Updated November 11th, 2024

Gartner Data Governance: Trends, Insights & Best Practices for 2025

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According to Gartner, 79% of corporate strategists see AI and analytics as critical to their company’s success.

However, Gartner’s analysts also predict that 60% of organizations will fail to realize the value of their AI plans due to a lack of a solid approach to data governance.
See How Atlan Aligns with Gartner’s Data Governance Vision

How can organizations close this governance gap to ensure the success of their AI and data analytics initiatives? The answer can be found in Gartner’s guidance on where data governance is now, and the strategies it offers to help companies achieve success in the short-term future.

Gartner defines data governance as the processes and standards for managing data to ensure accuracy, consistency, security, and usability across an organization. This approach enables better decision-making, regulatory compliance, and effective data management through defined policies, roles, and data quality measurements.

Table of Contents #

  1. The Gartner Hype Cycle and Market Guide
  2. Gartner’s stance on data governance
  3. Gartner’s market guide for data and analytics governance: core capabilities
  4. Gartner’s market guide for data governance: Trending capabilities
  5. Gartner on data governance challenges
  6. Future-proofing your organization’s data
  7. FAQs on Gartner Data Governance
  8. Gartner data governance: Related reads

The Gartner Hype Cycle and Market Guide #

Gartner tracks data governance through a series of reports and recommendations, the two most important being the Gartner Market Guide and the Hype Cycle for Data and Analytics Governance.

The Gartner Market Guide for Data & Analytics Governance Platforms lays out the capabilities that Gartner sees as critical to data governance in its current stage and also identifies trending new capabilities. Finally, it maps these capabilities to leading vendors in the data governance marketplace, showing which data governance platforms support which features (and how well). To bolster their in-house analysis, Gartner also provides the Gartner Peer Insights for Data & Analytics Governance Platforms report, where enterprise users rate and share their experience with different platforms in the real world.

The Gartner Hype Cycle graphically tracks the rise of new technology through several progressive phases. The Hype Cycle’s purpose is to identify when new tech has transitioned from awareness and overinflated hype and is at the stage where enterprises are using it to drive actual business value.

Specifically, The Gartner Hype Cycle for Data and Analytics Governance focuses deeply on the technology areas that improve the implementation of organizational data governance practices to achieve and accelerate business results. It examines areas such as advanced data cataloging and metadata management solutions, cross enterprise MDM, stewardship, and advanced data quality.

Additionally, Gartner is preparing to release two new related reports — the Magic Quadrant and Critical Capabilities for Data and Analytics Governance Platforms — later in 2024.

There are also two other Gartner reports which relate to the data governance space and provide valuable context and related information:

The full Market Guides, Hype Cycle, and Magic Quadrant reports are available only to Gartner customers, though Gartner also publishes other guidance — such as information on the foundations of data governance, challenges in the space, etc. — for the general public.

Gartner’s stance on data governance #

Gartner spends so much time on data governance because Gartner analysts view the process as critical to digital business success. Data governance was a central focus of the company’s latest Business & Analytics Data Summit, where Gartner speakers made clear that they see proper governance as critical for securing value from data, particularly for evolving use cases like AI.

“By 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive ethical governance frameworks.” - Rita Sallam, Distinguished VP Analyst at Gartner

According to Gartner, solid data and analytics governance:

  • Accelerates time to market for new data initiatives
  • Promotes safe and secure development of new initiatives (such as GenAI) via central command and control-style governance
  • Creates new business value through the production of new data products
  • Allows orgs to reallocate resources to high-priority projects

Considering these goals and the state of the market, Gartner says that computational governance, connected governance, and a robust data & analytics architecture are critical capabilities that still haven’t lost their potential. In particular, Gartner sees three capabilities as central to the future of data and analytics governance:

  • Metadata management, a set of capabilities that enable continuous access to and processing of metadata information — data about data — to help companies catalog and comprehend the vast amount and variety of data in their data estates.
  • Augmented data quality, or the use of AI and similar technologies to improve the consistency, accuracy, and reliability of data
  • Master Data Management, compiles and manages a master list — a set of identifiers — of the key business attributes in an org’s data estate (one of which is metadata)

Looking ahead, Gartner also sees AI governance and Responsible AI as delivering high potential for digital transformation within the next two to five years.

Gartner’s market guide for data and analytics governance: core capabilities #

Gartner’s market guide report differentiates between core and trending capabilities.

