How do you transform data into a great story?

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How do you transform your troves of data into a story that fascinates people and moves them to act? This is an issue with which researchers, analysts, and evaluators grapple on an ongoing basis.

Last week, I had the opportunity to learn and reflect on these very matters with colleagues working with data in the nonprofit sector all over the world. On behalf of my organization, Firelight Foundation, I participated in a conference entitled Data on Purpose: Telling Great Stories with Data, organized by the Stanford Social Innovation Review. There were a number of captivating speakers but I will share you summaries of two of the presentations that really stuck with me. (I am keenly aware of the improvement needed in my storytelling skills as I write a post specifically about how to tell great stories…)


Grabbing the Attention of Stakeholders Through Powerful Data Storytelling Techniques

In this presentation, Sacha Litman, Managing Director at Measuring Success, reviewed ways in which we can use the power of stories to reduce people’s resistance to and increase people’s engagement with data.

Sacha started off by noting that data analytics can be highly disruptive to an organization, because stakeholders have vested interests, and people experience fear, uncertainty, and doubt. Where does this resistance come from? Sacha argued that it comes from people’s anecdotes – to which they hold strongly, even when these are contradicted by data. As Sacha said, “data is unsatisfying when it does not support people’s anecdotes.” Sacha described how many – most even – of people’s anecdotes are not supported by data, and yet people base huge decisions with major implications on these anecdotal experiences that they have.

Sacha proposed that one way to harness the power of anecdotes and stories is to link data to those stories. Another strategy, to diffuse the power of anecdotes, is by turning them into hypotheses that can be tested with data. For example, if anecdotally we have seen that asking someone join a board resulted in their increased donations to the organization, we can take that anecdote and turn it into a hypothesis and question, “Does joining a board result in increased donations to an organization?” – and then testing that hypothesis or answering that question by looking at the data.

Sacha presented seven stages of data-driven decision-making (below), and argued that nonprofits spend too much time in stages 3 and 4, and perhaps even 5, but fall short in stages 1 and 2, and stages 6 and 7. He also spoke about needing to work over the long term to shift the culture at an organization towards data-driven decision-making.

Seven stages of data-driven decision-making: where nonprofits fall short

  1. Framing the problem
  2. Hypothesis and logic model
  3. Data collection
  4. Data analysis
  5. Interpretation
  6. Decision making
  7. Storytelling

Combining the Magic of Storytelling with the Best Practices in Data Visualization

In this presentation, Cole Nussbaumer Knaflic, founder and author of Storytelling with Data, reviewed a path from data to visualization to story, along the way explaining key principles in presenting your numbers, charts, and other visuals. The path she presented included five key steps, outlined here:

(1) Understand the context: Cole described the importance of asking and answering three major questions: (a) Who are your communicating to?, (b) What do you want your audience to do?, and (c) How can data help make your point?

(2) Choose an appropriate visual: Cole reviewed the power of simple text, tables, and common graphs. She demonstrated how different visuals can be effective for different purposes, depending on what point you are trying to make and what you want your audience to pay attention to.

(3) Identify and eliminate clutter: Cole reviewed Gestalt principles of visual perception (proximity, similarity, enclosure, closure, continuity, and connection) and showed how they can be applied to remove clutter (e.g., grid lines in graphs, data markers, label legends, etc.) from visuals to make them clearer to read and understand.

(4) Focus your audience’s attention: Cole reviewed strategies that can be used to focus your audience’s attention to key points in your data visualization – e.g., color, size, underlining, bold, etc.

(5) Tell a story: Cole talked about the importance of repetition (e.g., sound bites), and reviewed key components to include in a narrative: Plot – What context is essential?, Twists – What is interesting about the data and what it shows?, and Ending – What do you want your audience to do?


Many of the key principles presented by these two and other presenters at the conference have really stuck with me. I find myself referring back to them as I take second and third looks at my own and others’ writings – from research reports to blog posts, and all of the text, tables and charts within them presenting findings from data. It’s a shift in perspective – “from data publisher to data communicator” (in the words of another presenter – although I unfortunately cannot remember which presenter said this).

Knowledge sharing is a fundamental component and skill set for researchers, evaluators, and others working with data. It is critical to be able to communicate findings in ways that people understand, so that they can make evidence-based decisions about programs and policy. And yet, I am struck by the absence of ‘knowledge sharing’ as a topic in most academic and professional degree programmes in research and analysis. Communicating findings effectively needs to be a core part of the training provided to graduate-level and early career researchers/professionals. (On a related note, I think that graduate-level and early career researchers should also be provided support and guidance in managing their digital identities and navigating online fora for communicating, learning, and sharing research and evaluation findings.)

For now, I am looking forward to implementing some of these principles and strategies in both my academic and professional work, to transform some of the data I have been working with into stories which engage and move people to act.

How do you share your research and evaluation findings in ways that interest and motivate? Please share in the comments section.

Sadaf Shallwani

See related post:

One thought on “How do you transform data into a great story?

  1. I’m a big fan of sharing research information through blogs and short videos (e.g.TED style talks). As a blogger myself, one of the challenges I face is trying to get the information on my blog to non-bloggers who could benefit from the insight I share. I haven’t yet tried my hands on creating videos to share research though.

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