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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EMETADATA MANAGEMENT DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EMetadata management is the practice of creating, maintaining and governing information about data, such as its structure, meaning, ownership, lineage, sensitivity and usage. With a mature metadata management program, data can be discovered, understood, trusted and governed across an organization.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-397c6ffc02","text":"\u003Cp\u003EMost organizations ran on imperfect metadata management for years — functional enough for quarterly reports and executive dashboards, held together by experienced analysts who knew which tables to trust, which definitions to use, and which numbers to quickly recalculate before putting them in front of leadership. This process became harder to sustain when organizations started using data for more than reporting, when data began moving into automated decisioning systems, ML pipelines and AI. All the context that analysts had been supplying vanished.\u003C/p\u003E\n\u003Cp\u003EAutomated systems don’t know which tables the senior analyst avoids. They can’t apply judgment about whether a definition is being used consistently across source systems. Without relevant metadata, AI models will use whatever data they’re given, encode whatever inconsistencies are present, and produce outputs that reflect the undocumented assumptions made when it was generated — confidently, at scale, and in ways that are very difficult to trace back to the source when something goes wrong.\u003C/p\u003E\n\u003Cp\u003EMetadata management is not a new discipline. But as data is increasingly input into automated systems, the cost of neglecting it can’t be ignored.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-metadata-management":{"id":"title-v2-15821b5f60","additionalClasses":"anchor-title anchor-title--what-is-metadata-management","type":"heading2","lines":["What is metadata management?"],":type":"snowflake-site/components/title-v2"},"text_what-is-metadata-management_0":{"id":"text-e75087652a","text":"\u003Cp\u003EMetadata management is the set of practices, policies and tools that govern how metadata is created, maintained, enriched and made usable across an organization’s data assets. Metadata provides descriptive information about data: a table name, a schema, an owner, an update timestamp. Metadata management is the governance process that keeps that information accurate, complete, connected and usable.\u003C/p\u003E\r\n\u003Cp\u003EFor example, a minimal metadata record might include field names, data types and row counts. A mature metadata management program connects that technical foundation to business context (\u003Ci\u003EWhat does this field mean? Who owns it? Has it been certified?\u003C/i\u003E), operational history (\u003Ci\u003EWhere did this data come from? How has it moved? Who has accessed it?\u003C/i\u003E), and governance controls (\u003Ci\u003EWhat classification does it carry? What policies apply? What regulatory scope does it fall under?\u003C/i\u003E).\u003C/p\u003E\r\n\u003Cp\u003EMetadata management typically spans three dimensions:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ETypes of metadata:\u003C/b\u003E Technical metadata describes structure and physical characteristics. Business metadata explains what data means in an organizational context. Operational metadata captures how data moves, changes and gets used.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EOwnership and stewardship:\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-stewardship/\"\u003EData stewards\u003C/a\u003E, data owners and data engineering teams are each responsible for different aspects of metadata quality. Defining those responsibilities and maintaining them is part of metadata management.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EUsability:\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-catalog/\"\u003EData catalogs\u003C/a\u003E, data dictionaries, business glossaries, metadata repositories, tagging taxonomies and lineage tools all work together to make metadata useful.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EMetadata management is foundational to data governance. It provides the shared context that makes data discovery, access control, lineage tracking, policy enforcement and compliance reporting possible at scale. Without reliable metadata, teams lack understanding of what the data means, where it came from, who owns it, whether it’s trustworthy or how it should be governed.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-metadata-management-matters":{"id":"title-v2-27684b3451","additionalClasses":"anchor-title anchor-title--why-metadata-management-matters","type":"heading2","lines":["Why metadata management matters"],":type":"snowflake-site/components/title-v2"},"text_why-metadata-management-matters_0":{"id":"text-505f8692f3","text":"\u003Cp\u003EMetadata management turns data from an unmanaged inventory of assets into a discoverable, trusted and governable resource. It also determines whether an organization can actually do the things it claims to do with data.