Leverage Gartner’s research to understand modern data governance and pick the right governance tool for your team.
Gartner is a market research company specializing in technology, providing research insights, and tools, organizing conferences, consulting, and enabling peer connections that help your team make faster and better technology decisions.
Gartner leverages its research mainly through two mediums: Research publication and tools
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Gartner on data governance
Data governance is misunderstood. There is a tussle between traditional data governance which is bureaucratic, top-down, and control-driven vs. modern data governance which is driven by collaboration, and automation.
Modern data governance at its core is about diverse data teams working together and creating a better data culture for your organization.
Gartner, through its research over the years, has been interpreting and following the key trends in data governance. We’ll look at the following research and tools that help you navigate the data governance world:
- Market guide for data analytics and governance
- Gartner Tool: Vendor Identification for Data and Analytics Governance Platforms
- Gartner Peer Insights
- Gartner Hype Cycle for Analytics and Data Governance
- Gartner Magic Quadrant for Data Governance
Market Guide for Data and Analytics Governance, 2022
“The data and analytics governance platforms market is embryonic,” and “Most organizations have become good at setting up silos for specific governance components: data security and privacy management and data retention,” finds the market guide. With the new way of looking at governance through analytics pipeline, and DataOps — rather than the traditional “Master data management (MDM)” — the expanse of use cases for modern data governance is ever increasing.
Modern data and analytics initiatives need a balanced set of governance capabilities, but stand-alone products often do not provide what is needed. Data and analytics leaders must therefore explore the emerging market of converging capabilities and exploit them to support their governance needs.
Data analytics and governance: The research categories
The market guide explores the modern data governance spectrum through the following categories:
• Market definition
• Market direction
• Market analysis
• Vendor profiles
• Market recommendations
Key findings and recommendations
• The convergence of technologies in data management has laid the foundation for better and unified data governance.
• Business intelligence improves when governance activities are managed in a single platform that helps: integrate with multiple data sources, curate data better, and manage workflows seamlessly.
• To get started, create a checklist of various data user “personas” and identify key requirements and policy controls — data quality, security, and privacy. Now use this checklist as an objective framework to evaluate data governance tools.
Key technology capabilities of data analytics and governance platforms
Active metadata management
Metadata is everywhere, starting from your database, data pipeline, and orchestration tools, and up to your reporting dashboard. Active metadata platforms help unify, manage, and activate metadata for faster data delivery, better data discovery, and robust workflow management.
Gartner in its Market Guide for Active Metadata Management finds, “Metadata capabilities embedded in most data management solutions must expand to provide metadata analysis for all types of metadata categories from any platform, or that function will be replaced by embeddable, advanced metadata functionality from mature metadata solutions.”
The data governance tools must enable access control and protect sensitive data seamlessly across the entire ecosystem. Rather than thinking of governance as a user-specific, top-down approach, governance tools must think in terms of roles/function/persona, this helps automate and have better control over access management.
One of the goals of effective data governance is to enable users to access the right data, at the right time. Data catalogs help users search, discover, understand, and trust assets across the entire data ecosystem. Modern data catalogs are not just static repositories of metadata, they help bring metadata into the tools you use, and to your daily workflows.
Data dictionary and business glossary
A data dictionary provides information and insights about the data stored in your database; It is the documentation for your database. Most common insights include data sources, table names, descriptions, and relationships, column names and descriptions, data types, validation rules, and SQL queries related to the table.
A business glossary is a repository of business terms, metrics, KPIs, and key definitions associated with your data assets. Glossaries reduce the prevalence of inconsistent terminologies that affect data discovery and accessibility, hence paving the road toward data democratization.
Data Lineage and impact analysis
Data lineage records data provenance. It tracks data from its source to the dashboards — and tracks all the transformations it has gone through in its entire lifecycle. Lineage graphs help data engineers to investigate the downstream impact of a design change/optimization efforts.
Integration with the modern data stack (MDS)
The core of modern data governance is metadata interoperability. Governance tools must leverage open APIs to seamlessly exchange metadata across the entire data stack — Data ingestion, ETL, catalogs, and BI tools.
Easy for all users
At a time when every company tries to be “data-driven” in some way or another, almost everyone in the company is encouraged to utilize data for decision-making. This has created a whole gamut of data users with various levels of skills and goals. Thus a modern data governance tool must cater to the needs of these diverse users — Data engineers, analysts, architects, stewards, and business analysts across different teams.
Access full report → Market Guide for Data and Analytics Governance Platforms
Related Gartner reads:
- Hype cycle for data and analytics governance and master data management, 2021
- Implement your data and analytics governance through 5 pragmatic steps
- Market guide for active metadata management
A Guide to Building a Business Case for a Data Catalog
Gartner Tool: Vendor Identification for Data and Analytics Governance Platforms
Gartner provides a tool for data analytics and governance leaders to research, evaluate, and shortlist data governance solutions.
A Data and Analytics Governance platform is a set of integrated technology capabilities that enables governance across all information, data, and analytics assets. It focuses on the work of policy setting and enforcement and exposes an extended user experience for all relevant stakeholders (e.g., governors, stewards, business and IT roles) for a range of policies such as quality, privacy, security, and standards.
How to use the data governance evaluation tool
The tool is a spreadsheet that has the following 4 parts:
Data governance platforms list This section lists 52 vendors stacked against key evaluation criteria such as:
• Total customers served
• End-user persona
• Policy setting and policy enforcement
• Industries and geography served
• Deployment and pricing options
Vendors capabilities map
This section lists vendors stacked against key data governance capabilities such as:
• Active metadata management
• Data catalog
• Business glossary and dictionary
• Access management
• Data lineage and data orchestration
Vendor’s governance policy map
This section lists how different tools compare based on various data governance policies such as:
• Lifecycle and
• Definition and models
Some of the vendors included in the representative list are
Access the tool → Vendor Identification for Data and Analytics Governance Platforms
Modern Data Catalogs: The Key Trends, the Data Stack, and the Humans of Data
Gartner Peer Insights
Gartner Peer Insights is an open platform that gathers reviews of software products directly from verified users. The platform covers more than 400 categories, 18000 products, and 380,000 appraised reviews.
