Data Visualization: Key Considerations for Effective Data Visuals

When presented effectively, data are engaging, comprehensible, relevant, and meaningful to your data consumers. Data become more than a set of numbers; they are transformed into compelling evidence, a call to action, an answer, or a focused question. Given the potential of data to drive programmatic and systemic improvement, effective data visualization is a must.

Learning Objectives

Participants will

1. increase their knowledge of data visualization best practices
2. apply those best practices to create more effective data visuals
3. understand the benefits of using quantitative and qualitative data visualization to meaningfully communicate early intervention (EI) and early childhood special education (ECSE) data to a variety of audiences

Prerequisite skills

• Familiarity with IDEA Part C early intervention data and/or IDEA Part B, Section 619 preschool special education data
• Some familiarity with data visualization may be helpful but not required
• Some familiarity with Excel may be helpful but not required

Data Leadership Competencies addressed by this content

• FD-6. Is knowledgeable about data analysis methods, their strengths and limitations, and their use in developing data products to meet the needs of intended users including how to use data analysis and presentation techniques to portray data for historically underserved groups appropriately.
• DC-2. Demonstrates the ability to analyze and use data to develop the state performance plan and annual performance report (SPP/APR).
• DC-4. Demonstrates the ability to establish a data culture through
• DC-8. Demonstrates the ability to use data to ensure equitable access, services and supports, and positive outcomes for children and families.
• DC-11. Demonstrates the ability to effectively communicate data to policymakers, OSEP, the general public, and other stakeholders through presentations, websites, reports, etc.
• DC-12. Demonstrates the ability to support local programs and districts to build a culture of data use.

Video coming soon.

Resources

DaSy’s Data Visualization Toolkit
The DaSy Data Visualization Toolkit is designed to help state Part C and Part B staff effectively create and present data visuals. For each data visualization topic, a comprehensive set of resources and information is provided including design principles, data considerations, accessibility tips, general how-to’s, examples, and sample tools.

AISP Toolkit for Centering Racial Equity Throughout Data Integration
The Actionable Intelligence for Social Policy (AISP) is committed to ethical data use with a racial equity lens that supports power sharing and building across agencies and community members. This comprehensive toolkit is designed to help guide partnerships, collaboratives, agencies, and community initiatives seeking to center racial equity while using, sharing, and integrating administrative data. The toolkit also offers examples for positive and problematic practices for centering racial equity across the six stages of the data life cycle.

Data Feminism
Data Feminism is a book by Catherine D’Ignazio and Lauren F. Klein. It takes a critical and intersectional approach towards data justice and examining how power operates, providing the foundation for responsible data use and development of data visualization. The open access edition of this book is available online.

Stephanie Evergreen’s Data Visualization Checklist
This checklist is meant to be used as a guide for the development of high-impact data visualizations.

Urban Institute’s Do No Harm Guide: Applying Equity Awareness in Data Visualization
This guide, and the associated checklists and toolkits, pays attention to the subtle messages that can be conveyed through data visuals. It provides considerations and strategies to help use data more purposefully.

We All Count
We All Count is a project dedicated to increasing equity in data science. The website includes practical data tools an overview of We All Count’s Data Equity Framework, which is a systematic way of looking at data projects through seven stages.

Published February 2024.