Creating a Shared Language and Alignment in Using Data in a Network or Collaborative

For fifteen years, IP3 has worked with place-based, multi-sector collaboratives around the nation. In that time, we’ve watched both emerging and experienced collaboratives wrestle with and align around data. Their reasons are as varied as their geographies—to better share data, measure progress, assess their community, advocate and fundraise, etc. Various data types can be used to assess, align, and evaluate progress when a network collaborates to improve health and well-being. Using data to assess communities and inform community change work is important, and it’s equally important to create alignment across the community and project stakeholders when exploring, using, and talking about data. 

What we see as a consistent blocker to collaborative data use is a lack of shared language around data to effectively align and communicate as a collaborative. Often during meetings and discussions, each person around the table holds a different idea and underlying assumptions about data and measurement in general, as well as what data they are about to discuss. Having shared language helps focus conversations, align partners, clarify data use and goals, and prevent miscommunication.

Establishing shared language and definitions for different kinds of data ensures clarity, efficiency, and alignment across partners and stakeholders. Below, we share a starter list of data types, descriptions, and examples of how each type can be used in a collaborative network. While there is certainly overlap between these data types, having a basic language and definition can help add clarity to collaborative discussions. 

Note: Equity is not called out as a specific category but is assumed to be an integral part of all types of data (e.g., health outcomes and service utilization data disaggregated by race, income, geography, and other factors to understand and address disparities.)

1. Evaluation Data

Program data used to track progress, expected program outcomes, and the impact of the network’s initiatives, programs, and interventions. Evaluation data help determine program effectiveness, as well as identify areas for improvement. Examples:

• Qualitative feedback from participants

• Benchmarking against goals or external standards

• Network meeting attendance and engagement levels

• Partnership effectiveness metrics

• Collaboration impact assessments

  
  

2. Assessment Data

Data needed to understand conditions in the catchment area or community, and the population served; helps identify community needs and inform decision-making and community investment. Examples:

• Impact assessments

• Community health assessments

• Census data, including demographics

• Primary data collected through surveys, focus groups, and/or community conversations

• Asset mapping to identify existing community strengths

  
  

3. Resource & Referral Data

Data about available services, providers, and resources, including their capacity, eligibility criteria, and accessibility. Examples:

• 2-1-1 directories or social service referral platforms

• Service availability and waitlists

• Data on service utilization and trends

• Mapping of referral pathways

  
  

4. Stories & Lived Experience Data

Narrative and qualitative data capturing the experiences of individuals and communities to provide context beyond quantitative measures. Examples:

• Community member testimonials and case studies

• Oral histories and storytelling projects

• Qualitative interviews, conversations, or focus groups

• Participatory research methods

  
  

5. Community Voice & Engagement Data

Primary data (quantitative or qualitative) collected from residents to ensure that initiatives are informed by those they impact. Examples:

• Surveys on community priorities and perceptions

• Public forums and participatory decision-making sessions

• Crowdsourced data and community-reported concerns

  
  

6. Policy & Systems Data

Information about policies, regulations, and systems that impact the work of the network. Examples:

• Policy landscapes and legislative tracking

• Data on local, state, and federal funding allocations

• Regulatory impact assessments

  
  

7. Asset Mapping & Capacity Data

Data that identifies the network’s existing strengths, services, and resources. Examples:

• Directory of community organizations and their capacities

• Workforce and leadership capacity data

• Network mapping 

  
  

8. Financial & Funding Data

Data about funding sources, financial sustainability, and budget allocation. Examples:

• Grant and funding opportunity databases

• Community participatory budget data

• Return on investment (ROI) analyses

  
  

9. Data Sharing & Warehouse Data (Data about Data)

Information cataloging existing data sources and establishing protocols for sharing data across the network. This can include data that merges insights from multiple sectors (e.g., healthcare, housing, education, employment) to provide a holistic understanding of community well-being; and policies and structures that ensure responsible data collection, storage, sharing, and use. Examples:

• Inventory of available datasets within the network (e.g., patient records, program data, case management data)

• Data-sharing agreements and governance structures

• Common data standards and interoperability frameworks

• Centralized or federated data warehouses

What we've shared here is a starting point, and the categories above are not exhaustive, nor are they mutually exclusive. Our hope is that this list will provide a starting point for creating a common language around data and data use that can help networks and collaboratives jump-start conversations and alignment to ensure data catalyzes positive change.