\u003C/p\u003E\n\u003Ch3\u003EData discoverability\u003C/h3\u003E\n\u003Cp\u003EWithout managed metadata, analysts find data through tribal knowledge — asking colleagues, searching Slack, querying systems they have access to rather than systems that have the right data. A well-maintained metadata layer allows users to search by business term, domain, owner, classification or certification status. It replaces institutional memory with infrastructure.\u003C/p\u003E\n\u003Ch3\u003EData trust\u003C/h3\u003E\n\u003Cp\u003EBusiness users are regularly asked to make decisions on data they can’t fully evaluate. Managed metadata gives them the necessary context before acting: who owns the table, when was it last updated, whether it’s been certified, what the field actually means. This context reduces the risk of decisions made on stale, duplicated or misunderstood assets — and reduces the time analysts spend answering questions that documented metadata should answer automatically.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_why-metadata-management-matters_0":{"id":"quote-item-2c1277652a","alignment":"left","layout":"simple","quoteSourceName":"","quoteSourceTitle":"","showQuoteIcon":true,"quote":"Metadata management turns data from an unmanaged inventory of assets into a discoverable, trusted and governable resource.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_why-metadata-management-matters_1":{"id":"text-1678e5f32a","text":"\u003Ch3\u003ECompliance and governance enforcement\u003C/h3\u003E\n\u003Cp\u003EAccess controls, masking policies, retention rules and compliance reporting all depend on accurate metadata about data sensitivity, ownership, lineage and usage. Without this foundation, governance teams rely on manual documentation and point-in-time audits, both of which become stale and fail under regulatory scrutiny. Metadata management moves compliance from a periodic reconstruction effort to an ongoing operational process.\u003C/p\u003E\n\u003Ch3\u003EAI readiness and AI governance\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/ai/\"\u003EAI governance\u003C/a\u003E depends on metadata quality. Models trained on poorly labeled or poorly understood data produce unreliable outputs. Models deployed without lineage records are difficult to audit when regulatory or business questions arise. Model cards, training data documentation, feature store definitions and data observability all require reliable metadata as their foundation.\u003C/p\u003E\n\u003Cp\u003E“While humans can infer meaning from a column, AI agents are literal and context-blind,” \u003Ca href=\"https://www.snowflake.com/en/blog/universal-ai-catalog-data-governance/\"\u003Eexplains\u003C/a\u003E Danielle Kucera, Senior Product Manager at Snowflake. “An agent might recognize ‘TX_LMT’ as a number but can’t infer its currency or regional context — or it might guess that TX_LMT stands for ‘tax limit’ when it actually means ‘tax local municipal total,’ introducing an unfortunate error. The semantic layer would provide the specific definition of the term, acting as a hard guardrail and forcing both agents and humans to abide by official business logic, context and definitions.”\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_types-of-metadata":{"id":"title-v2-15d08228cd","additionalClasses":"anchor-title anchor-title--types-of-metadata","type":"heading2","lines":["Types of metadata"],":type":"snowflake-site/components/title-v2"},"text_types-of-metadata_0":{"id":"text-77b1bef4f6","text":"\u003Cp\u003EEffective metadata management starts with understanding what kinds of metadata exist and what role each plays in governance. Most enterprise metadata falls into three categories:\u003C/p\u003E\n\u003Ch3\u003ETechnical metadata\u003C/h3\u003E\n\u003Cp\u003ETechnical metadata describes the structure and physical characteristics of data assets: schemas, data types, field definitions, row counts, update timestamps, query performance statistics and storage details. Much of it can be collected automatically by data platforms, which makes it the typical starting point for any metadata management program. It supports discoverability, quality monitoring and observability, but it doesn’t tell users what the data means or how it should be governed.\u003C/p\u003E\n\u003Ch3\u003EBusiness metadata\u003C/h3\u003E\n\u003Cp\u003EBusiness metadata explains what data means in an organizational context: business glossary terms, domain classifications, ownership assignments, certification status and definitions for concepts like “customer,” “active subscriber” or “net revenue.” Unlike technical metadata, it typically requires human input from data stewards, domain owners and governance teams.\u003C/p\u003E\n\u003Ch3\u003EOperational metadata\u003C/h3\u003E\n\u003Cp\u003EOperational metadata describes how data moves, changes, and gets used: lineage records, job run history, pipeline execution logs, usage frequency, access patterns and audit activity. It supports lineage tracking, compliance reporting, impact analysis and operational troubleshooting. It’s also the metadata most often missing when a governance or audit question gets escalated, because it requires capture at runtime rather than at asset creation.