This graph shows data that should be red, data that should be green, and data that I’m not sure about. But it’s a chart, so it is clearly very important.

Practical Data for Nonprofits: Part 1 — A Primer

Jenn Taylor
3 min readAug 8, 2021


I challenge you to count the times in one day you hear or read the word “data” or something related to it. That number won’t be 0. We live in a moment where data is granted a starring role in conversations about politics, work, governance, and our private lives. Many people refer to data when they are really trying to say something is a “capital-T Truth,” or at the very least communicate certainty.

I’m encouraged that we are also living in a moment when people are increasingly vocal about the ethics and transparency of the algorithms that run our lives. Many people are also beginning to understand that the manipulation of data is power.

Even if you are not interested in talking about algorithmic bias, you may well be in a role where you feel powerless against the technologies and demands for data in your life. Just as a foundational ability to read and write are required to participate in daily life, a foundational and practical knowledge of data — where it comes from, how it is transformed, how to interpret it — is increasingly required to navigate much of our world.

Practical data literacy is much more than reading a chart or a graph in a report. It is the ability to think about the chain of actions and tools that led up to the graph being created in the first place and to impact that chain with confidence.

In my work, I see a number of negative consequences that stem from the lack of practical data literacy in the nonprofit sector, not least of which are wasted time and money, high stress, and avoidable inefficiencies that burn people out. I’m on a mission to address that however I can. As a starting point, I want to introduce some core concepts that I find are often missing from our conversations about data in the nonprofit sector.

The Four Pillars of Data Literacy

Practical data literacy starts by understanding four ideas that have nothing at all to do with math, programming, analytics, or anything else that I find nonprofit folks often (wrongly) self-identify themselves as bad at. These ideas are:

1. Human beings take deliberate action to generate, capture, store, use, and interpret data. Each of these steps has a cost in time and energy.

2. Everything is data and we store a lot of it on computers. This does not mean we can easily make use of it.

3. Data is inherently meaningless. The context is what makes it meaningful.

4. Some basic, non-specialist terminology about reporting can go a really long way in creating a data literate organization that uses technology effectively and ethically.

Data is simply one part of the collective way we make meaning. Widespread data literacy is just as important as the widespread ability to read and widespread basic numeracy. These are all just methods of transforming our ephemeral thoughts into a code that can be decoded into meaning by other people. Just as reading as basic literacy — the act of deciphering and making meaning out of patterns of squiggles — is achievable by huge numbers of people, so too data literacy is achievable.

In this series, I will explore these ideas and what they might mean for you as a nonprofit leader, decision-maker, “accidental admin,” foundation program officer, or anybody else who has felt the frustration of “but this data is WRONG and I don’t know why!” It’s more about how we, as humans, see and make sense of the world than anything to do with programming or t-tests, so I hope you’ll read on!

This article is part of a series on data literacy for nonprofit leaders. Its goal is to share terms and concepts that aid in making good technology decisions when you’re not a technology expert (or even if you’re a little bit tech-phobic).

Next up: All Data Comes from Somewhere



Jenn Taylor

Operationalizationer — one who defines a fuzzy concept so it becomes clear, measurable, and empirically observable. Founder @