Why Data Governance Is Faltering and How Tools Stack Up: A Strategic Guide for Decision-Makers

In an era where data is hailed as the new oil, effective data governance has become the linchpin for organizations aiming to harness its value while ensuring compliance, security, and trust. Yet, mounting evidence suggests that many organizations are grappling with its implementation. A recent exposé by CDO Magazine titled "Data Governance Is Failing—Here’s Why" lays bare the stark reality: data governance, despite its promise, is stumbling. This article dissects the reasons behind these struggles, pits legacy tools like Informatica, IBM, and Collibra against emerging AI-based contenders such as Atlan, data.world, and Ataccama, and offers a balanced perspective to guide decision-makers in navigating this critical landscape.

The Crumbling Foundations of Data Governance

Data governance is not failing for lack of intent but due to a confluence of structural, human, and technological challenges. The CDO Magazine analysis identifies eight core reasons, which we distill here:

• Knowledge Gaps: A pervasive lack of understanding among staff and leadership about governance principles fuels skepticism and resistance.
• Overreliance on Technology: The misconception that tools alone can solve governance woes often sidelines human stewardship and accountability.
• Implementation Woes: Poor execution, inadequate resources, or inflated expectations frequently derail programs.
• Lack of Consensus: Without a shared definition or goals, efforts fragment and falter.
• Overhyped Promises: Marketed as a quick fix, governance’s complexity often leads to unmet expectations.
• Organizational Misalignment: Placing governance solely under IT creates silos, disconnecting it from business strategy.
• People Problems: Absent champions, ownership, or aligned incentives, progress stalls.
• Elusive ROI: Quantifying long-term value remains a hurdle, especially when costs and benefits accrue to different stakeholders.

These challenges underscore a critical truth: no single tool or approach can unilaterally salvage data governance. Instead, organizations must weigh the strengths and limitations of available solutions—both legacy and AI-driven—to forge a path forward.

Legacy Tools: The Bedrock of Enterprise Governance

Legacy tools like Informatica, IBM, and Collibra have long been the stalwarts of data governance, offering depth and maturity honed over decades. Each brings distinct strengths, tempered by resource demands.

Informatica: Scalability Meets Complexity

Founded in 1993, Informatica is a titan in the field, crowned a Leader in the 2025 Gartner Magic Quadrant for Data and Analytics Governance Platforms (Informatica). Its Cloud Data Governance and Catalog platform, powered by the CLAIRE AI engine, excels in data integration, cataloging, and quality management. A centralized console ties data lineage to business processes, addressing implementation and organizational issues head-on. Yet, its robust capabilities demand significant expertise and investment—rated 4.3 on G2 as of February 2025—making it ideal for large enterprises but potentially unwieldy for smaller players.

IBM: AI-Powered Precision

IBM, another Leader in the 2025 Gartner report (IBM), leverages its Knowledge Catalog within the Cloud Pak for Data to deliver AI-driven profiling, cleansing, and monitoring. Its data fabric technology ensures scalability across sprawling data ecosystems, while dashboards quantify benefits, tackling knowledge gaps and ROI challenges. However, its comprehensive feature set may overwhelm organizations lacking robust training frameworks, amplifying people-related risks.

Collibra: Collaboration at the Core

Since its inception in 2008, Collibra has risen as a Leader in the 2025 Gartner rankings (Collibra). Its Data Intelligence Platform—spanning AI Governance, Data Catalog, and Quality—prioritizes collaboration via multi-persona experiences and customizable analytics. The recent Collibra AI Governance module ensures oversight of AI use cases, countering overreliance on technology. With a 4.5 Gartner Peer Insights rating as of February 2025, it shines in enterprise settings but shares the legacy burden of resource intensity.

These tools are battle-tested, offering enterprise-grade reliability. Yet, their complexity can exacerbate implementation and training challenges if not paired with meticulous planning.

AI-Based Tools: The Vanguard of Innovation

Emerging AI-based tools—Atlan, data.world, and Ataccama—bring agility and automation to the fore, targeting modern data stacks and resource-constrained teams. Their promise lies in simplicity and scalability, though depth may lag for the largest enterprises.

Atlan: Simplifying with AI

Launched in 2019 and named a Visionary in the 2025 Gartner Magic Quadrant (Atlan), Atlan’s AI-powered data catalog streamlines discovery and governance. Features like AI-driven stewardship and a metadata lakehouse integrate seamlessly with tools like DBT and Snowflake, easing knowledge gaps and implementation hurdles. Bolstered by a significant Series C funding round in 2024, Atlan’s high Gartner Peer Insights rating (January 2025) signals its appeal to agile, AI-ready organizations.

data.world: Community as Catalyst

Also a Visionary in 2025 (data.world), data.world, founded in 2015, reimagines governance through collaboration. Its AI-driven workflows and community features—like monthly town halls—foster decentralized stewardship, addressing people and structural challenges. With Series B funding secured in 2023, it suits data-driven firms but may lack the heft for highly regulated giants.

Ataccama: Automation with Balance

Ataccama, established in 2007 but revitalized with AI, earns recognition in the 2025 Gartner report (Ataccama). Its Ataccama ONE platform, featuring an AI agent and GenAI for natural language queries, automates workflows while preserving human oversight. Deployed via Ataccama Cloud, it tackles implementation and overreliance issues, offering scalability for mid-to-large enterprises.

These innovators excel in user-friendliness and automation—claiming up to 50% reductions in manual tasks—but may fall short in feature depth for complex, regulated environments.

Legacy vs. AI: A Comparative Lens

To guide decision-makers, we compare these tools across key dimensions tied to governance failures:

Driving Forces further illuminate the divide:

Legacy tools offer proven depth; AI-based tools deliver agility and innovation.

Charting the Future: A Hybrid Path

The data governance conundrum demands no one-size-fits-all solution. Legacy tools like Informatica, IBM, and Collibra anchor mature enterprises with their reliability and feature richness, while AI-based tools like Atlan, data.world, and Ataccama empower agile firms with speed and simplicity. A hybrid approach—melding legacy stability with AI-driven innovation—may prove optimal. Organizations must assess their data maturity, compliance mandates, and resource bandwidth, using tools like the Data Governance Readiness Assessment 2025 to stay aligned with industry benchmarks.

As data governance evolves, the choice is not binary but strategic. I invite you, data professionals, to share your experiences in the comments: What’s working—or not—in your governance journey?