A diversity of voices, skills, and experiences can lead to project breakthroughs and innovative interventions. As such, teams that are cross-functional (people with different roles in the organization) and cross-organizational (people from different parts of an organization or other organizations) are critical to a data project’s success. 

Include a diverse mix of policy and data experts, along with decision makers and implementers. Some potential project team roles might include:

• State Chief Data Officer or equivalent, such as data administrator or coordinator, open data manager, analytics director, etc.
  
  

• Governor’s office representative, such as policy director, deputy chief of staff, advisor, etc.
  
  

• Decision-maker or program manager from relevant agencies with policy or subject matter expertise
  
  

• Equity or DEI officer from relevant agencies
  
  

• Budget office representative, such as budget director, financial manager, etc.
  
  

• Community partners, such as nonprofits, local academic institutions, private-sector organizations, etc.

Data systems have the power to transform communities, but without a deliberate focus on equity, they can replicate the very disparities they aim to address. By embedding data equity principles throughout the project lifecycle, teams can help mitigate biases and work toward collecting, analyzing, and utilizing data in ways that are fair and inclusive for all people.

Data practitioners can inadvertently exacerbate social, economic, and racial inequities by failing to recognize the equity impacts of their work. It can also lead to misrepresentation of different population groups, poor policy design, ineffective interventions, and an erosion of trust in government institutions.

The modules in this playbook present key questions pertaining to data equity. Thoughtfully answering those questions will go a long way in establishing the foundation for equitable data practices. 

Here are resources to build your knowledge on data equity:

• A Quick Guide to Data Equity: CDO Insights Brief (Beeck Center)

• Toolkit for Centering Racial Equity Throughout Data Integration (AISP)

• The Data Equity Framework (We All Count)

• Data Equity: What Is It, and Why Does It Matter? (Hawai'i Data Collaborative)

• Elevate Data for Equity (Urban Institute)

While building out your data project, make sure to document your processes, decisions, and outcomes along the way. Good documentation strikes a balance that is both thorough and concise, and can:

• Sustain your project’s momentum during times of transition. Capture the evolution of your project, onboard people more quickly, and ultimately sustain the momentum of your work.
  
  

• Allow you to revisit your work and adjust. Revisit decisions, evaluate data collected along the way, and pinpoint opportunities to adjust your approach and fill in knowledge gaps as you work iteratively.
  
  

• Help you replicate similar projects in the future without reinventing the wheel.  Guides future projects and avoid unnecessary duplication of efforts.

When deciding what documentation to keep, ask yourself: what is important to keep, and how does it serve the purpose of reproducing the work? Here are some examples of documentation you might consider keeping on hand:

• • Project work plans

  • Pitch decks

  • Prototypes

  • Wireframes

  • Data audits

  • Community outreach tools

  • Reports

  • Key correspondence that documents decisions, agreements, sign-offs on actions, etc.