Why Data Governance Fails in Many Organizations: The IT-Business Divide

Introduction

Data governance is essential for organizations to manage their information assets effectively, ensure quality, and maintain regulatory compliance. However, the term “asset” here is crucial. Data becomes an asset only when it delivers value through proper use and management.

Without data governance, data stored in systems is simply a liability: It costs money to store and maintain, creates security and privacy risks, and can lead to poor business decisions if it’s inaccurate or misunderstood. Research demonstrates that organizations face significant financial losses from inadequate data quality, averaging $12.9 million annually according to Gartner research (2020). Additionally, when employees lack confidence in data accuracy, they waste substantial time verifying information – up to half their working hours spent “hunting for data” (Redman, 2013).

Despite widespread recognition of data governance’s importance, success remains elusive. Industry research from Gartner (2024) indicates that 80% of data and analytics governance initiatives will fail by 2027, and organizations waste approximately 40% of their analytical potential due to poor data quality and inconsistent stewardship practices (Airbyte, 2025).

In this two-part series, we examine four fundamental gaps that consistently undermine data governance efforts. Part 1 focuses on structural challenges – the IT-business divide and missing governance structures – that prevent organizations from establishing a solid foundation for data governance. Part 2 will explore why even well-designed structures fail without proper accountability mechanisms and diverse organizational capabilities.

The IT-Business Divide

A primary obstacle is the lack of mutual understanding between IT, business, and data roles. A 2024 study by leading data governance experts found that this knowledge gap extends to understanding the value of controls and how they contribute to business performance, with organizations demonstrating an “overreliance on technology” that “overshadows the importance of human elements, such as stewardship and accountability” (Redman et al., 2024).

In many companies, data professionals come predominantly from IT backgrounds, focusing on technical deliverables such as building systems, installing software, implementing tools, and creating dashboards and visualizations. While these technical capabilities are essential, they often overshadow the critical business-oriented aspects of managing data: establishing data governance frameworks, ensuring data quality, assigning clear ownership through stewardship roles (individuals responsible for ensuring data are adequately defined, maintained, and used according to business needs), and, most importantly, aligning data initiatives with strategic business objectives.

For example, an IT team might build a customer data platform that technically works perfectly. It stores data, runs quickly, and produces beautiful reports. However, without business involvement and designated stewards, critical questions remain unanswered: Who decides what counts as a “valid” customer record? What happens when the same customer appears multiple times with slightly different information? Who has the authority to access sensitive customer data, and under what circumstances? Who maintains the business glossary that defines what “customer” actually means in different contexts? Beyond these operational questions, there’s a more fundamental issue: Does this platform actually support the business objectives it was meant to serve? Are we capturing the correct data to answer strategic questions? Are we measuring what matters to the business, or just what’s easy to measure technically?

The Data Management Body of Knowledge (DMBOK), published by DAMA International, makes an important distinction here. Managing data is primarily a technical function – the day-to-day operational work of storing, processing, and maintaining data systems. In contrast, governing data is fundamentally a business function that provides oversight and decision-making authority over how data is defined, accessed, and used across the organization.

Data stewards play a crucial role here by maintaining business glossaries and data definitions, ensuring everyone in the organization speaks the same language about their data. Yet in many organizations, these stewardship roles simply don’t exist – no one is responsible for defining terms, updating definitions as business needs evolve, or resolving conflicts when different departments use the same term to mean different things.

The problem extends beyond missing stewardship roles to a deeper documentation chaos. Organizations often have multiple documents addressing the same concepts, but the language varies depending on which unit you ask, when you ask, and to whom you’re speaking. Some teams call these documents “policies,” while others use terms like “guidelines,” “standards,” or “procedures.” With no clarity on which term means what or whether these documents represent the same authority level.

More critically, no one has the responsibility or authority to define which version is the “appropriate” one. Documents get written – often as part of project deliverables or compliance exercises – but no governance process ensures they’re actually embedded into operations, kept current, or reconciled with other documents covering similar ground. The result is a proliferation of potentially conflicting guidance with no single source of truth, leaving employees uncertain about which direction to follow.

Without active business engagement and these dedicated roles that bridge the gap between technical teams and business stakeholders, organizations struggle to establish who is accountable for data assets, how quality standards should be enforced, and whether data initiatives actually support strategic business goals. Even when technical teams ask the right governance questions, alignment with business objectives only happens through continuous collaboration, ensuring not just that systems work, but that they deliver meaningful business value and enable strategic decision-making.

