Data governance, as it is currently practiced, is failing. Despite being positioned as essential for data quality, regulatory compliance, and strategic decision-making, data governance often falls short. Time and time again business and data leaders fail in their attempts to implement enterprise-wide governance.

Evidence is abundant—poor data quality continues, data debt expands, and leaders do not engage. Add a lack of clarity around roles and responsibilities and you get the perception data governance is practically ineffective. Rightly, there is growing skepticism about its value.

In the summer of 2024, we (Thomas Redman, John Ladley, Mathias Vercauteren, Malcolm Hawker, Anne Marie Smith, and Aaron Wilkerson) formed a study group to understand why data governance is failing and to propose improvements. This article summarized the results of our initial analysis of “why.” It aims to:

1. Advise Chief Data Officers (CDOs), Data Governors, and business leaders of the issues they face (and offer some sympathy for those encountering difficulties — you are not alone!).

2. Stir the data community to action by encouraging discussion and debate on our analysis and inviting others to help us propose and trial improvements. 

Forces impacting the success of data governance

To understand why data governance efforts often fail, it is essential to consider the forces holding it back (i.e., restraining forces). It is equally important to consider what compels so many organizations to try it, given the low rate of success (i.e., driving forces).

We followed the Force-Field Analysis (FFA) process described by Ladley and Redman to do so.

We made some modifications to adapt the process to our study group’s specific context, ensuring it accounted for the diverse perspectives and practical experiences of our members. By analyzing these forces, as illustrated in Figure 1, we can gain insights into what enables data governance to succeed and what holds it back. A short description of each force follows.

Driving forces

• New regulations mean consistent demand: The evolving regulatory landscape, such as CSRD and the EU AI Act, ensures that the demand for robust data governance controls remains consistent. Compliance is now a non-negotiable aspect of business operations, driving the adoption of effective data governance as a way to meet these requirements and maintain legitimacy.

• External pressures and competitive demands: External forces, such as international competitive pressures and shifting consumer expectations (e.g., privacy concerns in different cultures), compel organizations to enhance their data governance capabilities. For example, geopolitical competition has intensified the need for robust data privacy and security, forcing companies to align their governance practices with intensifying global standards.

• Established materials: Leveraging existing tools, resources, and frameworks allows organizations to build on established foundations, facilitating the implementation of data governance programs. These battle-tested approaches provide organizations with best practices and a knowledge base that can help accelerate data governance initiatives.

• Supportive data professionals: Many data professionals within organizations are increasingly supportive of data governance initiatives. Their engagement is vital for ensuring that governance is not only implemented but also maintained effectively. When roles and responsibilities are clear, data professionals can champion the initiatives, driving their success.

• Business stakeholder awareness: More and more business stakeholders across different levels recognize the importance of data governance, even if they do not always actively support it. The acknowledgment that data governance is necessary for achieving strategic business goals creates an environment where change is possible, even if the willingness to invest time and resources may vary.

Restraining forces

• Knowledge gaps and lack of training: There is a significant lack of understanding among staff and leadership regarding the core principles of data governance. This knowledge gap extends to understanding the value of controls and how they contribute to business performance.

  Without comprehensive training, many practitioners lack the foundational knowledge needed to implement effective governance practices, which leads to resistance and skepticism.

• Overreliance on technology: Many organizations mistakenly believe that technology solutions alone can address their data governance challenges. This overreliance often overshadows the importance of human elements, such as stewardship and accountability. Effective data governance requires a balance of tools, processes, and people to establish true control over data.

• As deployed, rarely works as expected: Even when data governance programs are deployed, they often fail to deliver on their promises. Whether due to poor implementation, lack of resources, or unrealistic expectations, the results frequently fall short, leading to disillusionment among stakeholders and a cycle of failure.

• Misunderstanding and a lack of agreement on what data governance is and is not: A lack of consensus on the definition and goals of data governance contributes to inconsistent applications and unmet expectations. Differences in opinion can result in conflicts over how governance should be implemented, undermining efforts before they have a chance to succeed.

• Over-advertised as easier and more effective than reality: Data governance initiatives are often portrayed as having transformative potential, with promises of quick, clear returns on investment. However, achieving these results is far more complex, and such overselling leads to dissatisfaction and skepticism when these promises go unfulfilled.

• Structural and organizational issues: Many organizations are not inherently structured to manage data effectively. Placing data governance responsibilities within the IT department, for instance, is often a poor fit. These structural misalignments make it difficult to integrate data governance into the broader business strategy, creating barriers to its success.

• People-related challenges: Successful data governance requires not only the right systems but also the right people. Many data governance programs fail because organizations do not have the right champions or the right change agents to drive the initiatives. Issues such as a lack of ownership, misaligned incentives, and the absence of data governance advocates can significantly hinder progress.