\u003C/p\u003E\n\u003Cp\u003EStrong metadata management programs connect all three types, because understanding where data lives without knowing what it means, or knowing what it means without knowing how it moves, leaves governance gaps that surface at the worst times.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_types-of-metadata_0":{"id":"card-v2-bcd7d5f5bf","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EMerkle improved its data development cycle by 64% and achieved up to 20% in cost savings (as of March 2025) by consolidating sensitive marketing data, governance and collaboration workflows in Snowflake. The team also built an RFP response solution with Document AI in Snowflake Cortex, reducing data entry for at least 25 team members.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Merkle"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/merkle/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Read the full case study"},"image":{"id":"image","alt":"Merkle logo","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8312d51a-ff46-4b26-9cb7-0b237526cf7e/merkle%25403x.png?preferwebp=true&quality=85","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"},"title_putting-metadata-management-to-work":{"id":"title-v2-a535a41a53","additionalClasses":"anchor-title anchor-title--putting-metadata-management-to-work","type":"heading2","lines":["Putting metadata management to work"],":type":"snowflake-site/components/title-v2"},"text_putting-metadata-management-to-work_0":{"id":"text-e7bc494505","text":"\u003Cp\u003EMetadata management involves several practices and capabilities — with each one addressing a different aspect of how organizations document, govern and use their data.\u003C/p\u003E\r\n\u003Ch3\u003EData classification\u003C/h3\u003E\r\n\u003Cp\u003EData classification is the process of tagging data assets according to their sensitivity, regulatory scope, domain and intended use. When implemented well, those tags travel with the data. For example, a column tagged as PII is automatically masked for unauthorized users, flagged in compliance reports, and subject to retention policies without requiring a separate manual step at each handoff. Classification is usually the first metadata management practice a governance team implements because it’s the one most directly connected to security and regulatory risk.\u003C/p\u003E\r\n\u003Cp\u003EThe most common reason governance policies aren’t enforced is that nobody labeled the data they were supposed to govern. Access controls, masking rules and retention schedules all require knowing what a data asset is before they can determine how it should be handled. When that labeling is missing or inconsistent, policy enforcement must be manually handled — which means it becomes intermittent.\u003C/p\u003E\r\n\u003Cp\u003ERead more: \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-classification/\"\u003EData Classification >\u003C/a\u003E\u003C/p\u003E\r\n\u003Ch3\u003EBusiness glossary\u003C/h3\u003E\r\n\u003Cp\u003EA business glossary is a governed catalog of approved business terms, each defined by a data steward or domain owner, linked to the tables and columns that implement it, and maintained so definitions remain accurate as business meaning evolves. A glossary makes official definitions findable rather than buried in documentation nobody updates. Governance programs that establish a glossary alongside a catalog typically see stronger analyst adoption, because users can search for data in the language they actually work in rather than trying to reverse-engineer meaning from column names.\u003C/p\u003E\r\n\u003Cp\u003EThe importance of a business glossary is evident when looking at how different teams interpret terms. For example, the reason two teams produce different numbers from the same data platform is because “net revenue” means something different in finance than it does in sales ops. Analysts spend time reconciling reports that should agree, and the underlying cause — inconsistent business metadata — was never addressed.\u003C/p\u003E\r\n\u003Ch3\u003EMetadata governance\u003C/h3\u003E\r\n\u003Cp\u003EMetadata governance applies governance principles to metadata itself. It defines who is responsible for metadata quality, what standards metadata must meet, how it’s reviewed and updated, and how issues are detected and remediated. In practice, it covers all three metadata types: business metadata requires stewardship over glossary terms and certification; operational metadata requires reliable lineage and access records; technical metadata requires consistency across schemas and platform-generated output.\u003C/p\u003E\r\n\u003Cp\u003EA data catalog is not self-maintaining. Without a deliberate process for keeping metadata accurate and current, even a well-designed catalog fills with stale definitions, misclassified assets, unclear ownership and broken lineage records — often within months of launch. If the catalog becomes another system teams route around rather than rely on, the organization is back where it started, minus the implementation cost.