Report:

Magic Quadrant for Metadata Management Solutions

Top Vendor Products for Metadata Management Solutions in 2025

• Ab Initio Software
• Alation
• Atlan
• IBM
• Informatica

Metadata Management Solutions Vendor Product Comparisons 2025

• Ab Initio Software - Leader
  

  Pros:

  • Comprehensive, enterprise-scale capabilities: Ab Initio excels at handling complex, end-to-end data platform deployments by integrating metadata from multiple cloud and on-premises sources, including various file formats, databases, reporting tools, and other third-party catalogs.
  • Advanced semantics: Ab Initio's Metadata Hub leverages agentic AI that utilizes ML/SLMs/LLMs for semantic discovery with reinforcement learning, increasing the accuracy and reliability of user interactions with metadata. Users can interact using business terms and corporate ontologies, asking questions that mix both metadata and data.
  • Strong use of active metadata: Ab Initio's approach emphasizes using metadata not just for description, but to drive actual data processing and automation, creating and leveraging active metadata. AI agents generate and manage understandable metadata, which then defines workflows for tasks such as data acquisition, masking, integration, and quality.

  Cons:

  • Limited marketing and partner ecosystem: Ab Initio has acknowledged that it has struggled to scale up its marketing and partner efforts. It has also focused more on engaging with IT technical personnel. Ab Initio is making a concerted effort to engage directly with business buyers, but this indicates a potential challenge in broader market awareness beyond its large enterprise customers.
  • Cost and complexity: Customers report concerns about vendor lock-in, given the complexity and required customization of enterprise solutions. They also report perceived higher costs than competitors, leading to many evaluating alternatives.
  • Platform as a service (PaaS): Ab Initio is transitioning from a pure software deployment to a PaaS model to simplify deployment and maintenance, but this PaaS offering is not yet generally available. Currently, containers are provided for deployment within the customer's tenant, and Ab Initio maintains and upgrades them. Customers seeking a fully managed, hosted PaaS experience for their metadata solution might need to wait for the future release in early 2026.


• Alation - Leader
  

  Pros:

  • Support for active and continuous metadata utilization: Alation's platform supports active metadata management by enabling real-time and continuous monitoring, automated alerts for metadata changes, and integrated workflows that guide users toward maintaining metadata trust and data quality. This approach is valuable for organizations looking to leverage their metadata for governance automation, AI readiness and generative applications.
  • Integrated collaboration tools: Alation incorporates social and collaborative features within its data catalog. Users can engage directly through embedded discussion platforms, annotations, and sharing functionalities that connect business and technical users in the context of metadata assets. This creates a community-driven approach to data governance, where shared insights and feedback loops continuously improve metadata relevance and quality.
  • Shorter time to value: Customers recognize Alation's quick deployment cycles and competitive pricing. The platform provides immediate insights through automated metadata extraction and self-service, as well as intuitive search capabilities, making it attractive for those seeking metadata-driven decision making and operational efficiency benefits.

  Cons:

  • User experience concerns: While recent improvements in navigation, customization, and visual design have made Alation's solution more approachable for a broader audience, including business users, some users have noted that certain advanced features still necessitate technical expertise and training. Ongoing improvements, such as streamlined workflows and improved onboarding resources, will continue to reduce training requirements.
  • Scalability in legacy environments: Alation's advanced capabilities are architected to scale across large data estates. Legacy systems often lack modern integration capabilities. Alation's open architecture and platform enable customers to address these challenges by leveraging Alation's services and integration options. Despite Alation's assurances of robust scalability and positive customer feedback, enterprises are advised to carefully evaluate integration challenges before proceeding with deployment in legacy settings.
  • Agentic platform vision versus current capabilities: Alation's focus on agentic data management and advanced features is promising, but many capabilities are new or evolving. Customers should validate the robustness and readiness of these features for mission-critical use cases.


