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Examples and Models

Quality data sharing looks different depending on who you are, where you are, and the goals of your effort. Below we offer distinctions, considerations, and examples across three dimensions. Click to learn more about each dimension.

Geography

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Management Model

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Core Purpose

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Example Sites

View examples from our national network of Integrated Data Systems (IDS). Each site’s model is displayed according to geography, management, and purpose(s) for data sharing.

Each example for our network of Integrated Data Systems (IDS) is shown in the flow charts below according to geography (state or local), management model (executive, agency, university public partnership, and non profit) and core purpose for data sharing (indicators and reporting; analytics, research and evaluation; and operations and service delivery).

Colorado Data Chart

Indiana

Executive, State

Allegheny County

Agency, Local

Colorado

University Public
Partnership, State

New York City

Executive, Local

Washington State

Agency, State

Baltimore

Nonprofit, Local

To learn more about these and other sites in our network, explore the map here.

Geography

The level of government where data sharing occurs (state vs local)

There are notable differences between state-level data sharing efforts and efforts that are local to a county, city, or region.

  • Staff capacity: On average, state-level sites are larger and are more likely to have dedicated staff to support data privacy, technology, and communications.
  • Funding: Local data sharing efforts generally receive a larger portion of their funding from private philanthropy, and are less likely to leverage federal dollars than state-level efforts.
  • Technology approaches: State-level sites more often build their own data integration infrastructure, while local sites generally use a mix of commercial and custom models. States are also more likely to allow remote access to data, particularly for external users.
  • Legal frameworks: The legal frameworks utilized by state and local efforts are similar but states are also more likely to draw on statutory authority for data sharing.
  • Engagement: Local efforts are generally more confident in their community engagement strategies, and more likely to work closely with advocacy groups to translate findings than state-level efforts.

Management Models

The organization(s) that carry out key data sharing activities.

Where an effort is managed or hosted will have implications for partnership dynamics, as well as legal and technology approaches. Across the data sharing landscape, efforts generally fall into four management models: executive-led, agency-led, university-public partnership, non-profit led.

Four Common Management Models

  • Executive-led: hosted by an executive-level office with close proximity to decision-makers (i.e., Office of a County Executive, Mayor, Governor, or Office of Policy or Budget and Management).
  • Agency-led: hosted by a large umbrella agency (i.e., Department of Health and Human Services, Department of Education) that encompasses multiple programs with proximity to both the people represented within the data and the practitioners implementing services.
  • University-public partnership: hosted by a university, often in collaboration with or in service of government agencies at either the state or local level.
  • Nonprofit-led: hosted by a non-profit agency or backbone organization (i.e., United Way), often with external partners (private, university, agency, or other) performing other key functions/activities.

Each of these models has common advantages and challenges. In some cases, for example, deciding who hosts data integration efforts is simple. If your goal is to support care coordination across a variety of health and human service programs, the umbrella agency that manages the majority of those will be an easy choice. However, many efforts find that decisions about hosting are more complicated, particularly when there are a large number of partners contributing data. In these cases, it may be helpful to consider which partners are best suited to take on three important categories of activities:

  • Facilitating governance (including stakeholder engagement and procedural oversight)
  • Managing technology (including data storage, integration, and access)
  • Conducting analysis (including research methods, tools, and insights)

One organization can manage multiple activities but there are also data sharing efforts that have distributed these responsibilities strategically based on the strengths of partners.

Core Purpose

The reason(s) for data sharing

We find it helpful to distinguish between three core purposes for data sharing—Indicators and Reporting; Analytics, Research, and Evaluation; and Operations and Service Delivery.

While it is possible to design infrastructure that combines several of these purposes, it is always important to differentiate them during design and planning. In most cases, we recommend starting with data sharing and integration work that can be reported in the aggregate and building on early successes toward the much more challenging work of coordinated operations and service delivery at the individual level.

Indicators and Reporting

Data can be summarized and reported at the aggregate—frequently an early step in data integration efforts regardless of management model (though it is especially common in non-profit led efforts). Often used to demonstrate the benefit of data sharing in order to build towards more advanced integration and analytics. Often community-facing and engenders public support.

