Want to solve data governance failures? First, move past nonsensical advice

Neil Raden Profile picture for user Neil Raden December 7, 2023
Data governance might be necessary - but it's anything but easy, and it's never complete. A recent post grossly oversimplified the solution - time for a rebuttal.


There is a limit to how much nonsense I can handle in my email, and it was breached last week, when a Substack newsletter boldly announced, “Unraveling the Complexity: Understanding Why Data Governance Programs Fail."

I won't mention the source so as not to embarrass the guilty party. Still, the first clue was that the article listed five “common reasons” governance initiatives fail along with solutions to each, all in an exiguous six hundred words. I doubt I could “unwrap the complexity” of tying my shoes in six hundred words. I categorize content like this as claptrap.

Claptrap: The word comes from the idea of "a trick to 'catch' applause," and its meaning evolved to mean "showy, cheap talk" and, to some degree, "nonsense." Definitions of claptrap: pompous or pretentious talk or writing. Synonyms: blah, bombast, fustian, rant.

To make matters worse, the five reasons are so generic that they could be applied to any IT or business transformation initiative. The solutions aren't even solutions, merely suggestions of what the solutions should be.

So, I’ll take them one by one.

Data governance plays a pivotal role in the modern business landscape, ensuring that organizations can effectively manage, protect, and derive value from their data assets. However, despite the increasing awareness of the importance of data governance [SOURCE?], many programs fail to deliver the expected results [SOURCE?]. In this article, we will delve into the common reasons behind the failure of data governance programs and explore potential solutions to overcome these challenges.

First, the unsupported claims:

"Pivotal role" - Does it? Data governance is just a component of data management, every one of which is pivotal. What is particularly more pivotal about it?

"Modern business landscape" - What exactly is that? In my experience, there isn’t just one.

"Increasing Awareness of the importance of data governance" - Google Trends would reflect otherwise. Besides being only modestly rising for five years worldwide, the topic only gains substantially due to interest in China, where we can be sure it is derived from a desire to “effectively manage, protect, and derive value from their data assets,” but for mostly oppressive and nefarious aims.

1. Lack of Clear Objectives and Alignment with Business Goals: One of the primary reasons for the failure of data governance initiatives is the absence of clear objectives and a strategic alignment with business goals. When organizations fail to define specific, measurable, and achievable objectives for their data governance programs, it becomes challenging to demonstrate value and secure ongoing support from stakeholders.

This is an excuse for every failure. A strategic alignment with business goals: 

Specific, measurable, and achievable objectives for their data governance programs.

These metrics tend to be incomplete, contrived and, for the most part, unknowable when initiatives are STRATEGIC. Even the eventual "stakeholders" are hard to define, and data governance can never play a central role in executing a strategy. At best, it is a tool to lubricate the process.

Solution: Establish a strong connection between data governance efforts and overall business objectives. Clearly communicate how data governance aligns with the organization's strategic goals and how it contributes to improved decision-making, compliance, and operational efficiency.

That's not a solution; it's just some aspirations without explanation either 1) how that would solve the problem or 2) how one can establish, communicate, and align. Overall business objectives from a governance program may only be in theory. Aligning with strategic goals may not even be the objective, as specific tactical or emerging issues drive the effort. How governance contributes to decision-making, compliance and operational efficiency may only be understood, if it happens at all, after the fact.

2. Inadequate Leadership and Stakeholder Engagement: successful data governance requires strong leadership and active engagement from key stakeholders across the organization. When leaders fail to champion the cause of data governance or when stakeholders are not actively involved, it can result in a lack of commitment and ownership, ultimately leading to program failure.

This old chestnut appears in every post-mortem. Leadership from key stakeholders is often ephemeral: they are there to defend and support the cost and disruption of the effort at the beginning, then quietly recede until the program succeeds in some way or join the chorus of blame-storming afterward. Leadership from key stakeholders is rarely an antidote to failure.

Strong leadership and active engagement from key stakeholders across the organization

What's the relationship between "key stakeholders" and "stakeholders?" It's likely that the key stakeholders are the ones behind the initiative, but how can there be alignment between them and the rest of the stakeholders, presumably everyone?

Solution: Appoint a dedicated data governance leader or team with the authority to drive the program. Foster a culture of data stewardship and ensure that key stakeholders understand the benefits of data governance for their respective areas. Regularly communicate the progress and successes of the program to maintain interest and support.

"Data governance leader or team with the authority to drive the program" - Often, this is the project Crumple Zone, where difficulties find someone to blame. To avoid blame, these leaders often impose overly strict interpretations of the governance principles, confusing lack of support and stagnation. Data stewards, an idea that popped up about twenty years ago, do not enjoy an excellent reputation as their roles are too narrow to allow the program to be agile.

Agility also contributes to strategic alignment -- how well your strategies are reflected in your business operations. Without agility, your organization can't consistently carry out a new strategy without an extended period of change. Indeed, you can only achieve strategic alignment if changes to the strategy occur slower than the organization can respond.

