Proposal for an Equity Data Framework and Institutional Strategy
This project involved development of an equity data framework for students and employees to identify and better understand equity gaps and make data-driven decisions to advance EDI.
DEI Foundations
4-Year Institution
British Columbia
Evelyn Asiedu
Diversity, Equity and Inclusion Data Analyst
Nathan Bartlett
Data Strategy Manager
Alana Hoare
Associate Director, Academic Planning and Continuous Quality Improvement
Natalie McNichol
People and Culture Advisor
Anita Sharma
Manager, Research Services
Shannon Wagner
Vice-president of Research

This was a highly iterative, exploratory process that built upon internal and external expertise.

Method for the framework involved multiple stages:
- Defined purpose, principle, parameters, and scope.
- Reviewed and built-upon previous institutional research and reports.
- Established the outline for the framework.
- Conducted environmental scan and comparative review of Canadian universities' equity data practices.
- Consulted with leaders at other Canadian universities (e.g., University of Calgary, Simon Fraser University, Dalhousie University).
- Consulted with institutional stakeholders (e.g., Student Union, Registrar, IT Services, Faculty of Student Development, General Counsel and Privacy Office).
- Identified comparators for benchmarking of census results (e.g., StatsCan).
- Presented proposal at TRU's inaugural EDI in Action Conference.


The goal of TRU's Data Equity Fellowship project was to develop an equity data framework for students and employees to:

1. Identify and better understand equity gaps, and
2. Make data-driven decisions to advance EDI.

Lessons Learned

Lessons learned include:
- Collection of equity data is not a straightforward process.
- Many Canadian institutions are working on creating a framework for collection of equity data.
- Requires consideration on multiple levels including federal and provincial legislative environment, as well as local and institutional context and needs.
- Drafting of questions is difficult and needs to consider local context and use of language.
- Data is collected across the institution with little clarity as to an institutional approach for collection and use of equity data.
- Requires a multi-pronged approach due to need for many different types of expertise (e.g., IT, EDI, legal, human resources, Registrar, etc.).
- One of the most important outcomes has been increase in areas/individuals with EDI knowledge.