At AISP, we help government agencies, university partnerships, and community initiatives share and integrate data to improve outcomes.
Data Sharing vs. Data Integration: What’s the Difference?
DATA SHARING is the practice of providing partners with access to information (in this case, administrative data) that they cannot access in their own data systems.
DATA INTEGRATION is a more complex type of data sharing that involves record linkage, which refers to the joining or merging of data based on common data fields.
Benefits and Risks of Data Sharing and Integration
Done right, data sharing and integration efforts can support communities to better:
- Understand the complex needs of individuals and families
- Allocate resources where they’re needed most to improve services
- Measure long-term and two-generation impacts of policies and programs
- Engage in transparent, shared decision-making about how data should (and should not) be used
Data sharing also comes with serious limitations and risks, so each potential use of data should be carefully considered by all relevant stakeholders. For more on engaging diverse voices and assessing the risk vs. benefit of data sharing, see A Toolkit for Centering Racial Equity Throughout Data Integration.
Purposes & Approaches to Data Sharing
It is generally helpful to distinguish between three core purposes for data integration, each with unique implications that drive the design of systems. For more on how to build an approach to match each of the purposes listed below, see AISP’s Introduction to Data Sharing.
Example: A collaboration across several public and non-profit agencies in a county emerges with the goal of bringing together disparate data on social, economic, housing, safety, and environmental conditions into a comprehensive report on community well-being. The information is made accessible through an interactive online dashboard where it is displayed as statistics tables, maps, and charts that can be explored by custom geographies (i.e., school zones, business districts). Community members and local organizations are able to use and engage with the data to better understand where they live and the trends and patterns that affect their lives.
Example: Federally funded early childhood providers in a state share identified information about the children they serve (i.e., name, date of birth, and address, with their state early childhood agency) with their state Office of Early Childhood. The state agency links these data at the individual level with birth records and records from locally-funded early learning programs to understand important patterns of access and service utilization. De-identified and at the aggregate level, these data allow the agency to estimate how many children are attending publicly funded early learning programs, how many have had no formal early learning experience prior to kindergarten, and how child outcomes vary across these groups. With each additional data set the agency is able to link, new research questions about the childrens’ trajectories will become answerable.
Example: A group of hospitals, service providers, and public agencies in a major city agree to link their data in near real-time to fuel a community information exchange that bridges health and social services. Individual data from multiple agency partners’ systems is integrated to facilitate the sharing for more comprehensive client-level information to care providers. Information viewed by approved practitioners may include health care records on risk assessments or screenings (i.e., biometric screens, depression/anxiety scales), past and future appointments with other providers, and eligibility for social services (i.e., SNAP, TANF). Populating longitudinal personal records enables coordination for improved delivery, deduplication of services, and increased quality of care.
No matter the purpose or approach, we have come to recognize that data sharing and integration efforts commonly grapple with five core components of their work. Click to learn more and access resources on each key topic: data governance, legal agreements, technical infrastructure, capacity, and impact.
We think of data governance as shared “rules of the road” for data sharing, or the policies and procedures that determine how data are managed, used, and protected. There is no one set formula for good data governance but it should be purpose-driven, strategically located, collaborative, and iterative.
Whether data can be shared legally is not a simple question, but depends on why you want to share, what type of information will be shared, who you want to share it with, and how you will share it.
There are a host of technical considerations related to data storage, management, security, linkage, and access procedures that arise with cross-sector data sharing. AISP does not serve as a vendor for solutions or tools, or do any data management or integration on behalf of sites. We help sites consider how best to select technical approaches that match their goals, and how to evolve those approaches over time.
The work of building and using cross-sector data infrastructure is time-consuming, and as relational as it is technical. It requires dedicated staff with a variety of core roles and competencies. At AISP, we help sites think about a start-up staffing structure for their efforts and then support them as they build the political and economic sustainability to grow their model to scale.
Data does not make meaning, people do, and there is absolutely no use integrating data if it will not be used to drive decisions. But how sites leverage cross-agency data to create impact varies widely. AISP helps sites start small for quick wins and steadily build a culture of data use and cross-agency learning to drive policy and program improvement.
To learn more about these five key considerations, explore our Introduction to Data Sharing.