The Data Equity Framework

Author: N/A

Publisher: We All Count

Publication Year: N/A

Summary: The following article discusses how anytime data is involved, decisions are being made and those decisions have consequences. Equity needs to be at the forefront, and this 7-step framework is a systematic approach to doing so. Each step has equity-impacting decision points that must be evaluated. These steps are 1). Funding (this is the stage that defines power and the relationship between data and money. It is where stakeholders and investors define equity in terms of their project goals); 2). Motivation (where your definition of equity is applied to the project goals); 3). Project design (where research questions and methodologies should be planned in intentional ways); 4). Data collection & sourcing (it is necessary to engage with data instead of extracting it with assumptions); 5). Analysis (where perspectives are often embedded into models); 6). Interpretation (which is an inherently subjective process. The nature of the two previous stages calls on transparency and explanation to gain trust and credibility); and 7). Communication and distribution (where you can simply and confidently explain how equity standards have been upheld throughout the processes. Begin with discovery, or thinking about your project in this context, and move to an exploration of the issues that arise as well as solutions. Then implement the system and sustain it).