The core capabilities for data and analytics governance platforms remain roughly the same as in previous Gartner reports. The full list is too long to include here. But some of the key highlights include:

  • Data catalog: A single source of truth for an organization’s data
  • Active metadata: Analyzing metadata patterns to drive event-driven actions across a data estate based on change events
  • Data classification: Organizing data by categories (e.g., sensitivity) to efficiently regulate and control access
  • Data lineage: The ability to track the movement of data across an organization and trace it back to its source
  • Orchestration and automation: Using techniques such as augmented data management to improve data quality, data integration, workload management etc., through the use of Machine Learning and AI
  • User interface: Creating data governance systems that are usable by all roles - from data architects and engineers to analytics engineers, business analysts, and other business users

In addition to these core capabilities, Gartner identifies trends in several areas. These represent areas where data and analytics governance vendors are adding additional capabilities that improve and simplify data governance.

The trends include:

  • Data management
  • Data stewardship
  • Augmented data quality
  • Unified governance
  • Dynamic trust models

Data management #


In data management, Gartner reports that more companies are utilizing active metadata to connect previously disconnected data silos. Making data silos discoverable (e.g., through a data catalog) means that the data in these repositories can be made readily available to data engineers and business users. This can result in increased revenue from the development of new data products based on previously “dark” data.

Gartner also sees a shift in using genAI-powered inference engines to populate, discover, and explore data catalogs. Moreover, it sees the use of data mesh architectures, which break complex data sets up into smaller data domains, as important to accelerating the implementation of data governance projects.

Data stewardship #


Gartner says that data stewardship is still an area where many data governance vendors falter. The issue, says Gartner, is that, while tools like data lineage and data classification are available, they’re often highly manual to use and siloed from overall data workflows. The result is that, for many data governance platforms, critical data workflow tasks like workflow creation or creating and maintaining personas are not self-service for users but instead require IT support.

Augmented data quality #


Gartner devotes an entire Magic Quadrant report to the augmented data quality market, which it defines as providing tools for an enhanced data quality experience that improve business insight and offer automation recommendations, such as next-best-action suggestions. In particular, Gartner says augmented data quality can provide automated insights in a number of areas, including:

  • Profiling & monitoring
  • Issue resolution
  • Matching, linking, & merging
  • Rule discovery and creation

The priority in this space is in ease of use and automation. Gartner predicts that these two factors will drive 90% of purchasing decisions for data quality solutions in the near term.

Unified governance #


Another area that Gartner identifies as lacking is unified governance. More customers, it says, are looking for comprehensive governance solutions that enable managing policies settings and enforcement across the enterprise.

Dynamic trust models #


A data trust is a structured framework for governing the access, use, and handling of data. Gartner says it hasn’t seen the movement in data trust that it had anticipated. However, automated processes that can dynamically determine trust may revive this concept.

Gartner on data governance challenges #

Looking at the overall market, Gartner finds a lot of mixed maturity when it comes to data governance.

The issue is that most governance programs are set up to fail. Most organizations aren’t where they want to be in their data governance maturity. Those driving for change are mostly doing so reactively, for example in response to a data compliance incident that cost them dearly. Few are taking a proactive approach to governance — i.e., one driven by measurable business outcomes.

Given this current reality, it’s no wonder why many companies say they experience numerous challenges implementing their data governance programs:

  • 63% said that they faced issues hiring the right people with the right skills or even securing headcount in the first place
  • 58% struggled to establish best practices for data management
  • 44% faced issues with understanding third-party compliance (e.g., securing data in a public cloud)
  • 43% said integrating data governance tools into their existing tech stack proved arduous

These answers paint a sobering picture. While most companies understand the importance of data governance in principle, there are numerous challenges with actually putting an effective program into practice. This is why, when companies look at data governance tools, they must focus on the tools that are easy to install, easy to use, and integrate seamlessly into an existing data stack.

Future-proofing your organization’s data #

No one can predict the future. However, Gartner’s insights into data governance trends based on its dialogues with large enterprise customers provide a set of signals for gauging which developments in the data governance space will prove critical in the coming years. Assessing these signals can enable you to make key improvements that “future-proof” your data governance program.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #

  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Are you facing your own unique challenges in implementing a successful data and analytics governance program? Talk with our data governance experts at Atlan today to see how we can help.

FAQs on Gartner Data Governance #

1. What is Gartner’s view on data governance for 2024? #


Gartner highlights data governance as essential for organizations to leverage AI and analytics effectively. The importance lies in establishing a robust data governance strategy to prevent failure in achieving AI goals.

2. Why is data governance critical for AI success? #


Gartner reports that 60% of companies may not realize the benefits of AI without a solid data governance framework. Effective governance ensures data quality, security, and accessibility, which are essential for AI success.

3. What are the core capabilities for data governance according to Gartner? #


Gartner outlines core capabilities such as metadata management, data quality management, and privacy management as foundational for a successful data governance framework.


Some of the trends Gartner notes include automated data governance, the adoption of AI-driven insights, and scalable governance frameworks that adapt to organizational needs.

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