NEAIR 2018: The Importance of Data Quality, Data Governance, and Data Sharing

Institutions of higher learning continue to move rapidly toward data-informed decision making, and this movement was strongly reflected at the 45th annual North East Association for Institutional Research conference. The City of Bridges played host to almost 400 representatives from over 200 institutions and associations to better understand methods and best practices for Creating the Bridge to Data-Informed Decision Making.

A few major themes came to light during the conference that highlighted specific areas in which institutional research plays an especially important role. These themes include data quality, data governance, and data sharing. While these concepts are interrelated, and each is critical to the success of an analytics-driven approach to decision making, their relative importance can vary depending on the phase of analytics initiatives. How they vary can be illustrated using the HelioCampus delivery model.

For instance, while data quality is always a fundamental basis of how institutional research supports university missions, the presence of high-quality data is sometimes assumed, and can be an issue that may not be given sufficient attention given the high workloads seen at most institutions. An analytics initiative is the perfect opportunity to review and identify potential weaknesses in data infrastructure, access methods, and accuracy. Involving subject matter experts in various functional areas improves data quality and stakeholder buy-in…two areas that can lead to an unsuccessful initiative. Intentional effort early in the process pays huge dividends in maximizing the success of a project! This focus on data quality and how it can be addressed early in the implementation cycle was the focus of the talk given by Krisztina Filep of University of Massachusetts- Amherst and myself.

Similarly, data governance can be a topic that fills an institution with dread. However, a methodical approach to understanding and identifying data owners and stewards, and the source and meaning of various information on campus, is one of the most important components of success for analytics. While the conversations involved can often be difficult and time-consuming, the outcome is improved communication and a better understanding of what data are available, who has access to it, and how it can be used in the push to student and institutional success. These issues, and the process and outcomes of a successful data governance structure was the focus of a discussion led by Elijah Earl of Ithaca College.

Finally, data sharing can be considered the culmination of the early phases of an analytics initiative. Many universities engage in data analytics projects with a strong focus on data science and predictive analytics, but these can best be achieved after the data are well understood, and this can only occur using an appropriate model of sharing the data so that obvious patterns can be discerned and understood. An excellent example of this sort of open approach to data sharing was provided by Ron Nowaczyk, President of Frostburg State University, in a recent webinar hosted by Inside Higher Ed.

While the triumvirate of data quality, data governance, and data sharing can be daunting in isolation, remember that they are the foundation of success in data analytics projects. Without them, success is uncertain. We hope this post leaves you with a better understanding of the HelioCampus perspective to these areas.