Communicating with Data
How to turn data into a clear message: why communication is part of the analysis, how to define the objective and identify an audience’s needs, and a WHO / WHAT / HOW framework to apply before choosing a format.
- Communication is part of the analysis, not an afterthought. A finding creates value only once an audience understands it and acts on it.
- Effective communication starts with the audience, not the data. What convinces an analyst is rarely what convinces a decision-maker.
- A single message should be defined before any chart is built. After that message can be stated in one sentence, the data, format, and tone follow from it.
Why communication matters
A correct result does not communicate itself. The same regression table can inform a decision or go unread, and the difference is typically communication, not statistical rigor. Well-communicated data is easier to engage with, more credible, and more likely to be remembered beyond the meeting where it was presented. It also reduces the burden on the audience, since a clear one-page summary answers more questions than a dense appendix.
The objective is not to demonstrate analytical sophistication, but to produce findings that are understood and used, a more demanding and more valuable standard.
Starting with the objective
Communication is easier to plan once its purpose is explicit. Most research communication serves one of three objectives:
- to inform donors, partners, or participants about what a project found;
- to involve the research community and relevant specialists in building a body of evidence and shaping debate; or
- to influence public-sector or other organizations to incorporate the evidence into their policies and programs.
The objective shapes every later choice, from the level of detail to the format and the timing.
Understanding the audience
Before selecting a chart type, communicators should identify who they are addressing. A visualization that convinces a fellow researcher is often unsuitable for a policymaker, a program manager, or a community partner. Relevant questions include:
- Who is the audience? Their role, technical background, and the context they already share with the presenter.
- What does the audience already know, and how much background is required before the point can land?
- What does the audience care about? A researcher may want to know whether an effect is statistically significant; a program manager wants to know what action to take.
- How will the audience encounter the material, through a printed brief, a slide during a presentation, or an interactive page? Each format requires different design choices.
A study finds that a tutoring program raised test scores. A researcher wants the effect size, confidence interval, and identification strategy. A program manager wants the hours of tutoring behind the effect and its cost of delivery. A funder wants the bottom line: does the program work, for whom, and is it worth the cost? The underlying evidence is identical; tailoring the message to each audience is not spin, but a way of meeting each audience’s specific decision needs.
Defining a single message
Before producing any visual, communicators should be able to state the central finding in one sentence that could be delivered without slides in under three minutes. After that message is defined, subsequent decisions become straightforward: how much data the audience needs to find the claim credible, the two or three takeaways to emphasize, and whether a chart is required at all or a sentence and a single number suffice.
An inability to compress a finding into one sentence indicates that the finding itself is not yet fully understood, a signal to continue the analysis rather than to add supporting slides.
A chart title should read as a complete, sentence-length takeaway (“Attendance rose 15 days after deworming”), not a topic label (“Attendance by group”). If it cannot, the underlying message has not yet been defined clearly.
The WHO / WHAT / HOW framework
For any piece of data communication, whether a slide, a brief, or an email, three questions organize the work:
- WHO needs to be reached, and who holds the power to act? Distinguishing between those who will use the result and those who are merely interested allows effort to be concentrated where influence and interest are highest.
- WHAT does the audience need? The one or two messages relevant to its decision, not the full set of findings. The takeaway should lead, with methodology available further down for interested readers.
- HOW should the message reach its audience, through a chart, table, memo, or conversation? The format should match how the audience consumes information and how much attention it is likely to give.
Turning Data into Impact applies this framework to decision-makers in more depth.
Choosing the format
Not every number requires a graph. The right format depends on what the audience needs:
- A sentence with a number suffices for a single key comparison (“children attended 15 more days per year”).
- A table works when readers need exact values or want to compare several figures.
- A chart is warranted when the point is a pattern or relationship that is easier to see than to read.
Choosing the Right Chart covers this decision in detail.
Format also extends beyond a single figure to the product as a whole—technical reports, briefs, presentations, blogs, or social media posts. As the audience grows broader, the appropriate level of detail generally falls: a core audience reads a full report, while a wider one is better reached through a brief or short post. Format should follow from the objective, the audience, and available time and staff—and timing matters too, since the same product lands harder while a decision is still open (see Turning Data into Impact).
Writing for a non-technical audience
A few habits keep results clear and credible for a non-specialist audience:
- Use plain language, avoiding jargon and technical shorthand.
- Lead with the main point rather than building up to it.
- Organize information from most to least important.
- Place each figure against a benchmark so it carries meaning, and make the finding’s relevance explicit.
- Avoid unfounded claims and emotionally loaded language, letting the evidence carry the argument.
Informed only that free school uniforms and child deworming cost the same amount, an audience has no basis for choosing between them. The evidence becomes useful once it is communicated in decision-relevant terms: deworming young children led them to attend school approximately 15 more days per year for the same budget. The underlying statistic did not change between the two framings; the communication did. This illustrates the central point of this page: the goal of data communication is not to describe the world accurately for its own sake, but to make a good decision easier to reach.
References
Duflo, Esther. 2004. “Scaling Up and Evaluation.” In Annual World Bank Conference on Development Economics 2004, edited by François Bourguignon and Boris Pleskovic, 341–69. Washington, DC: World Bank.
Evergreen, Stephanie D. H. 2019. Effective Data Visualization: The Right Chart for the Right Data. 2nd ed. Thousand Oaks, CA: SAGE.
Knaflic, Cole Nussbaumer. 2015. Storytelling with Data: A Data Visualization Guide for Business Professionals. Hoboken, NJ: Wiley. https://www.storytellingwithdata.com/.