You Can’t Have Digital Transformation without Data Governance

min read

If digital transformation is enabled by data and analytics and if data and analytics requires data governance, you can’t have digital transformation without data governance.

Abstract data visualization. Multicolored circles and squares of data transformed and shaped into a circle at right.
Credit: ArtHead / © 2024

Colleges and universities have been creating digital transformation (Dx) offices and initiatives over the past few years. EDUCAUSE defines digital transformation as "a series of deep and coordinated culture, workforce, and technology shifts that enable new educational and operating models and transform an institution's operations, strategic directions, and value proposition."Footnote1

The promise of a unified, enterprise-wide technological landscape that supports an institution's community and culture is highly appealing, especially to decision-makers and individuals who continue to experience the daily pains of siloed units, data, and reporting. Yet, achieving this promise is not without its challenges. Moving any higher education institution toward a collaborative data effort and an integrated data and analytics platform requires convincing staff, faculty, and leadership that change is needed, that the recommended solutions will result in the sought-after change (typically detailed in an institutional data and analytics strategy), that the institution has the ability to change, that executives support the change, and that the move will benefit staff, faculty, and students.Footnote2 This is no easy lift, to be sure.

The capacity of Dx to revolutionize the way most higher education institutions currently operate is nothing short of extraordinary. EDUCAUSE identifies six Dx capabilities—including data and analytics, which requires a robust data governance program (see figure 1). Anyone who has set out to establish a data governance program knows that the journey is not for the faint of heart, that these programs are very much needed, and that they require a dedicated team to implement. As Melissa Barnett, the first data governance manager for Georgia State University, has explained about data governance: "It's not sexy like analytics. . . . But [data] governance is the foundation. You wouldn't want a house without a foundation. It's not the thing you think about all the time, but it is important, and it needs to be there."Footnote3

Simply stated: If Dx is enabled by data and analytics and if data and analytics requires data governance, you can't have digital transformation without data governance.

Figure 1. Dx Capabilities
Inner circle: Dx capabilities. Ring of circles around it: Strategic Innovation | Driven by institutional strategy and goals, focusing on institutional differentiation, sustainable and strategic innovation, and a willingness to adopt new directions. Data and Analytics | Relies on analytics and other forms of evidence to inform and adjust institutional course and enjoys a culture of trust supported by accountability and data. Flexibility and Agility | Able to quickly adjust strategy in response to changing circumstances and new opportunities. Institutional Alignment | Focuses on institution-wide goals and cross-organizational alignment and collaboration. Diversity, Equity, & Inclusion | Committed to a broad range of concerns that include DEI issues in investments, curriculum, technologies, and the workforce. Transformation of Work and Skills | Focuses on developing the workforce to adapt to rapid, ongoing changes and meet emerging needs.
Credit:  Susan Grajek, "Higher Education in 2023 and Beyond: Grand Strategies for Post-Pandemic Grand Challenges" (EDUCAUSE, March 9, 2023), slide 45. Reprinted with permission.

So, What Exactly Is Data Governance?

As data governance practitioners, we get asked this question all the time. Our definition? Data governance is an interdisciplinary practice that leverages an organization's people, processes, and technology to advance a robust data culture in which data is trusted and is a valued institutional asset. Data governance coordinates across the functional areas of an institution and across the technical domains of data management to balance and maximize data quality, security, availability, and value for an organization.

The DAMA-DMBOK (Data Management Body of Knowledge) 2 Wheel (see figure 2) best illustrates this coordination.Footnote4 The DAMA-DMBOK 2 Wheel specifies ten knowledge areas of Data Management:

  • Data Architecture
  • Data Modeling & Design
  • Data Storage & Operations
  • Data Security
  • Data Integration & Interoperability
  • Document & Content Management
  • Reference & Master Data
  • Data Warehousing & Business Intelligence
  • Metadata
  • Data Quality

In any organization, some of these areas have a longer and stronger history than do others. Significantly, Data Governance serves as the hub supporting and connecting the ten knowledge areas.

Figure 2. The DAMA-DMBOK 2 Wheel
Circle cut into equal slices. Center of circle: Data Governance. Slices: Data Architecture; Data Modeling & Design; Data Storage & Operations; Data Security; Data Integration and Interoperability; Document & Content Management; Reference & Master Data; Data Warehousing & Business Intelligence; Metadata; Data Quality.
Credit: Copyright © 2017 DAMA International. CC BY-ND 4.0

Likewise, organizations vary in whether they have a formal data governance program coordinating these ten knowledge areas or if, instead, the ten areas informally and unevenly govern the organization's data. When present, a formal data governance program serves as a blueprint to guide development and decision-making around an institution's data landscape.