• Difficulty in quantifying business benefits: The challenge of quantifying data governance’s benefits is perhaps the most significant barrier. As our study group pointed out, even when the need for governance is accepted, proving its long-term value remains elusive.

  Many stakeholders are frustrated by the disconnect between those who bear the costs and those who reap the benefits, particularly when these benefits are indirect or realized downstream. While operational use cases may offer clear, quantifiable benefits, analytics use cases, for example, require a nuanced approach.

  If, for instance, analytics lead to increased revenue, how do we allocate that success to data governance? This difficulty in quantifying and attributing value complicates the justification for ongoing investment in data governance.

Next steps

The FFA revealed a stark truth — the forces restraining data governance are powerful and diverse. In many cases, they are stronger than the driving forces. Without regulations pushing for governance, many programs would struggle to justify their existence.

If the Chief Data Officer requires justification outside of regulations, they tread carefully due to resistance and ignorance. At the same time, the FFA also provides them with a clear directive: Build on the driving forces and address the restraining forces head-on. Even better, we advise Chief Data Officers to conduct their own company-specific FFAs.

Though we did not include it as a restraining force, we find it disconcerting that many data professionals privately acknowledge the shortcomings of current data governance practices, while continuing to publicly uphold the status quo. This reluctance to challenge existing norms makes improvement more difficult. We urge data professionals to take a more creative and courageous stance in acknowledging the current state and making improvements.

Our study group plans to continue exploring these themes in greater depth. We’re casting a wide net: Should we narrow the focus of data governance to only regulatory data requirements? Is there another “flavor” of data governance? Should we consider a drastic reset? Have we missed something?

At the very least we plan to dig deeper into this FFA and get to root causes. We aim to publish a series of articles that provide actionable solutions. We invite CDOs, data leaders, and the broader data community to join us in this journey—to debate, challenge, and refine our approach to data governance. 

About the Authors:

Anne Marie Smith is a leading consultant and educator in Data and Information Management with broad experience across industries. She is a frequent speaker and

an author on data management topics for a wide range of publications. She has taught numerous workshops and courses in her areas of expertise. Smith holds the degrees of Bachelor of Arts and Master of Business Administration in Management Information Systems, she earned a PhD in MIS and has earned various industry certifications and fellowships.

Thomas C. Redman, “the Data Doc,” is President of Data Quality Solutions. He helps companies and leaders chart their courses to data-driven futures with special emphasis on quality, organizational structure, and analytics. His latest book, People and Data: Uniting to Transform Your Organization (Kogan Page 2023), urges companies to increase the power of their data programs by getting everyone. Redman has a Ph.D. in Statistics and two patents.

John Ladley is a highly experienced practitioner, advisor and advocate for organizations looking for sustainable value from information and data.   His experience and knowledge is balanced between treating data assets as an essential component of modern business and economies, and the practical solution of business problems. Ladley’s books are the authoritative sources for Data and Information Management and Data Governance. He is a recognized authority and speaker on enterprise information management, including data monetization.

Mathias Vercauteren is a trailblazer in the world of data governance with over a decade of expertise transforming organizations. As the president and principal of Data Vantage Consulting, Mathias collaborates with top executives across industries

to implement practical data governance frameworks that work and are widely adopted. Known for his ability to bring order to chaotic data management, he also trains and coaches teams to foster a lasting culture of data literacy and ownership. In addition to consulting, Vercauteren is pursuing an Executive PhD in Data Governance at Antwerp Management School, conducting research that bridges theory and practice.

A sought-after speaker at conferences like DAIA, DGIQ, and EDW, Vercauteren consistently explores innovative ways to leverage data for business success. He is also establishing an international research institute to advance data governance through academic research and practical industry solutions.

Malcolm Hawker is the CDO of Profisee and is a thought leader in the fields of Data Strategy, Master Data Management (MDM), and Data Governance. As a former Gartner analyst, Malcolm has authored industry-defining research and has consulted some of the largest businesses in the world on their enterprise data and analytics strategies. Having served as a Chief Product Officer, Head of IT, and strategic business consultant, Hawker is an industry leader with over 25 years’ experience at the forefront of data-enabled business transformations. Malcolm is a frequent public speaker on data and analytics best practices, and he cherishes the opportunity to share practical and actionable insights on how companies can achieve their strategic imperatives by improving their approach to data management. When not sharing his passion for data or recording episodes of the CDO Matters podcast, Malcolm is an avid hobbyist landscape photographer and lives with his wife and two dogs in a small beach town in Florida.

Aaron Wilkerson has over 17 years of experience in data and analytics, having worked in various industries. He has specialized in building and driving data strategies that help companies leverage their data assets. He also has experience in building and leading teams to implement data solutions that enable data-driven problem solving. These experiences have provided measurable business value to the companies that have leveraged these solutions. Wilkerson has a Bachelor of Science in Engineering degree from Grand Valley State University. Aaron lives in Livonia, MI with his wife and their five children.