• Alex Solutions - Visionary
  

  Pros:

  • Extensive and open connector ecosystem: Alex invests R&D in its native connector framework to deliver breadth and automation across the ecosystem. The platform delivers over 110 prebuilt connectors for cataloging, lineage and profiling. Alex also provides OpenMetaHub, an SDK-like framework that enables customers and partners to build and contribute their own connectors, agents, and data playbooks. This open design extends the platform's reach to proprietary and niche technologies, making it suited for complex enterprise technology environments.
  • Knowledge graph and flexible architecture: Alex is built on a graph-powered, ontology-driven platform with an integrated knowledge graph and native lineage capabilities. The platform's architecture enables integration with all metadata types, allowing Alex to function as an active metadata repository across distributed systems. Its bidirectional integration capabilities support metadata exchange and synchronization with other applications.
  • Enterprise-scale delivery: Alex is characterized by its fast deployment and enterprise-scale coverage. Implementations for the full platform are typically completed within 14 days, with ERA dashboards going live within 10 days. Alex governs over 35 million assets in a single catalog for large enterprises and handles up to 500 million data assets under management across various environments.

  Cons:

  • Market growth and sales strategy: Alex remains profitable and debt-free, but smaller than its competitors. Eighty percent of its revenue comes from its own sales force. Reliance on its own sales teams, with limited operating resources, contributes to limited customer growth. The company is extending its reach beyond APAC, with growing partnership with system integrators and regional resellers in North America and EMEA.
  • Targeted industry and enterprise focus: Alex focuses much of its sales and marketing on regulated industries where governance maturity drives early adoption, although the tools can also be used in various industries. In addition, it primarily targets midsize or large enterprises, with less focus on smaller organizations, which limits sales growth.
  • Initial familiarization required for full capabilities: Some customers, particularly early users, express concerns about complex interfaces at first. This can lead to a longer learning period through familiarization, and guided onboarding typically addresses this over time. Fully leveraging platform capabilities requires training. The recent releases in the Data Discoverer interface and Alex Copilot, with simplified navigation and contextual assistance, help users come onboard.


• Ataccama - Niche Player
  

  Pros:

  • Unified data catalog: The Ataccama Data Catalog is crucial for automated metadata discovery, continuous scanning of data sources, and built-in data profiling. This helps organizations maintain an up-to-date repository of metadata. The catalog also supports business glossaries to ensure that the meaning of data is uniform across the organization.
  • Automated metadata enrichment: The integration of AI in metadata management supports functions such as automated classification, extraction, and annotation of metadata from multiple data sources.
  • Innovation: Ataccama has continuously invested in expanding its AI capabilities, such as the introduction of the ONE AI agent, which automates generating and applying data quality rules. Additionally, the company's ability to offer specific solutions, like the free Snowflake Native App for data engineers, indicates strong market orientation and responsiveness to customer needs.

  Cons:

  • Lack of a SaaS deployment option: Ataccama Cloud is now using a single-tenant architecture (PaaS). For enterprises that prioritize a multitenant SaaS model to minimize operational overheads and reduce deployment complexity, the current cloud option might represent a barrier to adoption. Organizations that require rapid, scalable and fully managed SaaS solutions may experience constraints in flexibility until the SaaS deployment option is implemented.
  • Focus on structured data: Ataccama currently offers native support primarily for text-based data types. Most of the capabilities related to processing unstructured data — such as parsing multimedia content — are based on Snowflake. For unstructured data, integrations with external services are required, meaning that the native functionality is limited.
  • Reliance on consulting and training: Customers have noted that the platform's complexity drives a dependence on Ataccama's consulting services. Although comprehensive training materials are available, the feedback suggests that organizations may need to invest significantly in professional services to fully realize the platform's benefits.


• Atlan - Leader
  

  Pros:

  • Go-to-market strategy: Atlan focuses on delivering business value with data across enterprises. Atlan's ability to articulate value and identify clients with a vested interest in high data investments is key to its success, as shown by its market traction and adoption. In particular, Atlan has been focusing on helping organizations that have failed in prior metadata management initiatives.
  • Product vision: Atlan's vision of being the metadata control plane to capture, unify, and understand enterprises' data estates is central to supporting all consumption and AI use cases and providing the necessary context and data for agentic solutions.
  • Ease of use: Customer references praise the ease of use of the technology, and particularly the lineage and search capabilities that are core to all metadata management use cases, particularly data usability enablement and data governance.

  Cons:

  • Competition due to market consolidation: Pure metadata solutions like Atlan will face competition in a market that has seen the emergence of metadata management capabilities as part of cloud service providers' data ecosystems or broader data management platforms.
  • Product evolutions: Atlan has an aggressive delivery roadmap and iterates rapidly on its product delivery, but while this is needed in a highly changing market, it can also cause confusion with clients. For example, some are reporting that documentation is not up-to-date or that feature delivery timelines lack clarity, which impacts enterprises' implementation roadmaps.
  • Customer support: By default, customers interact with support online via the customer portal, emails, and Slack/Teams. A few clients report that it can be tedious to interact with customer support and track support cases, and that case resolution can be slow.


