Heather Krause, Founder of We All Count, spoke on the Foundations of Data Equity during our Accelerate Speaker Series in ResultsLab’s Impact Collective community. 

In this blog post, we share highlights from the session around what data equity means, why ‘data is objective’ is a myth, and how you can begin to build a more equitable data practice.

 

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What is Data Equity?

“How to use quantitative data so that it aligns with the intended experience of the people you care about.”

Let’s break that down a bit.

Quantitative data is the numerical data, the numbers, the things we can count. This is most commonly where the false belief that data is objective comes in, and why this definition of Data Equity focuses on quantitative data.
Intended experience in this case, is the purpose of your research. What’s the question you’re curious about that you’re trying to answer with data.
People you care about are the lived experiences, communities and world views that you want to center on. Whose perspective do you need to keep coming back to? There is no right answer here, and it will vary from project to project, organization to organization.

Why the Belief ‘Data is Objective’ is a Myth

Now that we have an understanding of what data equity is, we need to understand that ideas like –  ‘data is objective’, ‘numbers don’t lie’ or ‘tools used for math are value neutral’ – are all a myth.

We want to think data is objective, but we need to realize that we are making hundreds of subjective choices throughout a data project, figuring out ‘what to choose is the heart of data equity.’

 

 

Understanding Types of Choices in a Data Project

Choices we don’t know we’re making: For example, when we look at the average classroom size, we can get two different answers that are both correct, depending on the perspective we chose to count our numbers from. If we chose to center the perspective on the teacher, we get one answer, if we chose to center the perspective on the students, we get a different answer. Both are mathematically correct, but we made a subjective choice on whose perspective to center on – and likely a choice we didn’t know we we’re making. Learn more on ‘Not Your Average Average’ here.

 

Choices we know we’re making, but don’t know it’s an equity choice. For example, when we choose to suppress data that is ‘not statistically significant’. In this scenario, we know that we are choosing to categorize a small sample size as ‘not statistically significant,’ but we might not be aware of the equity issues this can create. Read more on the equity issues when using ‘not statistically significant’ here.

 

Choices we know we’re making, we know it has equity implications, but we don’t know how to make the choice in the way that supports our equity goals. For example, when we design a research question we know we are making choices to craft this question, and we know this will guide the following activities in the project, so there are equity implications that could follow. However, there are slight variations in the way a question is asked, which can be problematic. How we chose to frame a question can put the onus to change on the wrong subject, creating a misalignment with our equity goals. The way we frame questions can shift and influence ultimately, who is the expectation to change on. For example, do we expect an individual to change, or the system/environment that surrounds the individual? Read more about Framing Research Questions that Reflect Who is Expected to Change.

 

 

6 Steps You Can Take Right Now

Whether you work for a grassroot organization in a rural community or you’re the director of business intelligence for a corporation, you can apply these six steps to your work.