This alignment, or lack of it, can be seen in the organization's operational or front-line activities. Indeed, divergent agendas and miscommunication between those working on an organization's strategy and those carrying it out operationally are chronic problems. These disconnects can hinder executive leadership's access to information about what's going on and their ability to effect change in organizational behavior when they see the need.

In addition, when data governance is applied to large enterprises' relational data, stewardship is a much less complicated job. Today, with orders of magnitude more data from more types and sources of data, and often streaming faster than the infrastructure can integrate it, stewardship is a poor solution for governance.

Foster a culture of data stewardship

What is a culture of data stewardship, and what methods would you employ?

"Culture" - Here is the first occurrence of the word culture. In every failed initiative, the culture issue rises to the top of the list of critical causes. What is culture?”   If you asked someone in IT what part of the company they represent, they would likely say IT or something like it. From the perspective of the IT organization (or whoever is the primary contractor and driver of the project), culture is defined as everyone else, the "business, “ or, even more condescendingly, the “users.” If you asked anyone not in IT the same question, they would not say, "I’m ‘the business,'they would identify with their roles and placement in the organization.

Insufficient Data Quality Management: Data governance is intrinsically linked to data quality management. Poor data quality can undermine the effectiveness of governance initiatives, leading to inaccurate insights, compliance issues, and decreased trust in data.

DQM is tricky to implement - possibly more troublesome than governance. Medicine is intrinsically linked to chemistry, but that does not guide how to practice medicine well. The recent emergence of AI-driven DQM solutions is partially helpful. Still, the dissonance of semantics across the sea of data and the ambiguous nature of human language renders DQM a pre-condition for governance bordering on the ridiculous.  

Solution: Implement robust data quality management processes, including data profiling, cleansing, and monitoring. Establish data quality metrics and regularly assess the quality of critical datasets. Educate data stakeholders on the importance of maintaining high data quality standards.

It's not a solution, just an aspiration. With this advice, has the author given you any direction on how to proceed? Where is the unraveling of complexity?

Resistance to Change and Cultural Barriers: Resistance to change is a common hurdle in the success of data governance programs. Organizations may face cultural barriers, where employees are reluctant to adopt new processes or technologies associated with governance.


Why are people resistant to change? For a different explanation than the obscure "cultural barriers," see my previous article, Want to get digital transformation right? Address what's left behind.

Solution: Prioritize change management strategies that address cultural challenges. Provide comprehensive training programs to help employees understand the benefits of data governance and how it aligns with their day-to-day responsibilities. Foster a culture that values data as a strategic asset.

Two words people with established work practices, processes and, additionally, purpose and esteem in their roles shudder at are "change" and "management." The author is partially correct in raising cultural challenges, but it is more complex than that.

People develop a sense of identity and belonging within their workplace framework, which is deeply intertwined with their tools, practices and processes. These cultural elements serve as anchors that provide stability, security, and a sense of continuity. Therefore, when confronted with the prospect of change, individuals may experience anxiety, uncertainty, and a fear of losing their identity. Often, they manifest these fears as passive resistance, creating obstacles and delaying or derailing progress. The fear of leaving something behind can be rooted in resistance to relinquishing power dynamics or social hierarchies. Change may challenge existing power structures and re-distribute resources, authority, or privileges.

5. Inadequate Technology Infrastructure: Successful data governance relies on the right technology infrastructure to support data management, integration, and security. Insufficient or outdated technology can hinder the implementation and sustainability of data governance programs.

"Data governance relies on the right technology infrastructure" - "Oh Lord, won’t you buy me a Mercedes-Benz.” Janis Joplin. So, in addition to DQM, governance relies on the right infrastructure. This simplistic view that an organization is organized is an oxymoron. Enterprises often comprise various businesses, legal entities, jurisdictions, and regulations. Organizational Drift arises from mergers and acquisitions, joint ventures, and dynamic supply chains. All these changes render simple, high-level schemes for governance unworkable and create considerable liability to an organization.

Legacy data adds to the challenge. Coupled with inadequate security are last-generation solutions to metadata, effectively rows and columns that must be queried and often joined in a relational format. This approach is generations old and needs to be revised for the requirements of a digital organization.

Solution: Invest in modern data governance tools and technologies that align with the organization's needs. Ensure that the selected tools can support metadata management, data lineage tracking, and security measures. Regularly assess and update the technology stack to keep pace with evolving data governance requirements.

Sure, all good advice, but unraveling the complexity of it all is an NP-hard problem.

Data governance is a complex and ongoing process that requires a strategic approach, leadership commitment, and active engagement from all stakeholders. By addressing the common pitfalls such as unclear objectives, inadequate leadership, poor data quality, resistance to change, and outdated technology, organizations can enhance the chances of success in their data governance initiatives. Embracing a holistic and well-structured approach to data governance will pave the way for organizations to unlock the full potential of their data assets and drive informed decision-making in the digital age.

No direction.

My take

Data governance can only be a partial process. It requires trade-offs and ruthless prioritization; leaders must pick the issues with the most significant centrality to the organization's strategies and provide the most protection against danger while ensuring the organization can be as effective and competitive as possible. The challenge? The cadence of technology innovation surpasses most organizations’ ability to implement each new or improved technique before the next one arrives.

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