• Coalesce - Visionary
  

  Pros:

  • Product strategy: Coalesce aims to define data management by unifying data transformation, metadata and governance in a single AI-powered platform. This aligns with the market convergence across adjacent data management markets. This vision will appeal to clients who have been struggling to build a data ecosystem by integrating multiple technologies.
  • Sales execution: Coalesce has been focusing on sales execution and is quickly gaining market traction. In addition to its presence across the U.S., France, the U.K., and DACH, it has added offices in New Zealand and Australia. It has also expanded its partner network across service providers and technology partners.
  • User experience: Coalesce provides an intuitive user experience augmented with generative AI, addressing various personas from data engineers, data management experts and data stewards to business users, and combines capabilities in a very intuitive manner across data preparation, data cataloging, metadata management, and data governance.

  Cons:

  • Completeness of product capabilities: Coalesce's product offering for metadata management solutions covers all of the major requirements, but has some gaps in data lineage, impact analysis and semantics. For example, lineage is only automatically computed upfront without providing lazy or eager evaluation of lineage. The vendor does not support graph technologies, like exploiting metadata by leveraging a knowledge graph.
  • Confusing market positioning: Coalesce's positioning and capabilities span multiple markets, forcing enterprise clients to compare Coalesce not only for its metadata management capabilities, but also for its overall product portfolio, particularly for its data integration. This can be a challenge for organizations that have a mix of technologies in their data management ecosystem or who wish to standardize on a converged data management platform.
  • User groups and communities: Coalesce currently does not have user groups or communities where users can share insights and experiences. All the support requests or product feedback sharing have to go through the vendor's official channels. This limits the opportunity for the vendor to directly gather feedback on their product and scale customer support capacity.


• Collibra - Challenger
  

  Pros:

  • Market presence: Collibra's brand is well-established in the market and is frequently mentioned in Gartner inquiries; it also has a strong partner network across technology vendors and service providers, which further drives market traction.
  • Product vision: It has accelerated the delivery of its vision for enterprises to confidently use data and AI via the acquisition of Deasy Labs and Raito, which bring unstructured data governance, security, and governance of data products to deliver a semantic graph of organization's business entities.
  • Broad spectrum of capabilities: Collibra is one of the largest vendors in the Magic Quadrant, with a broad range of capabilities, including a large set of connectors, particularly around data governance, data observability, data lineage and semantics.

  Cons:

  • Lack of metadata management solution market focus: Collibra's core use cases are primarily data governance, including policy enforcement. It is further extending into AI governance. While these use cases require strong metadata management capabilities, and these capabilities are foundational to Collibra's platform, Collibra has not demonstrated a strong marketing strategy in supporting wider use cases via its metadata capabilities.
  • Pricing and licensing: Based on Gartner inquiries, Collibra customers express limited satisfaction with the complexity of its pricing options, the increasing subscription costs, lack of pricing flexibility, and poor negotiation ease. However, Collibra's new pricing strategy should alleviate the complexity and elevated costs that customers have identified.
  • User friendliness: In inquiries and client feedback, Collibra clients mention that while Collibra attempts to address various user personas, it is complex, particularly to business users, and may have an initial steep learning curve.


• DataGalaxy - Niche Player
  

  Pros:

  • Multilingual support: The platform leverages AI for enhanced user experience and automation in metadata management. This includes a chatbot (Blink), content generation, and live multilingual capabilities that can translate all metadata content into multiple native languages (e.g., German, French, Spanish, Portuguese, Dutch) without needing to duplicate content, effectively removing language barriers.
  • Strategic portfolio management with Strategy Hub: Through the acquisition of YOOI, DataGalaxy extended its offering with a Strategy Hub that enables customers to manage their data initiatives like a financial portfolio. The hub directly connects execution to measurable business goals and strategies. It provides templates and advisors to drive business alignment and identify potential use cases.
  • Persona-based and collaborative experience: The platform is designed to offer a tailored, persona-driven experience for C-suite leaders, business teams, data teams, AI agents, and engineers, fostering collaboration across all stakeholders. Its Data Knowledge Studio (a tool in Metadata Hub) bridges the gap between executives, technology and business, ensuring everyone can contribute to and benefit from metadata knowledge.