Presently Gartner does not have peer insights specifically for the data governance category, but you will find reviews of governance tools categorized as Enterprise Metadata Management tools.
You can sift through the platform using various criteria like geography, business size, total revenue, industry, and ratings. The platform also allows you to compare products to examine the key differences.
Gartner also publishes the “Voice of the Customer” report that crunches insights from the reviews — total ratings, responses, company size, and geography — and ranks 20 top software under various categories.
Visually the “Voice of the Customer” is made of four quadrants: Customer’s choice, Established, Strong performer, and Aspiring. The 20 selected tools are then grouped inside each quadrant.
Gartner Hype Cycle for Analytics and Data Governance
What is the Gartner Hype Cycle?
Gartner Hype Cycle research is a “graphical representation of the maturity and adoption of technologies and applications” that helps you make better technology adoption decisions.
Hype Cycles helps you answer questions like: “Should I be an early adopter of a technology, what are the risks involved? What does the cost-benefit analysis look like?” Or “Has the technology I’m adopting already run its course and is it now antiquated? what other options do I have?”
The Hype Cycle as the name suggests, helps you to separate out the hype — fed by media and companies — from the overall maturity lifecycle of a technology trend.
How to read a Gartner Hype Cycle?
The graphical representation of the Hype Cycle has 5 phases:
This the where a new technology/idea gets innovated. It is still in the early research/lab/PoC stage, so no clear product or application emerges. Significant media interest is generated at this stage.
The peak of inflated expectations
Few products adopt the technology and the initial success stories produce even more interest in the media. Since these are experimental and highly specialized products, use-case mismatch occurs. It becomes very difficult to adopt, which results in a series of failures.
Trough of disillusionment
This is the phase where interest wanes, the market understands the hype, products fail, and a consolidation of vendors happens. This is also when the existing vendors, take a step back, learn from the early feedback and try to realign.
Slope of enlightenment:
Initial failures help the industry to understand the application and risks better. As tangible benefits start to emerge, a new generation of companies and products are launched to carry forward the baton.
Plateau of productivity
This is the phase of the maturity lifecycle where the technology is widely adopted and is considered to be a standard. Businesses clearly know the costs and benefits before investing. A new ecosystem of products is built to support the technology.
Analytics and Data Governance Hype Cycle.
Gartner in June 2022, published its Hype Cycle research on data analytics and governance. This, at a time when, ”80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance,” and data practitioners will find it ever more challenging to trust data “because of data distribution combined with disconnected business processes and data silos”.
Gartner’s Hype Cycle research predicts,
Data and analytics leaders must understand the hype and progress of governance practice and technology innovations so their adoption delivers organizational value at the right time. This research identifies such innovations’ progress in driving more business value with data and analytics governance.
Key technological trends in Analytics and Data Governance Hype Cycle
Phase 1: Innovation trigger
• Connected governance
• D&A governance platforms
• Adaptive D&A governance
• Augmented MDM (obsolete)
• Data observability
• AI governance
Phase 2: Peak of Inflated Expectations
• Augmented data quality
• Analytics governance (obsolete)
• Data literacy
• Trust-based model for governance (obsolete)
Phase 3: Trough of Disillusionment
• Master data management
• D&A stewardship
• Digital ethics
• Augmented data cataloging
• Information stewardship application (obsolete)
Phase 4: Slope of Enlightenment
• Multi-domain MDM solutions
• MDM of customer data
Phase 5: Plateau of Productivity
• MDM of product data
Gartner Magic Quadrant for Data Governance
Gartner Magic Quadrant is a proprietary market research methodology that helps compare and contrast tools and technology providers. The magic quadrant evaluates and places the tools in 4 quadrants namely: Challengers, Leaders, Niche Players, and Visionaries.
At present, Gartner does not have a magic quadrant for data governance tools.
If you are trying to understand data governance tools with Gartner’s lens then resources like strategic roadmap, market guide, vendor evaluation tool, and peer review platform are great places to start.
Gartner resources to understand the trends in modern data governance:
- Enhance your roadmap for data and analytics governance
- Quick answer: What are the foundations of data and analytics governance?
- Market Guide for data and analytics governance platforms
- Vendor identification for data and analytics governance platforms
- Gartner Peer Insights for data governance and metadata management tools
- Playbook: Building a modern data governance program
- Data and analytics governance requires a comprehensive range of policy types
Atlan: Data governance, without the governing
Atlan has been included in the representative data governance tools list in the following research study:
- Market guide for active metadata management
- Augmented data catalogs: Now an enterprise must-have for data and analytics leaders
- Cool vendors in DataOps
- Tool: Vendor identification for data and analytics governance platforms
- Hype Cycle for data management, 2022
- Hype Cycle for data and analytics governance, 2022
Demo of Atlan data governance
If you are evaluating and looking to deploy best-in-class data governance for your data ecosystem — you might want to check out Atlan.
Deep integration possibilities across the stack and open API enable Atlan to solve other modern data governance use cases across DataOps, workflow management, and pipeline automation.
Gartner data governance: Related reads
- What is data governance: definition, importance, and components
- 6 commonly referenced data governance frameworks in 2022
- 8 best practices for a robust data governance program
- Data governance roles and responsibilities: The complete list
- Data governance for Snowflake data assets
- Manage data governance for Databricks