Each of the ten DAMA-DMBOK 2 Wheel knowledge areas has a specific data goal. For example, data security ensures that data is adequately protected, whereas data warehousing and business intelligence enables data analysis and insights. Data governance programs operate at the intersection of functional business areas and the ten knowledge areas of data management to improve the quality and security of the institution's data landscape while simultaneously strengthening the institutional data culture through strategic and ethical data utilization.

Data governance, therefore, exists across the institution's landscape rather than within a department or functional area. Because of this extensive coverage, and because data governance is often an added or retrofitted responsibility in many higher education roles, implementing and managing a formal data governance program often feels like a Sisyphean struggle.

Why Data Governance Is a Challenge to Implement

Data governance involves numerous challenges. Among them are some common misconceptions.

Data governance is a one-time project.

By its nature, data involves many aspects, from its sources to its integrity to its access. As these aspects are constantly evolving, improvement can be made in different ways, and prioritization of the aspects can change. While specific improvements can be made through the assistance of formal projects, the governing of data lends itself to continuous improvement and continuous delivery to strategically address current and future needs and goals of an institution.

One individual can do it all!

There may be recognition of the benefits of data governance, but questions arise. Where should we start? Who should lead the effort? The legal office, the IT department, institutional research, or some other area? Data touches many areas. Who has the time to coordinate with the many stakeholders? Do we need to create a chief data officer or another position? These difficult questions can cause stakeholders to rationalize why data governance should be another area's responsibility. The reality, of course, is that data governance is everyone's job: we all touch data in some way. But institutional leaders have to decide who will lead data governance and where representatives will come from.

We already have data governance.

"We have data entry standards." "Our departments share their data with someone when asked." "We have an information security office." "We do a 'good job' of managing our data." These are statements you might hear at your institution.  Without pain points or a champion who sees the benefits of a comprehensive approach, developing the broad support that is necessary for a successful data governance initiative may be difficult. Executive-level support can convince other leaders that improvements must be made at the institution. Only a formalized methodology will include the necessary level of coordination to realize the full potential of data governance.

Our campus is not ready for data governance . . . or is it?

At the same time, there can be a hesitation to begin. Typically, elements of data management, such as those mentioned above, are in place. These are excellent building blocks! An important first step is assessing what's in place and then asking where to go from there. For an institution to realize the benefits of data governance, the most important point is to begin. As conversations about improvements take place, an openness to change will be needed, and we all know change can be difficult.

Despite all of these challenges and misconceptions, if an institution seeks to move the needle on Dx, the time and effort to invest in data governance is undoubtedly needed. Waiting or putting off the effort will not make the process any easier.

Why Data Governance Is a Challenge to Implement . . . But Absolutely Necessary!

By now it should be clear that establishing (and maintaining) a data governance program is seriously hard work. But doing so is a must for any higher education institution that relies on, or strives to rely on, data and analytics to make decisions rapidly regarding, for example, the strategic plan, the academic curriculum, student success, and business processes. Data governance not only enhances institutional trust in and the ability to utilize data but also provides the rules by which data is created, accessed, used and analyzed, stored, and destroyed or archived.

Imagine, for a moment, that you have come into a substantial amount of money and want to build your dream house. You wouldn't hire an architect and contractors and tell them to just begin building. You need a plan, a blueprint. The plan likely is the result of many painstaking and detailed conversations with your architect about what you want and how you want your dream home to look, feel, and function. Only once you approve the blueprint will the ground be broken and the foundation poured. The blueprint serves as a guide for (and your agreement with) the architect and contractors to create and build your dream home to your specifications. Data governance serves as an institution's guide and agreement regarding its data management. Additionally, like dream homes, data governance programs are not one-size-fits-all. Blueprints and interior design plans are created for the needs and goals of a given individual and not for a neighbor's vision of a dream home. Similarly, an institution's data needs are unique, and its institutional culture must be taken into consideration when planning a data governance program.Footnote5

With agreed-upon guidelines in place, all community members know what to do in each situation and are less inclined to make things up on the fly. Trust in how the data is handled, in the quality of the data, and in the resultant decisions based on the data and analytics is, therefore, built on a robust data governance program. That data is based on people and their daily experiences. Knowing that one's data is handled in a trustworthy way is also worth its weight in gold because once the trust of community members is lost, the institution will have a very difficult time getting it back.