  Cons:

  • Vision and marketing strategy: DataGalaxy's marketing strategy has been skewed more toward the data governance use case than other, more comprehensive and emerging metadata use cases, such as supporting AI-ready data and enabling modern architectures. This may potentially impact the vendor's future roadmap planning.
  • Inflexibility of pricing model: DataGalaxy's pricing model is based on the number of users and the number of connectors. Although the vendor offers unlimited viewers as a benefit, organizations requiring a large number of users or wanting to connect various data sources with complicated and diverse environments are likely to see a high cost for their implementation. This may cause challenges to scaling with this model later on.
  • Connectors to on-premises environments: DataGalaxy currently provides 70+ connectors out of the box. However, in the Gartner Peer Insights portal, some customers comment that there are not sufficient connectors for on-premises environments and that using some of them is difficult. Overall, on-premises integration is not easy.


• IBM - Leader
  

  Pros:

  • Hybrid cloud support: IBM supports on-premises, multicloud and SaaS deployments for metadata management. This capability extends to deploying its metadata products in environments such as air-gapped on-premises systems and connecting to on-premises data sources. This flexibility has been a significant factor in its growth, particularly in regions like Latin America.
  • End-to-end AI-powered metadata management platform: IBM offers an integrated platform that spans the entire data stack, from the persistence layer through to data consumption, including data, model, and application layers. This end-to-end ownership allows IBM to embed AI automation and metadata intelligence throughout the platform, providing an integrated user experience.
  • Commitment to open standards and ecosystems: IBM is embracing open standards and open ecosystems in metadata management. Key examples include support for OpenLineage, Open Data Contract Standard (ODCS), and Model Context Protocol (MCP), which facilitates metadata exchange across diverse tooling.

  Cons:

  • Evolving product portfolio: IBM legacy metadata products, such as IBM Knowledge Catalog, Manta and Data Product Hub, are still available stand-alone. IBM customers should confirm whether bundled features meet all their needs or if a stand-alone product is needed for specific requirements.
  • Legacy IKC and Manta limitations: Customers report that IKC has limited connectivity to certain data sources (such as SAS), metadata description, and custom attribute creation. The legacy version of IBM Manta Data Lineage is complex and more technical-user-oriented, making it difficult for nontechnical users. IBM has addressed this usability gap through the new unified lineage capabilities in watsonx.data intelligence.
  • Complex software solutions: Some IBM customers comment that its solutions are complicated, expensive, large-scale and suitable only for large enterprises with skilled IT professionals. IBM has worked to address this concern with new easy-to-consume SaaS-based plans that have low-entry-point pricing, implementation accelerators, preconfigured templates and training programs.


• Informatica - Leader
  

  Pros:

  • Market understanding: Informatica demonstrates a comprehensive understanding of how to utilize metadata effectively for current and emerging metadata-driven AI and business use cases. Informatica continues to adapt to changing market requirements, as demonstrated by its CLAIRE AI suite of tools embedded across all IDMC products.
  • Broad data management support: Informatica has strong integrations across the broader data management market, leveraging metadata captured from Cloud Data Governance and Catalog to enable more effective data management beyond core metadata use cases. IDMC leverages metadata to drive insights, suggestions and automation for adjacent data management activities.
  • Partner ecosystem: Informatica has a strong global ecosystem, with over 500 partners across its product portfolio. These include system integrators, independent software vendors, and technology and service providers.

  Cons:

  • Uncertainty from Salesforce acquisition: Informatica states that its product portfolio, including IDMC and Informatica Cloud Data Governance and Catalog, will continue. Its acquisition by Salesforce creates uncertainty regarding Informatica's long-term strategic direction, product offerings, pricing options, and commercial models postacquisition.
  • High cost and pricing model: Informatica's pricing model, using Informatica Processing Units (IPUs), requires customers to forecast their monthly or annual usage and expected spend. Because IPUs cannot be carried between periods, and many clients are not experienced in estimating usage, some customers report that Informatica's cost of ownership is relatively higher than its competitors. Informatica also offers flexible pricing options for customers with irregular usage patterns.
  • Cloud-only and end of software maintenance (EoSM) announcements: Informatica has shifted its product approach to lead with cloud-only solutions (IDMC). Some existing clients are concerned with challenges in migration to the cloud, and also support for on-premises and hybrid solutions. Prospective customers of on-premises solutions should be prepared for potential EoSM announcements over the next few years (for example, for the legacy product, Enterprise Data Catalog) and the impact of this cloud-only approach while evaluating Informatica's metadata solutions.


