What are the nine key aspects of Gartner’s view on data governance? #
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Gartner’s perspective on data governance extends beyond technology into organizational processes, cultural adoption, and metadata-driven control.
“Shift [your] data governance strategy from data to outcomes so that business roles within the organization can see the connection between data, its governance and achieving the enterprise mission.” - Gartner on data governance
Below are the key aspects highlighted in their research, which includes the Hype Cycle for Data and Analytics Governance, Market Guide, and Magic Quadrant.
1. Focus on processes and standards #
Data governance should be grounded in well-defined processes and standards that guide how data is created, stored, accessed, and shared. This ensures consistency across teams and reduces ambiguity in data handling.
2. Data quality #
Accurate, consistent, and reliable data is a non-negotiable requirement. Gartner frames data quality as a central pillar of governance since it underpins analytics, decision-making, and compliance efforts.
A key trend here is augmented data quality – the use of AI and similar technologies to improve the consistency, accuracy, and reliability of data.
In particular, Gartner says augmented data quality can provide automated insights in a number of areas, including:
- Profiling & monitoring
- Data transformation
- Rule discovery and creation
- Matching, linking, & merging
- Active metadata support
- Data remediation
- Role-based usability
3. Data security and usability #
Effective governance frameworks safeguard sensitive information while ensuring data remains usable and accessible for those who need it. Gartner emphasizes balancing protection with practical access.
4. Better decision-making #
By ensuring data is trusted, organizations can improve the quality of their insights. This shifts governance from being seen as a “control mechanism” to a value enabler for business outcomes.
5. Regulatory compliance #
Governance structures should support compliance with legal, industry, and jurisdictional regulations, reducing risks of fines and reputational damage.
6. Governance maturity model #
A data governance maturity model outlines stages from reactive (ad-hoc, compliance-driven) to proactive (embedded, business-aligned), giving organizations a framework to assess progress.
7. Metadata-driven governance #
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) can result in increased revenue from the development of new data products based on previously “dark” data.
Additionally, active metadata is vital for scale and AI-ready data governance as it can drive automation of key processes, such as data classification, policy enforcement, lineage tracking, and quality monitoring.
8. Education and collaboration #
“Drive culture change to support data-driven decisions and deliver business value. Data literacy is a first step to fully leveraging data and analytics.” - Gartner on the role of culture in data governance success
Gartner emphasizes the cultural side: governance must be understood, embraced, and practiced across the business. Training, awareness, and collaboration across silos shift governance from “command and control” to “enable and empower.”
9. Platforms and technologies #
Gartner evaluates data and analytics governance platforms, publishing its Magic Quadrant to help enterprises identify tools that combine cataloging, policy, quality, lineage, and collaboration into a single ecosystem.
What does Gartner have to say about data governance challenges? #
Gartner predicts that by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks. More importantly, Gartner maintains that current data governance practices are often too rigid and insensitive to the business context.
While 60% of organizations have established a data governance framework and another 32% are on their way to executing a roadmap, effective enforcement is challenging because of:
- Lack of talent
- Inability to establish data management best practices
- Understanding third-party compliance
- Integrating modern data governance tools to the existing tech stack
- Lack of business support and data leadership
The biggest barriers to executing data governance frameworks. Source: Gartner.
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.
What are the seven key elements to achieving good governance according to Gartner? #
By establishing effective data governance, you lay the foundation for seizing business opportunities and addressing organizational challenges.
According to Gartner, here are the seven foundations of effective data and analytics governance:
- Value and outcomes: Governance activities must directly connect to business priorities and deliver measurable outcomes.
- Accountability and decision rights: Clear models ensure all stakeholders know where data is created, consumed, and controlled.
- Trust: Governance must validate and curate both internal and external data sources to ensure reliability.
- Transparency and ethics: Governance frameworks should operate openly, with ethical foundations that can withstand scrutiny.
- Risk and security: A risk-aware approach integrates information security and compliance into all governance efforts.
- Education and training: Continuous training builds the skills and awareness needed for stakeholders to uphold governance practices.
- Collaboration and culture: Sustainable governance requires cultural change, shifting from control to partnership and shared responsibility.
Gartner’s key elements to good governance. Image by Atlan.
What tools does Gartner recommend for data governance? #
In the 2025 Magic Quadrant report for data and analytics governance platforms, Gartner recommends tools that integrate business and technology capabilities, so that business leaders and users can:
- Develop and deploy a diverse set of governance policies
- Monitor and enforce those policies across their organizations’ business systems
Atlan is recommended as a Visionary in this Magic Quadrant, with its broad platform approach and role as an emerging trusted advisor in the governance community.
Key capabilities that can scale your data governance efforts, driving policy setting, execution and enforcement across all policy types, include:
- Focus on automation and AI-driven solutions
- AI recommendations and tools to enrich and govern data at source
- Open metadata lakehouse infrastructure to accommodate increasing data scale and diversity
- A customer-centric platform and service approach
- Courses, certifications and templates through Atlan University for continuous learning and improvement
Real stories from real customers: Ensuring data governance success and unlocking value creation #
One trusted home for every KPI and dashboard
“Contentsquare relies on Atlan to power its data governance and support Business Intelligence efforts. Otavio Leite Bastos, Global Data Governance Lead, explained, ‘Atlan is the home for every KPI and dashboard, making data simple and trustworthy.’ With Atlan’s integration with Monte Carlo, Contentsquare has improved data quality communication across stakeholders, ensuring effective governance across their entire data estate.”
Otavio Leite Bastos, Global Data Governance Lead
Contentsquare
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Modernized data stack and launched new products faster while safeguarding sensitive data
“Austin Capital Bank has embraced Atlan as their Active Metadata Management solution to modernize their data stack and enhance data governance. Ian Bass, Head of Data & Analytics, highlighted, ‘We needed a tool for data governance… an interface built on top of Snowflake to easily see who has access to what.’ With Atlan, they launched new products with unprecedented speed while ensuring sensitive data is protected through advanced masking policies.”
Ian Bass, Head of Data & Analytics
Austin Capital Bank
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Book a Personalized Demo →Ready to future-proof your data governance efforts? #
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.
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Book a Personalized Demo →FAQs about Gartner data governance #
1. What is data governance at Gartner? #
Gartner defines data governance as the processes, policies, and standards that ensure accuracy, consistency, security, and usability of data across the enterprise.
It emphasizes both technology and organizational practices, including roles, accountability, and measurement frameworks.
2. What are the key aspects of Gartner’s view on data governance? #
Gartner highlights processes and standards, data quality, security, metadata-driven governance, regulatory compliance, and collaboration. They also provide a maturity model to help organizations assess and advance their governance programs.
3. 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.
4. What role does metadata play in Gartner’s governance approach? #
Metadata is central in Gartner’s view. Active, accurate metadata simplifies management, drives automation, supports compliance, and ensures data is trustworthy and AI-ready.
5. What challenges does Gartner identify in data governance? #
Gartner predicts many governance initiatives fail due to lack of urgency or stakeholder buy-in. They stress the need for clear value connection, education, and cultural change to avoid governance being seen as control-heavy or bureaucratic.
6. What tools does Gartner recommend for data governance? #
Gartner publishes a Magic Quadrant for Data and Analytics Governance Platforms, reviewing technologies that support metadata management, policy enforcement, lineage, quality monitoring, and collaboration to enable scalable governance.