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Indigo Faculty and Staff Guide to Conducting Data Quality Evaluations | Erin Mills Town Centre
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Higher education data quality evaluations, synonymous with data audits, are becoming increasingly important to institutions aspiring to provide evidence of student learning and success to a variety of stakeholders. This evolution is spurred by increased accountability and realizations that decision-making requires insights gleaned through data, preferably collected over time, and triangulated across multiple methods. The audience for this publication includes program coordinators, department heads, data analysts, and institutional leadership across different institutional types and sizes. Individuals with varying levels of data expertise can also benefit from this guide as the information is designed for cross-functional teams and extends beyond an individual role.
A major goal of this guide is to provide resources and direction for how to facilitate data quality evaluations at the readers' respective campuses or organizations. Another, equally important, aim is for institutions to build capacity for data collection, communication of results, and associated best practices, including establishing key partnerships that will support current and future data-focused projects. Data sources and technology platforms may have increased substantially over time. However, the method by which campuses connect information, particularly student-level data, in consistent fashion is far less developed. This publication addresses some of the prevailing challenges in continuing to move the needle on data audit best practices. It also provides recommended activities to advance readers' understanding and application of successful data quality evaluation strategies.
Higher education data quality evaluations, synonymous with data audits, are becoming increasingly important to institutions aspiring to provide evidence of student learning and success to a variety of stakeholders. This evolution is spurred by increased accountability and realizations that decision-making requires insights gleaned through data, preferably collected over time, and triangulated across multiple methods. The audience for this publication includes program coordinators, department heads, data analysts, and institutional leadership across different institutional types and sizes. Individuals with varying levels of data expertise can also benefit from this guide as the information is designed for cross-functional teams and extends beyond an individual role.
A major goal of this guide is to provide resources and direction for how to facilitate data quality evaluations at the readers' respective campuses or organizations. Another, equally important, aim is for institutions to build capacity for data collection, communication of results, and associated best practices, including establishing key partnerships that will support current and future data-focused projects. Data sources and technology platforms may have increased substantially over time. However, the method by which campuses connect information, particularly student-level data, in consistent fashion is far less developed. This publication addresses some of the prevailing challenges in continuing to move the needle on data audit best practices. It also provides recommended activities to advance readers' understanding and application of successful data quality evaluation strategies.
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