We don't deny that data governance is detailed, abstract to many, and a huge lift. At the same time, it is a sound investment of time, energy, and focus. With data governance, higher education leaders know that the data is available and that they can rely on it to make needed decisions. We echo AIR-EDUCAUSE-NACUBO's Joint Statement on Analytics, which encourages all higher education institutions to not wait but to invest now in the resources to make sure the foundation on which our decisions rest is solid.Footnote6

Sign Up My Institution! Where Do We Start?

We all start at different places. Your data governance program might have a visionary, long-term roadmap. You might instead be working on a more detailed, operational level with a step-by-step plan. Or you might be tackling the implementation of a new enterprise resource planning (ERP) or customer relationship management (CRM) system. In any scenario, you've probably recognized the need for data governance to help with the data work. Getting started involves five steps.

1. Assess your institutional data landscape.

The journey toward data governance begins with taking a close look at how your institution manages its data. You need to ask the right questions: What kinds of data does your institution use? How does the institution integrate and report on this data? What issues have you faced with the data? Don't forget to check if there are any established policies or practices already in place for managing different aspects of data governance, such as policies for responding to data quality issues, approving data access, and handling changes to application data configurations. Most importantly, ask who the key stakeholders are and how they impact or are impacted by data governance. These discussions and discoveries will prepare you to answer the most important question: "Why does your institution need data governance?" Establish that as the starting point for your governance efforts.

2. Cultivate data governance support.

We recommend that following this assessment, you create a data governance executive governing group. This step is about securing a commitment from the highest level of the institution and about rallying support, both of which are important for the success of data governance initiatives. Engage executive leaders to champion data governance efforts, use concrete examples to demonstrate the tangible value of a data governance framework, and most importantly, show how the absence of data governance can lead to data misunderstandings and financial losses, among other impacts. This executive governing group will be the safety net that prevents you from making costly mistakes.

3. Appoint data governance roles.

With strategic oversight in place, the next move is to form a cross-functional team and select people for specific roles. In every organization, individuals should be dedicated to functions for handling data, refining definitions, aligning data usage with the goals of the business, enforcing the rules and policies, maintaining high-quality data, and managing the technical aspect of storing and sharing data safely. However, confusion about responsibilities leads to the misconceptions that everyone is a data steward or that data stewardship is the responsibility only of the IT department.

Roles fall into three categories:

  • Governance and Policy Setting: This is about creating the blueprint for how data is used and making sure the use is in line with what the organization is trying to achieve. These big-picture roles usually fall to those at the top and are commonly called Data Owner or Data Trustee.
  • Policy Execution and Quality Assurance: Once the policies are in place, someone needs to make sure they are followed—and that's where this role comes in. People in this role ensure that data policies actively guide how data is managed day-to-day. This role, commonly referred to as Data Steward, is usually within the business unit. People in this role "steward" a particular data domain, such as HR, Finance, or Student Records, and work across departments to keep data accurate and secure.
  • Technical/Functional Management of the Data: The maintenance and operational side of things includes keeping data safe, making sure it can be shared securely, and integrating it into the organization's systems. These can be technical roles such as Database Administrator and IT Security Officer. Or they can be functional-area users with administrative privileges such as Data Custodian and Data Manager.

These roles may go by different names depending on an institution's culture and blueprint, but all of them are about making sure that data is handled securely and is following established rules and policies.

Having a clear structure is key to making these roles work well. Many organizations struggle with putting data governance into practice every day, a problem that emphasizes the importance of creating stewardship roles with well-defined expectations and duties.Footnote7

4. Define policies, processes, and training.

Defining clear policies, processes, and training programs is another critical recommendation. This involves establishing policies for data sharing and handling, designing processes for change control and access rights, and conducting regular audits to ensure compliance with both internal policies and external regulations. Training programs for data stewards and other key roles are essential to provide them with the knowledge and skills needed to fulfill their responsibilities effectively.

5. Utilize tools and establish metrics.

The implementation of data governance cannot be successful without the right technology tools. Tools for cataloging data, creating data dictionaries, managing approval processes, and capturing data quality errors before they occur are critical. In addition, establishing key metrics to measure the overall value of the data governance initiative is very important.

To build a strong foundation for data governance, start with the five steps noted above:  (1) take a close look at how data is handled within your organization, (2) set up a committee to oversee governance, (3) form a team from different departments to work on data governance and define the roles involved, (4) establish policies, processes, and training, and (5) choose the right tools and metrics to measure success (see figure 3).

Figure 3. Five Steps for Getting Started with Data Governance
5 circles, each one pointing to the next: Assess Your Institutional Data Landscape | Cultivate Data Governance Support | Appoint Data Governance Roles | Define Policies, Processes, and Training | Utilize Tools and Establish Metrics.
Credit: Edlira Stefani © 2024

This strong foundation not only builds trust in the way data is used but also makes sure your organization is ready to tackle the challenges that come with digital transformation, proving that data governance is a must-have in today's digital age.