Analytics, Research, and Evaluation

Data must be curated, shared, linked, and then de-identified for statistical purposes—this is the primary goal/purpose of data integration efforts surveyed, especially executive-led and university-public partnerships.

Operations and Service Delivery

Data must be identifiable and may include case notes to support client level services. Operational data use is more legally and technically complex and therefore less common. It is most common for agency-led efforts, as they often operate a wide range of programs under a single umbrella and generally have the legal authority to contact clients to manage consent-based rules.

Both Analytics, etc. + Operations and Service

Challenging to balance and done only by the most advanced sites, but can be mutually beneficial and built off of the same governance structures (with separate legal agreements and technical processes for each primary purpose).

Indiana Management Performance Hub (MPH)

Executive, State

The Indiana Management Performance Hub (MPH) is a standalone state agency that governs the enterprise-level integrated data system and drives evidence-based decision making across Indiana. MPH was made possible through a 2014 executive order with the collaboration of the state’s Office of Management and Budget and Office of Technology.

Each example for our network of Integrated Data Systems (IDS) is shown in the flow charts below according to geography (state or local), management model (executive, agency, university public partnership, and non profit) and core purpose for data sharing (indicators and reporting; analytics, research and evaluation; and operations and service delivery).

Indiana Data Chart

Allegheny County Data Warehouse

Agency, Local

The Allegheny County Warehouse is hosted by the County’s Department of Human Services, Office of Analytics, Technology and Planning. Data integration capacity drives research and evaluation across key social policy domain areas as well as service delivery and operations for child welfare.

Each example for our network of Integrated Data Systems (IDS) is shown in the flow charts below according to geography (state or local), management model (executive, agency, university public partnership, and non profit) and core purpose for data sharing (indicators and reporting; analytics, research and evaluation; and operations and service delivery).

Allegheny County Data Chart

Linked Information Network of Colorado (LINC)

University Public Partnership, State

The Linked Information Network of Colorado (LINC) is a collaborative partnership between the Colorado Governor’s Office and the Colorado Evaluation Action Lab at The University of Denver. Their capacity for data integration helps strategically target services and benefits to vulnerable populations and identify opportunities to improve services, delivery, and opportunity.

Each example for our network of Integrated Data Systems (IDS) is shown in the flow charts below according to geography (state or local), management model (executive, agency, university public partnership, and non profit) and core purpose for data sharing (indicators and reporting; analytics, research and evaluation; and operations and service delivery).

NYC Center for Innovation on Data Intelligence (CIDI)

Executive, Local

NYC’s Center for Innovation in Data Intelligence (CIDI) is housed in the Office of the Mayor of the City where they primarily perform policy research and evaluations.

Each example for our network of Integrated Data Systems (IDS) is shown in the flow charts below according to geography (state or local), management model (executive, agency, university public partnership, and non profit) and core purpose for data sharing (indicators and reporting; analytics, research and evaluation; and operations and service delivery).

New York Data Chart

Washington State Integrated Client Data Base (ICDB)

Agency, State

The Department of Social and Health Services Research and Data Analysis Division (RDA) developed and maintains Washington State’s integrated client data base (ICDB), which provides research capacity to improve policy and analysis of governement-funded social services and programs.

Each example for our network of Integrated Data Systems (IDS) is shown in the flow charts below according to geography (state or local), management model (executive, agency, university public partnership, and non profit) and core purpose for data sharing (indicators and reporting; analytics, research and evaluation; and operations and service delivery).

Washington Data Chart

Baltimore’s Promise, Youth Data Hub

Nonprofit, Local

Baltimore’s Promise is a nonprofit organization that hosts the Baltimore Youth Data Hub- an initiative focused on meeting the needs of the City’s children, youth, and families in partnership with other City agencies and community organizations.

Each example for our network of Integrated Data Systems (IDS) is shown in the flow charts below according to geography (state or local), management model (executive, agency, university public partnership, and non profit) and core purpose for data sharing (indicators and reporting; analytics, research and evaluation; and operations and service delivery).

Baltimore Data Chart
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