Missing Governance Structure

When Technical Roles Are Mistaken for Governance

The problem deepens when governance structures exist but fail to fulfill their true purpose. Many organizations have technical data management roles – data managers, database administrators, data engineers – and mistakenly treat these as governance functions. This confusion reflects a broader pattern identified in research: organizations often fail to distinguish between data management (technical execution) and data governance (business oversight) (Khatri & Brown, 2010).

These professionals handle the technical aspects of data: building databases, managing storage, optimizing performance, and ensuring systems run smoothly. However, they typically lack the authority and business context to address governance aspects, such as defining data quality standards from a business perspective, assigning accountability, establishing policies for data use, ensuring compliance, or embedding governance controls into business processes.

The Technical Obligation Trap

Without proper governance, a problematic pattern emerges: Technical teams impose technical obligations on business people, requiring them to validate data formats, approve schema changes, or participate in narrow technical reviews, while the real governance questions go unaddressed. Business stakeholders are involved only in a few steps of the data lifecycle, without understanding the whole picture or having authority over business-critical decisions.

This problem intensifies in organizations operating across multiple countries, where different regions may have legitimate reasons for calculating the same business metric differently (due to local regulations, market practices, or tax requirements). Yet, no governance body exists with the authority to decide whether to standardize, accommodate variations, or establish enterprise-wide definitions with regional exceptions.

When Innovation Gets Blocked

The absence of governance authority also stifles innovation and improvement. When someone identifies problems with data quality, inconsistent definitions, or broken processes and tries to address them, they often find themselves excluded from decision-making to “keep the agenda moving forward.” Without a governance body to evaluate issues objectively and authorize improvements, organizations default to protecting the status quo. Those attempting to raise concerns or drive change get sidelined, while those controlling the narrative – even if they lack real expertise – maintain their position by avoiding transparency and keeping things complicated.

The Real-World Cost

Consider a common scenario: A multinational company launches a sales reporting system without defining what “sales revenue” means across regions. Does it include taxes? Discounts? Returns? Different countries may calculate it differently due to local requirements, leading to conflicting reports. A database administrator might ensure the system captures all fields accurately, but cannot define which calculation represents the business truth. That’s a governance question requiring business authority and cross-regional coordination.

Or imagine deploying a marketing platform that collects customer data across jurisdictions without governance to establish region-specific consent policies, retention periods, or compliance requirements, thereby exposing the organization to significant legal risks.

The Metadata and Business Rules Gap

The governance challenges become even more insidious when organizations produce reports that appear identical in format while concealing fundamental differences in their underlying methodology. In large organizations, data governance issues manifest not merely as insufficient effort but as inadequate structuring of responsibilities, methods, and information flows.

Reports may share the same layout, column headers, and visual presentation, yet rest on entirely different foundations: divergent calculation methods, aggregation rules, source selections, reference populations, or sampling strategies. The DMBOK emphasizes that these discrepancies stem from a lack of metadata standardization and business rule governance. Without documented, shared, and governed metadata and business rules, organizations cannot ensure comparability of results across time periods or between organizational entities.

This speaks to the domains of Data Quality Management and Metadata Management within the DMBOK framework. Without common definitions, explicit quality indicators, and traceability of data transformations, the figures produced can be technically accurate while remaining conceptually misaligned with the original intent or decision-making needs. The numbers are “right” in isolation but wrong in aggregate – a situation that undermines strategic planning and cross-organizational coordination.

When Governance Roles Exist Without Authority

The governance challenges deepen when organizations create the right roles on paper but fail to position them with appropriate authority or influence. Many organizations have established policy, governance, and documentation roles – positions with titles that suggest oversight responsibility. However, these roles frequently suffer from critical structural flaws that render them ineffective.

First, these roles often report to inappropriate organizational units. A data governance officer might report to a mid-level IT manager rather than to a chief data officer or executive committee, immediately signaling that governance is viewed as a technical support function rather than a strategic business concern. This reporting structure ensures that when governance requirements conflict with operational pressures or political interests, governance loses.

Second, these positions are not considered strategically relevant by the organization. They’re treated as expendable – roles that can be easily cut during budget pressures or reorganizations. When a governance role can be eliminated without executive-level discussion or concern, it reveals the organization’s true priorities. If a chief financial officer position were to become vacant, immediate action would follow; if a data stewardship lead position were to become vacant, operations would continue unchanged.

