2024 EDUCAUSE Top 10
#2: Driving to Better Decisions

Improving Data Quality and Governance

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Driving to Better Decisions is issue #2 in the 2024 EDUCAUSE Top 10.

Person surrounded by graphs and charts. #2
Credit: Zach Peil / EDUCAUSE © 2023

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"If you're going to center yourselves in equity and if equitable outcomes are the metric you're going to focus on, then data and technology systems are the drivers that get you there. Obviously, mindsets are important. And how folks come together and agree on key goals and then putting in the services and supports that are needed to meet those goals—those are all important too. But almost none of that happens without the technology and data supports, because that's what grounds the work and then supports and allows you to do it amidst a complex environment with lots of moving pieces."

—J.B. Buxton, President, Durham Technical Community College

Higher education has never had so much data and such useful technology to help inform decision-making. So why are we still struggling with the basics of data governance and quality?

Culture is a big barrier at many institutions. The data literacy of an institutional community often lags leaders' expressed commitment to analytics. Many administrative and academic stakeholders don't understand how to use data effectively in decision-making, and many haven't bought into the value of data to inform decisions. Data ownership is another cultural barrier, with departmental staff often viewing themselves as owners, rather than as simply stewards, of the institutional data they collect and manage. These attitudes impede efforts to define, clean, protect and integrate data—all critical tasks that must precede any uses that can be made of it.

Time is another factor. There's so much data available, and the questions that one can ask and have answered are endless. But instructors and administrators don't necessarily have the time to get access to data and to get to the stage of using data to answer the questions relevant to them.

Technologies, risks, and policies related to data are changing rapidly. Such ongoing change demands ongoing efforts to maintain stable technological and institutional policy environments. Institutional leaders' efforts to develop and improve privacy-related policies are hampered by rules and by a motley agglomeration of laws across global regions, countries, and U.S. states. This swirl affects how institutional constituents can assert their privacy rights and how decision-makers can collect and use data. Technologies and practices to integrate, manage, and visualize data continue to advance, which is both exciting and expensive. AI will shape not only how technologies collect data but also how technologists and users use it or derive intelligence from it.

The Promise

Data can help leaders understand and address a lot of the challenges they're facing with finite and often shrinking resources and revenue. Change initiatives and experiments to improve teaching and learning models, to reach new populations of students, and to operate in remote and hybrid workplaces are underway at many institutions. Data can help people better understand the ways in which their institution is changing, how successful those experiments are, and why and for whom. Data can also help leaders look for opportunities to do less with less and to clarify when expedience is better than excellence, rather than exhort exhausted staff to do more and better with less. Data can help managers and leaders make difficult decisions by using evidence, rather than conflicting gut feelings, about what to stop and what to celebrate and build on.


A top-down approach will drive culture change. If campus leadership and trustees value data, use it themselves to inform decisions, and ensure that their teams have the time to develop the processes, policies, and subsystems to support a data-focused culture, the institutional culture will shift accordingly. If not, data governance will top out within departmental pockets of excellence.

Listen to your people. Leaders need to listen to and work with instructors, students, administrative staff, and other individual stakeholders to be sure data and analytics tools are actually useful to them.

No heroes, please. Data governance initiatives that depend on individual "heroes" to bridge gaps will make initial progress but will fail to scale.

It's not about technology. Process, culture, and outcomes—rather than a particular technology solution—need to drive these initiatives.

Risk aversion is risky. Institutions that focus too much on avoiding risks can miss opportunities that could easily outweigh those risks.

The Key to Progress

Institutional leaders must treat data as a true strategic asset in the same way that they treat the institutional facilities, buildings, and physical infrastructure. Both data and infrastructure need ongoing maintenance and attention. But there are major differences. The cycles of overhauls and replacements of data and the associated technologies and policies are much shorter. The value proposition of a new building, with state-of-the-art classrooms, labs, and equipment, is much more defined and easier to understand than a proposal to develop a data dictionary, an ETL process, or a data warehouse.

From Strategy to Practice

What You're Saying

"As we implement our data warehouse and enhance our IR capabilities, we understand the importance of having a 'single truth' of data.  As a result, we are committing significant time and effort to our data quality and governance process."

"Our current technology and data environment has been described as a 'digital jungle.' Data quality and governance will be key to our long-term success, and we are just starting this journey."

"This goes along with the data warehouse, as especially cross-functional data access falls into the realm of data governance.  We have been managing data quality but now need to expand more broadly into governance of access."

"We have a campus-wide governance committee, but we're stalled on step 1 (data classification) and are not even at the stage of data definitions."

Solution Spotlights

"Universitat Oberta de Catalunya in Spain is deploying an enterprise strategy about data. Data democratization."

Enric Espejo

"Fairfield University has two major projects running in parallel: a Workday SIS implementation project and a Data Warehouse (DW) project. The data conversion phase of WD SIS reviews data quality. The DW project has data definition, classification, lineage, and transformation. New cross-functional data governance is forming to create a single source of truth, maintain data integrity and reliability, and increase access to data."

