Data isn't neutral. Once we start to make choices, data quality is affected. The methods we choose, how analysis is approached, in what ways the data is shared and the decisions we make influence data collection and interpretation. Our beliefs, assumptions, knowledge, culture and context can do the same.

"Data equity is a set of principles and practices to guide anyone who works with data (especially data related to people) through every step of a data project through a lens of inclusivity."¹ 

How can we manage and use data responsibly across the research lifecycle? How should we think about the relationship between data and the people being studied?

There is no prescription for practicing data equity or responsible data but there are frameworks and tools we can adopt to encourage intentional, critical thinking and support equity analyses with historically excluded people (for example, Hispanics, persons with disabilities, people whose incomes are below the federal poverty threshold).

This year's IDEA Forum will provide guidance for and spark conversations about equitable data practices that go beyond ensuring demographic questions are inclusive and help improve data quality including:

• Exploring approaches and best practices for understanding how to bring an equity lens to research
• Learning how to do no harm when engaging and measuring people from historically excluded backgrounds as well as telling their data stories.
• Understanding systemic barriers that limit data inclusivity and how data systems can be improved to promote fairness, transparency, and inclusivity.

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¹ “What Is Data Equity and Why Does It Matter? - Data.Org.” Data.Org, https://data.org/resources/what-is-data-equity-and-why-does-it-matter. Accessed 29 July 2024.