At the intersection of Black and Women’s History Months, it’s a good time to revisit the basics of data equity and intersectionality.
First coined by Kimberlé Crenshaw in 1989, “intersectionality” is a theoretical framework that aims to highlight the converging causal mechanisms that underlie inequality and discrimination. Intersectional analysis on race, for example, holds that race cannot be understood in isolation from other identity categories of inequality such as gender, ethnicity and economic status, to name a few.
Data is playing an increasingly important role in driving and guiding all areas of business, including corporate diversity, equity and inclusion (DEI) efforts. Although progress is being made to address bias in algorithms and other technology development, there is still much work to be done in building equitable data processes across the entire business. It is critical that corporate America takes an intersectional approach to its data practices; failing to do so risks leaving some of its core stakeholders behind and excluding those at greatest risk of marginalization.
So, what do equitable data practices actually look like? Here are four steps to start putting intersectional data practices into action:
1. Implement equitable data practices across the corporation, not just in DEI work.
Center voices by identifying the stakeholders—employees, consumers and partners—who are most impacted by inequality and actively engage them in the data process across all levels and teams within the organization, from product design to policy creation. Working with those stakeholders to then identify gaps and opportunities in data is a critical first step.
2. Develop practices and models of data equity that are unique to your organization.
Once you understand the various identities your organization touches, you can then design data models that are inclusive of the lived realities of those identities. This is often the most demanding step, as it involves reevaluating the most fundamental aspects of your data collection and analysis methods. From making your interview methodology more intersectional to examining which worldviews impacted analysis and outcomes, developing consistent, tailored models based on equitable data practices is crucial.
3. Intentionally promote your data equity practices throughout each stage of the data value chain.
The data value chain is the cycle by which a need for data is identified and a process is implemented and generally follows four major stages: collection, publication, uptake, and impact. Inclusive data practices may require that you make specific changes at various stages of the data value chain based on the unique conditions of a specific project. In order to best identify when changes are needed, it is therefore crucial that your data equity practices are promoted and understood throughout the entire data value chain.
4. Use the insights gained to inform equitable solutions and decision-making.
It’s important that the insights gained are not only used to highlight problems of inequality but also potential solutions. This is where everything comes full circle as leveraging your data to identify equitable solutions requires looking to employees, consumers and partners with expertise in developing solutions and action plans through an intersectional lens. An intersectional approach to data requires thoughtful intent and including the expertise of those at greatest risk of marginalization can help ensure meaningful outcomes and impact.
While we may have two distinct months to celebrate Black and Women’s history, for many people these are not mutually exclusive identities. Intersectionality is an important consideration for corporate America that begins by understanding the intersecting identities present across stakeholder groups and then using that knowledge to thoughtfully and intentionally develop practices and models of data equity that are tailored to the needs of your organization.