The concept of Dx has been gaining traction for the past decade and, understandably, gained considerable momentum during 2020. While Dx-specific offices have been cropping up in higher education, so have offices dedicated to data and analytics. While these offices may have different names and reside in different places in the organizational chart, data governance is usually found as a responsibility somewhere within them. Although it may seem a large jump to tie the two concepts together, let alone say that Dx is essentially dependent on data governance, we see a strong connection.

As you dive deeper into the  culture, workforce, and technology shifts put forth by EDUCAUSE, you can also look for signals at your institution.Footnote8 As you look through the EDUCAUSE list of possible signals, two things are worth noting. First, a strong data governance program (even a newly formed program) checks off many of the signals, some of which are littered throughout this article. For example, workforce shifts are happening with the creation of new titles such as Chief Data Officer and Data Governance Manager. Data governance provides shifts in technology by creating (or at least supporting) an institutional data and analytics strategy that can guide decision-making. Cultural shifts—such as a new reliance on data and analytics to adjust institutional directions due to an increased trust in data—are also direct outcomes of a data governance program. Data governance itself is thus a form of Dx that may be happening on your campus right now.

Second, data and analytics is one of the six core capabilities of Dx identified by EDUCAUSE. In order for any data and analytics program to thrive, data governance is a must. It is the foundation of all larger data management initiatives. Can you get reports without data governance? Yes. Can you have a data warehouse without data governance? Yes. Can you integrate various systems, set up models, get metadata, and even secure your data without data governance? Yes to all. However, without the central hub of data governance, the implementation of your other data and analytics initiatives will definitely have problems in many, if not all, of the following areas: consistency, quality, transparency, accountability, privacy, security, completeness, reliability, timeliness, and accuracy.

This brings us full circle, back to Dx and the core capability of data and analytics. If we agree that Dx needs data and analytics to be successful and that data and analytics needs data governance to be successful, we can arrive at the logical conclusion that you can't have digital transformation without data governance.

If data governance is under your purview or you are interested in starting a program at your institution and need some help, please join the EDUCAUSE Data Governance Community Group, where there are plenty of other data governance professionals willing to assist you.


  1. Susan Grajek and Besty Reinitz, "Getting Ready for Digital Transformation: Change Your Culture, Workforce, and Technology," EDUCAUSE Review, July 8, 2019. Jump back to footnote 1 in the text.
  2. Achilles A. Armenakis and Stanely G. Harris, "Reflections: Our Journey in Organizational Change Research and Practice," Journal of Change Management 9, no. 2 (2009). Jump back to footnote 2 in the text.
  3. Jacquelyn Bengfort, "Q&A: Melissa Barnett Wants to Build a Solid Data House," EdTech, February 2, 2022. Jump back to footnote 3 in the text.
  4. S. Earley, D. Henderson, and L. Sebastian-Coleman, eds., The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) (Bradley Beach, NJ: Technics Publications, 2017). Jump back to footnote 4 in the text.
  5. Jason F. Simon and Melissa D. Barnett, "Beyond Tools and Technology: Why Culture-Focused Leadership Matters to Successful Data Governance," EDUCAUSE Review, October 24, 2023. Jump back to footnote 5 in the text.
  6. AIR, EDUCAUSE, and NACUBO, The Joint Statement on Analytics, updated August 2022. Jump back to footnote 6 in the text.
  7. Andrew White and Ted Friedman, A Day in the Life of a Data and Analytics Steward (Stamford, CT: Gartner, July 28, 2022). Jump back to footnote 7 in the text.
  8. Malcolm Brown, Betsy Reinitz, and Karen Wetzel, "Digital Transformation Signals: Is Your Institution on the Journey?" EDUCAUSE Review, May 12, 2020. Jump back to footnote 8 in the text.

Todd Barber is Executive Director of Enterprise Applications and Data Services at the University of Tennessee Health Science Center.

Melissa Barnett is the inaugural Data Governance Manager at Georgia State University.

Rachel Groenhout is Director of Data & Change Management at Colby College.

David Schaefer is the former Director of the Business Intelligence Team at Miami University.

Edlira Stefani is Executive Director of Data Engineering and Operations at Bentley University.

© 2024 Todd Barber, Melissa Barnett, Rachel Groenhout, David Schaefer, and Edlira Stefani. The content of this work is licensed under a Creative Commons BY-ND 4.0 International License.