Data represents people.
It can be easy to forget that when we’re being inundated by numbers, percents, charts, and tables. Adding context to these numbers and percents is one way to help partners connect with the stories and lives behind the data. Context provides clarity, meaning, and insight.
Here’s a great example from New England Donor Services that immediately had me Googling to fact-check and learn more.
For every face you saw in State Farm Stadium during the big football game, there’s another awaiting a transplant. In fact, there are over 1.5 times the number of folks on the national transplant waiting list as fit in the stadium. Wow.
When we contextualize data, we can understand not only the depth of an issue, but also the opportunities around us to help change the narrative or take action.
Here are four ways to add context to your data:
Providing an equivalent offers information on a scale that is more easily understood and interpreted by our audiences.
For example, I recently described a challenging hike to a non-hiker. This hike was tough! We climbed up 4,200 feet over massive boulders to get to the summit. I could tell this description was not computing. So I shifted and said that’s equivalent to climbing approximately 3 Empire State Buildings. Then it connected. They realized this was a big deal!
The key to equivalents is choosing a scale that your audience is familiar with. My example worked because the person I was speaking with is familiar with the Empire State Building. But that would not resonate with everyone.
What places, spaces, and metrics might connect most with your audience(s)?
Providing comparisons allows us to look at similarities and differences in our data.
A common example is using geographic comparisons, where we compare our data to other local, state, regional, or national findings. For example, you might compare high school graduation rates in your town to surrounding towns. Or maybe you are interested in comparing graduation rates of specific schools to the state.
When looking at comparisons, we can choose geographies or other characteristics that are aligned with the context of our data to highlight similarities. Or we might select areas that are different. For example, we could look at regional differences in health outcomes across the country, where we might expect variation in data due to different funding priorities and policies.
Adding historical context provides insight into trends over time, rather than presenting one number in isolation.
Let’s think about this in terms of donor data. Let’s say we’re at a meeting and someone says, we’ve brought in $100,000 in donations this quarter. Ok. That sounds like a lot of money. But is that less than or greater than what we’ve seen in the last few years?
By presenting historical data over the past few months, quarters, or years, we are able to understand changes over time, and make decisions about our strategies moving forward.
Including information about internal and external factors that may influence our data helps to provide additional nuance about the “why” behind the numbers.
For example, perhaps we had a change in staffing or funding that required us to reduce our programming temporarily. Maybe there was a global health crisis like the Covid-19 pandemic that required a shift in services, or an economic crisis like a recession or depression that reduced spending. Perhaps a new state or federal health policy was implemented that relates to the services we offer.
We see a lot of the major news outlets using this technique by annotating graphs or adding gray shading to line charts.
Providing this information helps our audiences to understand what factors might be associated with changes in our data. It also documents important organizational and historical context, that easily gets lost over time.
Think about the different ways that you might provide context to your data.
- What equivalents might connect most with your audience?
- What public data sources could provide comparisons?
- What time points offer historical context and insights into trends?
- What internal and external factors might influence your findings?
Check out this example from the New York Times about Hurricane Harvey.
- What techniques did they use to add context to the data?
- What worked well?
- What did you find challenging?
- What made you curious to learn more?