Third, and perhaps most damaging, these roles are systematically excluded from decision paths. They’re not invited to strategy meetings where data initiatives are planned. They’re not consulted when systems are designed or purchased. They’re not involved in redesigning business processes. Instead, they’re brought in after decisions are made and asked to “document” what was decided, functioning as scribes rather than governors.

This relegates governance professionals to what can only be described as a “theoretical provider’s role.” They produce documents – data dictionaries, policy frameworks, quality standards – that sit in repositories, unread and unimplemented. They’re asked to create governance artifacts without the authority to ensure anyone follows them, reviews them, or even acknowledges them. It’s governance theater at its most visible: all the trappings of proper governance with none of the actual authority or influence.

The DMBOK framework explicitly addresses this dysfunction by emphasizing that governance roles must include not just responsibilities but commensurate authority, appropriate reporting lines to senior leadership, and explicit inclusion in decision-making processes. A data steward without authority to challenge data quality issues isn’t performing governance – they’re performing documentation. A Governance Council that reports to middle management and gets overruled by operational leaders isn’t governing – it’s advising, and its advice can be safely ignored.

The Data Management Body of Knowledge makes clear that data governance is fundamentally about accountability and authority over the business aspects of data, not technical management. It requires organizational structures: a Data Governance Council with executive sponsorship and decision-making authority, working groups focused on specific domains, and clearly defined stewardship roles. These can operate through centralized, federated, or decentralized models.

Regardless of model, governance bodies have specific obligations: establish and enforce policies that protect data as an asset; create accountability frameworks that assign clear ownership; resolve conflicts over definitions or access; ensure regulatory compliance; embed controls into systems and processes; and align data initiatives with business strategy. This distinguishes data management (technical execution) from data governance (business oversight, ensuring value and reduced risk). Organizations often have technical teams managing infrastructure but lack business-focused governance that treats data as a strategic asset, with clear responsibilities, enforced policies, and embedded controls – not just technical requirements imposed on business people who are excluded from the decisions that actually matter.

Conclusion: Building the Foundation

The IT-business divide and the lack of governance structures represent fundamental obstacles to effective data governance. When data professionals operate primarily from technical backgrounds without business engagement, when document proliferation creates chaos without a single source of truth, when governance roles lack appropriate authority and reporting lines, and when technical management roles are mistaken for governance authority, organizations cannot establish the foundation necessary to transform data from liability to asset.

The path forward requires acknowledging that data governance is fundamentally a business discipline, supported by technical capabilities, not the other way around. Organizations must establish clear distinctions between data management (technical execution) and data governance (business oversight), create dedicated stewardship roles that bridge IT and business stakeholders with real authority, implement governance structures with executive sponsorship and appropriate reporting lines, and ensure governance professionals are included in decision-making processes rather than relegated to documentation roles.

Addressing metadata and business rule governance is equally critical. Organizations must document, share, and actively govern the definitions and rules that ensure data comparability across entities and over time. Without this foundation, even technically excellent systems produce results that cannot be trusted for strategic decision-making.

In Part 2 of this series, we’ll explore how even well-designed governance structures fail without proper accountability mechanisms and diverse organizational capabilities. We’ll examine the accountability gap – where governance exists on paper but lacks enforcement – and the skills deficits that undermine data initiatives when organizations rely solely on technical expertise without complementary capabilities in change management, training, ethics, and structured project management.

References

Airbyte. (2025). What is Data Stewardship: Best Practices & Examples. Retrieved from airbyte.com/data-engineering-resources/data-stewardship

Atlan. (2023). DAMA DMBOK Framework: An Ultimate Guide. Retrieved from atlan.com/dama-dmbok-framework/

DAMA International. (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd ed.). Retrieved from dama.org/cpages/body-of-knowledge

Gartner. (2020). Data quality: Why it matters and how to achieve it. Retrieved from gartner.com/en/data-analytics/topics/data-quality

Gartner. (2024). Gartner predicts 80% of data and analytics governance initiatives will fail by 2027. Retrieved from gartner.com/en/newsroom/press-releases/2024-02-28-gartner-predicts-80-percent-of-data-and-analytics-governance-initiatives-will-fail-by-2027-due-to-a-lack-of-a-real-or-manufactured-crisis-

Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152. doi.org/10.1145/1629175.1629210

Redman, T. C. (2013). Data’s credibility problem. Harvard Business Review, 91(12), 84–88.

Redman, T. C., et al. (2024). Data Governance Is Failing – Here’s Why. CDO Magazine. Retrieved from cdomagazine.tech/opinion-analysis/data-governance-is-failing-heres-why