John Lombardi

"The University of Alabama at Birmingham is deploying a digital marketplace, which will include our systems of record and premium services, to drive usage of existing services and create awareness of new capabilities."

Rachel Moorehead

What You're Working On

Comments provided by Top 10 survey respondents who rated this issue as important

Data definitions, classification, management, and quality

  • Created the university's first-ever data classification policy.
  • Having discussions surrounding this topic and practicing good communication. Ensuring Central IT obtains proper permissions before distributing data. Aiding in an institutional data dictionary to help high-level administrators understand particularities of our data and the definition of data elements.
  • Collaborative data governance policies and procedures. Customizing a data classification system; collecting data sources from all campus departments.
  • There are a multiplicity of places holding the same data. We're working on deduplication mechanisms as well as strategies to keep data current.
  • Data governance is now mature, and the data integration initiatives are forcing improvement in data quality and understanding.

Governance framework and policies

  • Creating data governance subcommittees and working groups.
  • Development of a more robust data governance capability as a joint effort between IT, institutional analysis, and the privacy office.
  • Established and empowered cross-institutional IT governance and data governance committees to build and enforce commitments to leveraging standardized and accurate data resources to meet clear institutional objectives.
  • Formalized a data steward council and launched several leadership dashboard initiatives.
  • In our medical education program, we have instituted a data governance committee just for our school's education data. This is not institutional data governance, but there is a high level of engagement from leadership because they value the role the data plays in accreditation and operations.
  • Launching a data governance committee to address discrepancies in how certain data changes are requested and approved, particularly around the university catalog; this is in conjunction with implementing new degree audit and course catalog management systems.
  • Overall IT governance has been established with an executive steering committee and subcommittees that include finance, systems architecture, enterprise (student and administration) systems, and data governance. Engaging with functional owners of data to determine and implement data stewardship, retention, privacy, etc.
  • Recent formation of an Enterprise Data Governance Council (EDGC).
  • Emerging data governance framework combined with new institutional policy seeking to democratize data, adding in guiding practices and ethical frameworks for appropriate use.
  • Developing a three- to five-year institutional strategy. Investing in next-generation data management and data governance frameworks and tools.

Staffing and restructuring

  • Combined institutional research and business analytics into one team and hired an inaugural chief data officer with focus on data governance and quality.
  • We have established an enterprise information management program and a privacy program. We have hired a chief privacy and data officer.
  • In an environment of cost-cutting and staff layoffs, when ICT was restructured with a brief to cut costs and reduce headcount but make some provision for strategic growth into the future, the only new position that was created was mine, focusing on data (governance, architecture, quality, and integration).
  • We have a business intelligence team that focuses on data visualization and implementing management reports based on the different datasets on which our dashboard is based. We are currently recruiting a data engineer who will be the bridge between the ICTS service and the BI team. The data engineer will help digitize the various data flows that feed the dashboard.
  • We have hired a chief data officer to work more on governance, vision, etc.

Technology solutions

  • Implementation of EAB Edify and re-envisioning our institutional research operational model.+
  • New ERP for finance is coming next year, which will allow for much better data quality on the finance side. ERP for alumni and fundraising just launched, and it will also improve data quality.
  • Part of the Workday process has resulted in maturing our data governance processes and setting a goal to increase capacity for data literacy into the functional areas.
  • Since 2018, the college had been using a modern ERP platform. The platform has led to quality and standardized sources of data and governance.
  • We are about to go live with Workday Finance—moving from a suite of old systems into an ERP to drive much more accurate and timely financial data, which will in turn enable better data-driven decision.
  • We are aiming to leverage a new data management platform in conjunction with our SIS rollout and integration platform, which will hopefully serve as a basis for a broader data management and governance strategy that enables agility along with data integrity.
  • We are building a data lake for aggregating data from multiple sources to provide a central point for queries and analyses.

And more…

  • We are among the most mature in our data governance practice among R1-style institutions, with data stewards and steering committees, enforced by technology such as data integration platforms. We have the ability to automate approvals and provisioning based on the risk level of the request. The data governance program includes academic and administrative data and supports learning analytics programs.
  • We'll add sensitive data handling to the mandatory employee annual cybersecurity training in the fall of 2023.
  • Investing in Tableau more fully for data visualization; growing our data governance committee structure; seeking grants for unified data analytics.
  • Leveraging our contracted software and data partner tools along with central office policies to build a better workflow for information.
  • New School of Data Science, which collaborates with the community on numerous projects.
  • We are using the EDUCAUSE data governance questionnaire with our data management committee to discuss where we are and what functions we should be focusing on for the next year.
  • University system is pooling knowledge to improve all aspects of data and governance to streamline best practices.

Vipin Ahlawat is Director of IT Services, Loughborough University, United Kingdom.

Param Bedi is Vice President and Chief Operating Officer, Bucknell University.

Catherine Zabriskie is Senior Director, Digital Learning & Design, Brown University.

© 2023 Susan Grajek and the 2023–2024 EDUCAUSE Top 10 Panel. The text of this work is licensed under a Creative Commons BY-NC-ND 4.0 International License.