Introduction to Data Sharing

What is data sharing and how can it be used to improve the lives of individuals, families, and communities?

Data sharing is the practice of providing partners with access to information (in this case, administrative data) they can’t access in their own data systems. Data sharing allows stakeholders to learn from each other and collaborate on shared priorities.

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. These data fields include personal identifiers, such as name, birth date, social security number, or an encrypted “unique ID” that is used to link or join records at the individual level.

Data sharing efforts are known as:

  • Integrated Data Systems (IDS)
  • Data Hubs
  • Data Collaboratives
  • Data Intermediaries

Where does the work begin?

To ensure the benefits, limitations, and risks of data sharing and integration are carefully considered, ask three simple questions at the outset of your effort.

Is it legal?

Is it ethical?

Is it a good idea?

Use Cases for Linked Data

AISP recommends a developmental approach to data integration, beginning with basic data sharing and building with complexity as use cases are successfully used to improve policy, practice and outcomes. Explore the following examples.

Early Childhood Iowa uses its integrated data system to better understand early childhood service utilization and the early childhood workforce. Data sources included public health, education, and human services data. Initial efforts focused on determining an unduplicated count of children in care from birth to age 5 across the state, and found that 73% of children had at least one center-based experience during the year before kindergarten entry. Importantly, the analysis revealed significant gaps for vulnerable children, particularly those in rural counties. Analysis also found shortages in both the quantity and quality of the early childhood workforce, with staffing challenges being particularly acute in rural counties, which comprise 89% of Iowa counties. This project was an important precursor to receiving a Preschool Development Grade, Birth-5 grant in 2019.

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The Housing Advisory Board of Charlotte-Mecklenburg, with support from Mecklenburg County Support Services, used the county’s integrated data system to better understand families experiencing housing instability and homelessness. Analysis found a disconnect between students identified by schools as experiencing homelessness (using McKinney Vento records) and children and youth identified as literally homeless in an emergency shelter, transitional housing facility, or unsheltered location (using Housing Management Information Systems [HMIS] records). Some individuals were not identified as experiencing homelessness by the school system, but had, in fact, experienced homelessness in an emergency shelter. This discrepancy was communicated to the county, local providers, and the school district; as a result, additional social workers were placed within the emergency shelter system to identify children for and connect them to services.

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The Miami-Dade IDEAS Consortium for Children used their integrated data system to ​map aggregate outcomes of early childhood education that better inform decision-making at local agencies, including resiliency mapping by census tract to identify areas of persistent need and areas where children are outperforming socioeconomic expectations. The Consortium determine​d that while 83% of children entering kindergarten had a preschool experience, countywide, there are significant opportunities to increase access to high-quality preschool programs, as only 31% of preschool programs are licensed. Using preschool attendance data and K-12 data, analyses also found that children who consistently attended preschool scored higher on math and reading assessments in preschool and in kindergarten, especially for children living in census tracts with higher concentrated disadvantage. These analyses have supported work to improve early childhood program attendance.

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Caliornia’s Health and Human Services Agency (CHHS) partnered with the USC Children’s Data Network (CDN) to conduct a record reconciliation that linked and organized administrative, individual-level client records across eight major CHHS programs (31+ million records prior to deduplication) and generated an encrypted master client identifier for interagency use. This record reconciliation supports everyday operations as well as the development of longitudinal, cross-sector evaluation and research that facilitates a holistic view of client experiences. These efforts have spurred the development of a secure, cloud-based research enclave for hosting record-level research data sets and accompanying linkage keys. Once operational, this environment will provide carefully controlled, role-based access to analysts within CHHS.

In the longer term, the goal is to develop protocols that, with necessary approvals, will give external university-based and other research partners access to curated data sets and statistical resources within this analytic environment. It is anticipated that this secure platform will advance rigorous evaluation, improve the reproducibility of research, create efficiencies in data management, and further the engagement of university-based researchers with government. Additionally, this research data hub will enhance record security and client confidentiality through data access and security protocols that can be more carefully audited.

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The City of Philadelphia, in partnership with the Penn Child Research Center at the University of Pennsylvania, used its CARES integrated data system to inform how Philadelphia could best leverage revenue from its newly established soda tax to expand universal pre-k offerings. This is an example of how data integrated at the individual level can be aggregated and mapped geographically to drive resource allocation to neighborhoods and families with complex needs. The city was also able to build on this initial success and execute a separate legal agreement allowing trusted practitioners access to information about families who might benefit from the new pre-K slots so that those most in need could be connected directly to services.

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After the passage of Chapter 55 legislation in Massachusetts, multiple state agencies collaborated to use integrated data to better understand trends in opioid-related overdose and death. Their efforts resulted in a more holistic view of those affected by the public health crisis and improved their ability to target resources and interventions, leading to promising reductions in opioid-related deaths in 2019.

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Steps to Effective
Data Sharing

Start with these tips and explore Introduction to Data Sharing to learn more. Do some foundational reading to dig deeper.

  1. Start with a strong vision, mission and purpose.

  2. Identify and regularly seek input from a core group of stakeholders that can inform approach, processes and policies.

  3. Spend time thinking through the data that will be needed to conduct high interest use cases that can lead to action.

  4. Craft a legal framework, including data security approaches, that facilitate data access and use.

  5. Develop a data model and a technical approach that can support your use cases.

Establishing trust with data owners and providers requires continual communication and reassurance that their data is being used securely, and in the interest of their programs.

Learn more about the basics of data sharing.

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