Book Demo

Using Data

as a Tool

for Equity

Innovare leverages a human-centered approach when developing data visualizations and supporting education leaders and organizations to visualize, analyze, and use data to positively impact the communities they serve.
Ground Your Work in the Race Equity Cycle:
Understand the tactics organizations should adopt to become more equitable
Adopt an Equitable Data Cycle:
Embed equity throughout your data lifecycle
Apply Equity Awareness in Your Data Visualizations:
Ensure your data visualizations are developed with empathy and communicate an equitable lens of your stakeholders
I think about the faces behind the numbers, to ensure we use our data to tell a story our communities would be proud of, comfortable with, and empowered by.
Nick Freeman
Co-Founder & President

Ground your work in the

Race Equity Cycle

Internal and external data analysis is imperative to building a Race Equity Culture, as it builds transparency, and allows employees who enter their work with a race equity lens to interact and engage. Equity in the Center developed the Race Equity Cycle framework to guide organizations towards a more equitable action in how they function and operate.
Reflect on your own organization:
Where would you place your organization within the Race Equity Cycle?

ADOPT AN EQUITABLE DATA CYCLE

UPenn’s AISP posits that “building a data infrastructure without a racial equity lens and understanding of historical context
will exacerbate existing inequalities among lines of race, gender, class, and ability.” To avoid that pitfall, reflect on the following questions ahead of planning for your next data cycle:

Planning
  • Do we have a plan for embedding racial equity in every stage of the data cycle?
  • Have we sought out community interests?
  • What framework can we use to help clarify how to improve policy, services, and outcomes?
Data Collection
  • Do we have a secure way of collecting data?
  • Do we have a plan for collecting data that respects the rights and privacy of these individuals?
  • Are we planning to collect both qualitative and quantitative data? If not, why?
Data Access
  • What data should be open, restricted, or made unavailable?
  • Have we reflected on why we’ve decided what access to provide for particular data?
  • Have we considered the intended or unintended consequences of releasing data?
Statistical Tools
  • Do we have a plan to identify, log, and explain sources of error?
  • How are we communicating the potential assumptions being made within the data?
  • Should we have a 3rd party assess the algorithms were using to check for bias?
Data Analysis
  • Are we considering individual, community, political, and historical contexts of race to inform our analysis?
  • Have we considered which data to include or exclude?
  • Do we have a plan to involve multiple perspectives in the analysis or interpretation of the data?
Reporting
  • Have we considered the audience and method of communication?
  • Have we identified which data will be highlighted?
  • Have we considered the readability and accessibility of the report?

To apply equity awareness in your data visualizations, consider the following:

  • Using people first language
  • Using a variation of colors
  • Changing colors or orders of labels to avoid gender or racial stereotypes
  • Using terms besides “other” to more accurately identify the individual or group
  • Using icons and shapes that represent all individuals to which the data refers
  • Using empathic images and language to connect the audience to the data
  • Creating visualizations that are different from the standard you typically use

About Innovare

Innovare is empowering leaders in the education ecosystem with the essential tools they need to impact the people they serve. Our app Inno™ aggregates siloed data in education organizations into one personalized dashboard, and then guides leadership teams to develop strategies, manage key initiatives and measure impact in real-time.
We combine data and strategy into one intelligent tool and provide dedicated support to guide leaders to drive continuous improvement and achieve results. And now, our Innoverse™ community of changemakers connects diverse leaders to one another to share best practices, foster social innovation, and achieve collective impact.
Sources:

1 Awake to Woke to Work 2 Centering Racial Equity Throughout Data Integration 3 Principles For Advancing Equitable Data Practice
4 Applying Racial Equity Awareness in Data Visualization

1 Awake to Woke to Work 2 Centering Racial Equity Throughout Data Integration 3 Principles For Advancing Equitable Data Practice 4 Applying Racial Equity Awareness in Data Visualization

Data + Equity Checklist:

A resource to apply equity awareness in your data visualizations.
Concept
Questions to Reflect + Act On
Notes
1. Planning
1. Do we have a plan for embedding racial equity in every stage of the data cycle?
2. Have we sought out community interests?
3. What framework can we use to help clarify how to improve policy, services, and outcomes?
2. Data Collection
1. Do we have a secure way of collecting data?
2. Do we have a plan for collecting data that respects the rights and privacy of these
    individuals?
3. Are we planning to collect both qualitative and quantitative data? If not, why?
3. Data Access
1. What data should be open, restricted, or made unavailable?
2. Have we reflected on why we’ve decided what access to provide for particular data?
3. Have we considered the intended or unintended consequences of releasing data?
4. Statistical Tools
1. Should we have a 3rd party assess the algorithms were using to check for bias?
2. Do we have a plan to identify, log, and explain sources of error?
3. How are we communicating out the potential assumptions being made within the data?
5. Data Analysis
1. Are we considering individual, community, political, and historical contexts of race to
   inform our analysis?
2. Have we considered which data to include or exclude?
3. Do we have a plan to involve multiple perspectives in the analysis or interpretation of the
   data?
6. Reporting
1. Have we considered the audience and method of communication?
2. Have we identified which data will be highlighted?
3. Have we considered the readability and accessibility of the report?