AISP Toolkit Feb25 2025 - Flipbook - Page 6
Data integration is the process of bringing together data from different
sources, which often includes identifiable information (e.g., name, date of
birth, SSN) so that records can be linked at the individual level.
Data sharing and integration involve significant privacy risks, and all
data use should be carefully considered to ensure that sharing is legal and
ethical, with a purpose that can be linked to action.
Why Data Integration + Racial Equity?
INTRODUCTION
Cross-sector data sharing and integration enable the use of multiple sources of information to
better understand individual, family, and neighborhood experiences and conditions over time.
With more cross-sector data, we can often better capture both the causes and impacts of
complex social issues and improve programs, policies, and funding approaches to build stronger
communities. Yet, the way that cross-sector data are used can also reinforce legacies of
racist policies and produce inequitable resource allocation, access, and outcomes.
We understand structural racism as the normalization and legitimization of
historical, cultural, institutional, and interpersonal dynamics that advantage
Whites, while producing cumulative and chronic adverse outcomes for people of
color. Embedded within structural racism is institutional racism, the ways policies
and practices of organizations or parts of systems (schools, courts, transportation,
etc.) create different outcomes for different racial groups (see Terms).
Black, Indigenous, and people of color as well as people living in poverty are often over-represented
within government agency data systems, and disparate representation in data can cause disparate
impact.2 Laws, policies, business rules, and narratives are permeated by structural racism, which is the
root cause of the racial disparities evident in system outcomes. Such disparities are often sterilized by
well-intentioned names (e.g., “disproportionate contact” in the legal system or the “achievement gap” in
education) that hide the social consequence of structural racism: that, as a group, Black, Indigenous,
and people of color in the United States have worse outcomes in many human service system outcome
measures regardless of socioeconomic status.3 And yet, many agency solutions and data initiatives are
largely disconnected from this root cause, and the “hunt for more data is [often] a barrier for acting on
what we already know.”4
2 Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. Calif. L. Rev., 104, 671.
3 Hayes-Greene, D., & Love, B. P. (2018). The Groundwater approach: Building a practical understanding of structural racism.
Racial Equity Institute.
4 Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Wiley.
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