that an average describes every person's experience. A team average can hide substantial variation.
If the comparison depends on assumptions, state them in ordinary language. Ask a colleague to explain the comparison back to you without seeing the calculation.
Watch for emotional language that outruns the data. Words such as crisis, breakthrough and unprecedented require an appropriate basis.
You can still express appreciation or concern. Tie the response to a defined finding and leave room for the audience to inspect it.
The aim is understanding that supports responsible action, not a dramatic reaction disconnected from the evidence.
Test the message with someone outside the analysis
Ask a colleague to explain the scale, contribution and next action after hearing the draft. Check whether they infer a stronger causal claim than you intended.
If they do, revise the wording. A limitation that exists only in your private notes does not protect the audience from misunderstanding.
Explore Storify, storytelling in presentations and storytelling in meetings for related guidance.
For your next briefing, choose one of the three approaches. Use it to make the finding more understandable, then let the audience inspect the evidence and decide what it warrants.
AI-assisted researched guidance from PeakBelt. Hypothetical examples are labelled in the text. See our editorial approach.