Why You Need to be Disaggregating Your Data for Racial Equity

When working with organizations, we often see leaders with good intentions make a simple mistake: taking their data at face value instead of looking deeper into the numbers. But when you take your data at face value, this can often lead to incorrect and counterproductive conclusions. 

Consider an organization seeking to improve the graduation rates of high school students in a specific county. Imagine that this organization looks at their raw data and sees that graduation rates have improved since beginning work in that county. If this is the extent of their analysis, the organization may be satisfied and call it a job well done. 

It is entirely possible, however, that overall graduation rates are only a part of the story. Even with improvements in the whole population, it is likely that some groups (eg. women, white students, etc.) are improving while others are staying stagnant or even declining. Here lies the solution: disaggregation of data sets. 

What is Data Disaggregation?

Disaggregating data means taking a single measure — like “Number of program participants” or “Infant survival rate” — and splitting it into multiple measures that reflect particular characteristics or subpopulations. Characteristics are things like race, gender, and socioeconomic status. Subpopulations are things like regions, counties, and neighborhoods. There are endless ways to disaggregate your data. Ultimately, through disaggregation, you are digging deeper into your data to unlock insights that accelerate progress and maximize funding dollars for impact.

To get started, here is a list of ways that you may want to disaggregate your own data: 

• Race, 
• Sex,
• Gender, 
• Socioeconomic status, 
• Disability status,
• The intersection of any of these categories

As implied by the final category listed above, this work is rarely straightforward, and a deeper breakdown will almost definitely be necessary. For example, this article by PolicyLink argues that broad racial categories often do not accurately represent people’s actual lived experiences. In response, the article proposes identifying different ethnicities and cultures in addition to racial categories.

Why Should I Disaggregate My Data? 

First and foremost, if you were to disaggregate almost any data trend nationwide or locally, you’d be guaranteed almost 100% of the time to see a racial disparity in the data. Without this racial equity lens, it is impossible to understand the whole story, know where to dig deeper, and ultimately determine the underlying causes of the inequities. Without this context, our progress on community wellbeing becomes stagnant. Our uninformed actions may actually cause more harm than good or widen existing opportunity gaps.

Let’s return to the example of high school graduation rates. If the organization monitoring graduation rate had broken down its data by statistically significant groups, they may have found that the numbers were different for teens belonging to different racial categories. This information would have allowed them to develop and target their strategies toward the most impacted groups, thus accelerating their impact on the overall average.

How Do You Get Started?

If you want the full story, you must disaggregate your community indicator data. But how can you get started?

Every organization needs specialized software explicitly made for this type of work. Your chosen software system should allow you to disaggregate a single metric into several contributing metrics and allow you to compare all metrics on the same graph. In addition to tracking data effectively, you must also set up your data collection methods to fit your new efforts. For example, you should find systems that allow you to easily collect customizable demographic data about your program participants and then organize your participants into sub-groups. Measurement isn’t enough in and of itself. It will also be helpful if your system offers strategies, insights, and processes to help you improve your metrics.

Clear Impact Suite is one such system available to you. It offers data disaggregation, accountability tracking, and a simple process for “turning the curve” on your racial equity metrics.

Finally, when integrating disaggregation into your performance and impact reporting, you should remember that this strategy can not solve all of your problems. To really accelerate your progress, we recommend using the eight core strategies of success from Social Sector Hero – How Government and Philanthropy Can Fund for Impact. Data disaggregation is the focus of chapter five.