Show, Don’t Tell: How Your Data Can Reveal Your Impact Story
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The document emphasizes the importance of utilizing well-organized data to tell impactful stories, particularly in the context of social enterprises and nonprofit organizations. It discusses how understanding the audience is crucial for effective data-driven storytelling and outlines methods for structuring data to convey meaningful narratives. Additionally, it highlights the significance of context, potential biases, and the need for categorizing qualitative data to uncover patterns and insights.
Show, Don’t Tell: How Your Data Can Reveal Your Impact Story
- 1. Show, Don’t Tell How Your Data Can Reveal Your Impact Story
- 2. The river I step in is not the river I stand in - Heraclitus I’m a first-generation settler of Irish descent and I would like to acknowledge the place known as Toronto is covered by Treaty 13 and is the territory of many nations including the Mississaugas of the Credit, the Anishnabeg, the Chippewa, the Haudenosaunee and the Wendat peoples. It is now home to many diverse Indigenous people. As a person whose home is still under colonial rule I support the rights of all colonized people to assert their autonomy in the face of an settler state. Beadwork by Amanda Laliberte, Ashley Copage, Ashley McKenzie-Dion, Didi Grandjambe, Jennelle Doyle, Joelle Charlie, Kyla Woodward, Lenore Augustine, Marissa Magneson, Mellz Compton, Monique Jolly, and Rena Laboucan.
- 3. From poetry to process documentation... I’m Aine and I’m an “accidental techie”. I earned a doctorate in literature from U of T in 2010 and since then I’ve analyzed very few poems. Instead I’ve had every conceivable role at a nonprofit from gift administrator to board chair and have leveraged cloud computing at every step along the way. I now serve dozens of clients in a consulting capacity helping them choose and implement cloud technologies while supporting them through the change process along the way.
- 4. Been telling stories about data for a while A bunch of awkward screenshots and photos from the last 10 years of working on supporting nonprofits through digital transformation work. Check them out at ainemcglynn.com
- 5. What’s on tap? ● What stories can you tell with good, well-organized data? ● Why audience matters most? ● How do you apply structure to your data to tell stories? ● What steps can you take today to surface stories in your data?
- 6. Well-organized data that can be manipulated by the user Getting to showing. Using data visualization - or self-serve data sets.
- 7. A data-driven story for funders Showing your audience your data will support the story you’re trying to tell.
- 8. Data-driven storytelling inspires trust You have to know something about the audience for your data story before you know how to inspire trust in them.
- 9. ALL stories have audiences Who is the story for? Like any good storytelling exercise, whether it’s fiction, or text or data, you must consider your audience. i) What does this person care about? ii) What information do they need to know? iii) Who are they?
- 10. Use the Know Feel Do method Your data story isn’t about the message that you send, but the one that is received. Consider using this method: Audience Know Feel Do Who are they Articulate the point Show me you care about my priorities Be clear about what I should do next. Private Foundation with interest in social innovation Our program is committed to supporting social enterprises We track every $ invested in social enterprises. Your support for our program is also support for social innovation Review the data yourself and see where we spend our money.
- 11. Know your audience? Get to know your data Data Knowledge Information Wisdom
- 12. What logic will you apply to your unstructured data? Data must have structure to be used in storytelling.
- 13. Theory of Change The theory of change leads to the measurement framework Here you generate the kinds of indicators that help you understand what kind of structure to apply to your data
- 14. First step is to take stock Auditing your program data is the first place to start. Gather: ● Surveys ● Email invites ● Event details ● Event themes ● Registration forms ● Facilitators ● Participants ● Photos ● Budgets
- 15. Then determine the questions you want to ask What questions do you have about your program that you want to apply to this data that you’ve collected? ● Did participants benefit from the program ● Which events were best attended ● Did some facilitators resonate better than others ● Have our program costs increased, decreased or stayed the same? Why?
- 16. Context is key Make sure you’re considering context ● Your registration data might look high, but your attendance data is low. Did something happen that led to people not attending? Was an email reminder sent? ● Survey data may indicate that some people did not have a good experience with your program. Are there commonalities in their context that would explain this? Did they all participate on a day where there was technical difficulties for example?
- 17. Be aware of bias The data about those who didn’t complete your program or weren’t successful is as important as the inverse. Your solution might not be in what is there, but what is missing.
- 18. Categorize qualitative data You may not know how to analyze text - for example anecdotal feedback given via interview or survey. Read all of these responses first, then group them into broad categories. Now you can begin to recognize patterns.
- 19. Reflections and Questions ● What data do you have? ● What questions are you trying to answer? ● Who is the audience for your data-driven stories? ● What structure do you need to apply to your data?
Editor's Notes
- #7: Data is best shared in dashboards that can be manipulated by the end user For example, working with a client who had a fundraising board, we created a dashboard in google data studio and embedded it on the board intranet so that board members could always see how they’re fundraising efforts were going. When people can see their progress against a goal, they’re much more likely to continue towards it. In this case, the story is fundraising campaign success and the data - the pipeline is motivating people to continue to engage with the effort. Similarly, working with a client who runs a program with post-secondary students and mentors. When they see program completions data coming in, they are excited and motivated to keep working hard. They can do their own analysis on the students meaning that they can pose any question they’d like of this data set and generate an answer to it so that they can tell their funders great stories of progress and success.
- #8: Used the data to tell the story. Because the data - especially financial data - really doesn’t lie. The effort to track indicates to funders the intention and the values of the organization. This creates trust which in turn leads to the right conditions for support.
- #9: LIke an
- #12: At the bottom of the DIKW pyramid is data - this is received from the world more or less as a reflection of how the world works (this is drastic over-simplification for this purpose). Used to be used in religious contexts to indicate something given. When water boils, the steam rising from the kettle is data. When I observe this data, I apply reasoning to produce information. The water is boiling and ready to make my tea. But I can’t alway be watching the kettle. How can this information be transformed into knowledge? When I apply a modifier/whistle to the spout, I can walk away from the kettle and the whistle will inform me, wherever I am in the house, that the water has boiled.
- #13: Most of the data that you have is unstructured data. But you can apply logic to this unstructured data to extract insights and apply structure to it so that you can work with it in a story-telling context more easily. Applying structure to this data starts by clarifying your mission-related goals. If you haven’t conducted a theory of change exercise for your organization/program or haven’t refreshed it in a while, this is your first step.
- #14: A Theory of change is the story flow that all data contributes to. Without it, it can be very difficult to know What kind of data we should be collecting? What do we collect that we don’t need? What data are we not yet collecting? And as such it is very difficult to use data to tell a compelling story of impact, transformation etc without knowing what you’re working towards.
- #15: Knowing what kind of data you have is a good first step in understanding what kind of stories you can begin to tell now. Did you have more than 75% participation in your program survey? This can be a sign of high engagement. Did you have a high open rate on your email invites? This can be a sign of good engagement. Do you have participants that attend multiple events or programs? This can be a sign of high satisfaction. Look for the “countables in this data” to begin to make inferences about what these figures mean.
- #18: During World War II, researchers at the Center for Naval Analysis faced a critical problem. Many bombers were getting shot down on runs over Germany. The researchers’ bullet hole data had created a map of the exact places that the bomber could be shot and still survive.