• OvalEdge - Niche Player
  

  Pros:

  • AI chatbot: OvalEdge's agentic AI chatbot, askEdgi, enables users to ask and receive answers to both data-related and metadata-related questions in natural language. It leverages a metadata knowledge graph to facilitate data discovery and analytics, with multiple agents running behind the scenes for tasks like data discovery and permission checks.
  • Connectivity: The OvalEdge platform provides over 150 connectors to various data sources, including data lakes, databases, SQL servers, business applications like SAP and Salesforce, and API systems. These connectors are developed internally, often in collaboration with customers, and offer access to user permissions and code parsing, allowing for better data understanding.
  • R&D investment and workflow management: OvalEdge prioritizes its R&D investment and has a staff of over 100 people dedicated to R&D (one-third of the total headcount) and a $12 million budget. Its product development focuses on many areas, including workflow management. OvalEdge provides a complete workflow system that can be configured to manage complex processes, particularly for data access requests in large enterprises.

  Cons:

  • AI limitations: OvalEdge uses AI for metadata curation, but AI curation may produce hallucinations because it doesn't know the business context. A human-in-the-loop is required to overcome the limitation. The vendor developed the AI Question Wall to solicit human input for complete and accurate business-contextual metadata.
  • Pricing model: OvalEdge's pricing model is based on connectors and product features. If a required connector is not available, development will incur an extra cost (though at a reduced rate, with OvalEdge retaining the IP). The total cost of ownership can increase significantly with the number of data sources to be connected and the specific features required.
  • Marketing execution: OvalEdge participated in relatively few marketing or partner events over the last 12 months. The vendor is rarely mentioned in Gartner client inquiries, and it is less likely than other vendors in this report to be shortlisted in competitive situations seen by Gartner.


• Precisely - Niche Player
  

  Pros:

  • Engagement strategy: Precisely offers a portfolio of datasets and prelinks them with other leading data vendors through its Data Link partner program to enable data enrichment. The vendor also offers guidance on AI readiness, AI governance, and metadata strategies through its Data Strategy Consulting, and Precisely also has strong relationships with major consulting groups and partners for further expertise.
  • Foundational metadata layer: Precisely enables a common metadata foundation, which is shared across its entire Data Integrity Suite. This foundation ensures faster time to value and interoperability across all its solutions, including data integration, data quality, and data governance.
  • Extensive connectivity and deployment options: Precisely continues to expand connectivity through new source connectors covering enterprise and third-party metadata sources, leading to faster onboarding. The vendor offers flexible deployment options, including SaaS, on-premises, and hybrid cloud, allowing customers to deploy services where their workloads reside, effectively supporting complex environments.

  Cons:

  • Maturity of AI offerings: At the time of this evaluation, Precisely has not taken advantage of AI layers as effectively as many of its competitors. The vendor is working to enhance its AI offerings and provide additional help to customers in accelerating their AI initiatives.
  • Positioning and market confusion: Customers report confusion over where Precisely can enable metadata management within broader data initiatives (data integration, data quality, etc.), as many are not aware of Precisely's metadata capabilities. Precisely's Data Integrity Suite enables capabilities across many data management markets, and potential customers should be aware of the breadth of Precisely's offerings.
  • Product pricing and value: The Precisely Data Integrity Suite is offered as multiple interoperable services with clients reporting complex pricing and licensing discussions. Potential customers should be aware that solutions often are positioned to include additional capabilities beyond metadata management. Precisely recently released a new pricing model — usage-based pricing based on value metrics and modular licensing that enables customers to adopt capabilities at their own pace.


• Quest Software - Challenger
  

  Pros:

  • Strong data governance and modeling capabilities: Quest's products highlight its heritage of robust data modeling and governance, with features such as semantic layers, virtual data stewards, and automated governance policy enforcement. These capabilities support advanced stewardship, mapping meaning for AI, and ensure high-quality, trusted data assets.
  • Innovative data marketplace and data productization: The introduction of a data marketplace allows organizations to curate, score and monetize datasets and AI models as data products. This approach makes data easily discoverable and accessible, fostering a "data-as-a-product" mindset and enabling economic rationalization of governance.
  • Focus on automation and operational efficiency: Quest erwin directly integrates automation into the process of building and maintaining data models and AI workflows, reducing the risk of errors and saving time. It also applies data governance rules automatically as work is done, so teams don't need to interrupt their processes for separate compliance checks.

  Cons:

  • Market position: Quest is changing its products and adding AI features. Its focus on building a broad data management platform may cause confusion to buyers who are only interested in metadata management.
  • Incorporation of LLM use cases: The rapid evolution in artificial intelligence and GenAI means that incorporating LLM use cases into metadata management is becoming increasingly important. Quest erwin, however, has lagged behind competitors in incorporating LLM use cases. Organizations looking to harness new AI-driven insights from their data should review available and related planned capabilities and delivery timeframes from the vendor.
  • Economic considerations: While the Quest erwin platform offers extensive features, potential customers should carefully evaluate the total cost of ownership against the functional benefits offered. Quest's platform strategy aims to reduce operational overhead and customer complexity, aligning with industry guidance that converged platforms can significantly reduce costs and operational efforts.


• ServiceNow (data.world) - Visionary
  

  Pros:

  • Product strategy: Through the acquisition of data.world, ServiceNow positions itself to build a converged data ecosystem that powers a data control tower, which powers all data, AI, and agentic AI use cases that converge data and application requirements.
  • Flexibility and ease of use: Client feedback through inquiries and Gartner Peer Insights praises the ease of use and flexibility of implementation of the solution. For example, the knowledge graph supporting the solution offers great flexibility in collecting, managing metadata and building ontology.
  • Improved sales execution and go-to-market postacquisition: data.world will benefit from both sales and marketing as part of ServiceNow. In particular, its direct and indirect sales channels should reach a wider set of customers, starting with the existing ServiceNow installed base.

  Cons:

  • Uncertainty for existing data.world customers: As part of ServiceNow, data.world will be focusing on developing ServiceNow's catalog and governance. As a result, its ability to continue delivering to the requirements of its existing installed base, which may or may not have any future focus on ServiceNow AI platform or Workflow Data Fabric of ServiceNow, might be challenged.
  • ServiceNow is new to data management: ServiceNow is expanding as a data management platform vendor, but this will require expanding its product portfolio significantly through organic development, acquisitions, or partnerships, which will take time and development in an increasingly competitive market.
  • Administration capabilities: data.world is missing features for proper versioning, concurrent authoring of metadata. These capabilities are key to scaling to large deployments with large numbers of concurrent users.


• Solidatus - Niche Player
  

  Pros:

  • Comprehensive data lineage: Solidatus provides very strong, fine-grained data lineage capabilities and has a strong success record in highly regulated environments such as the finance, healthcare, and government sectors. The solution also provides time-travel capabilities to support strict auditing requirements and enable future planning.
  • Strong partnerships: Solidatus is partnered with Microsoft as a preferred data lineage partner supporting Microsoft Purview and Fabric. In addition, Solidatus has strong relationships with industry partners (EY, KPMG, etc.), and other major CSPs.
  • Aligned product roadmap: Solidatus has a strong understanding of the market overlaps with other data management offerings, targeting broader capabilities enabled by metadata and lineage. The vendor also has a strong strategy for leveraging AI in future product innovations.

  Cons:

  • Solution complexity: Solidatus continues to improve its user interface, but customers report that the UI is often difficult to use due to complex visuals and the number of customizations required, leading to challenges for nontechnical users.
  • Market visibility: While Solidatus' capabilities can support multiple verticals, the product is perceived as a niche vendor, only focusing on specialization in data lineage use cases in highly regulated industries, like finance, and doesn't appear as frequently in competitive evaluations for metadata solutions in other industries.
  • Narrow customer use-case focus: Solidatus is well-established in regulatory and compliance-oriented use cases, focusing on data governance, privacy, and risk management. However, the number of customers using Solidatus as a solution for other metadata use cases remains low.

Reference

• Gartner, Magic Quadrant for Metadata Management Solutions, 19 November 2025, ID G00808349

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