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_putting-metadata-management-to-work_0":{"id":"text-4c63c5891f","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDon’t treat metadata management as a onetime cataloging exercise. Many organizations invest in a data catalog, document assets once and assume the work is done. But without ongoing stewardship, ownership, classification and lineage updates, metadata quickly becomes stale — causing users to lose trust in the catalog and return to tribal knowledge, spreadsheets and ad hoc workarounds.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_putting-metadata-management-to-work_1":{"id":"text-e962dbf5bd","text":"\u003Cp\u003EPrograms also tend to fail when metadata requirements are too rigid at the outset. If teams are asked to complete long forms, map every field to a glossary term or meet exhaustive documentation standards before publishing data, adoption slows and metadata quality often gets worse rather than better. Mature programs usually start with the minimum required fields needed for discovery, ownership, classification and trust, then expand metadata requirements as workflows mature.\u003C/p\u003E\n\u003Ch3\u003EActive metadata\u003C/h3\u003E\n\u003Cp\u003EActive metadata uses machine learning to make metadata management self-improving rather than entirely dependent on human input. New assets can be tagged on ingestion, stale descriptions can be flagged automatically, glossary matches can be suggested rather than manually assigned, and lineage breaks can be detected before they surface as reporting errors.\u003C/p\u003E\n\u003Cp\u003EWhere passive metadata sits and waits to be consulted, active metadata participates in governance workflows — recommending classifications, identifying duplicate assets, surfacing quality issues and notifying stewards when review is needed. It allows programs to scale without requiring proportional growth in the stewardship team, and these capabilities are now embedded in modern data governance platforms rather than tacked on separately.\u003C/p\u003E\n\u003Ch3\u003EMetadata standards\u003C/h3\u003E\n\u003Cp\u003EMetadata standards define the schemas, vocabularies and interchange formats that allow metadata to be shared and understood consistently across systems and organizational boundaries. Relevant standards include ISO/IEC 11179 for metadata registries and naming conventions, Dublin Core for descriptive metadata about digital resources, DCAT for data catalog vocabulary, and the \u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/fair-principles/\"\u003EFAIR principles\u003C/a\u003E (findable, accessible, interoperable, reusable) as a framework for evaluating whether data infrastructure actually delivers the outcomes organizations claim to want.\u003C/p\u003E\n\u003Cp\u003EIndustry-specific standards may be relevant as well, such as HL7/FHIR in healthcare or ISO 20022 in financial services. Conforming to recognized standards reduces conversion overhead and makes metadata legible to systems and regulators that have no context for how an organization has chosen to document things internally.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_metadata-management-on-snowflake":{"id":"title-v2-75d9305ae8","additionalClasses":"anchor-title anchor-title--metadata-management-on-snowflake","type":"heading2","lines":["Metadata management on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_metadata-management-on-snowflake_0":{"id":"text-14dde757d2","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E provides built-in metadata management and data governance capabilities directly within the Snowflake platform. Rather than requiring a separate catalog layer that must be synchronized with the platform, Horizon Catalog keeps metadata connected to the assets it describes so metadata can drive governance workflows rather than simply sit alongside them as documentation.\u003C/p\u003E\n\u003Cp\u003ECapabilities include Universal Search for cross-asset discoverability, automated classification for sensitive and PII data at the column level, object- and column-level lineage, business glossary and data dictionary functionality, and tag-based policy enforcement for access controls and masking rules.\u003C/p\u003E\n\u003Cp\u003EHorizon Catalog also supports governance for Iceberg tables and open data formats, which matters for teams running hybrid architectures where data lives both inside Snowflake and in external object storage. Metadata management stays consistent across centralized, federated, and data mesh environments rather than requiring separate governance approaches for each. For organizations operating at scale, that integration model changes the governance equation. Because the metadata is built into the platform, it stays consistent. And because policies are tied to metadata tags, they are enforced automatically at query time—not only when someone remembers to review them.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_metadata-management-on-snowflake_0":{"id":"text-1757291f50","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EOrganizations often fail at governance not because they lack policies, but because they lack context. Metadata management creates that context, enabling data to be discovered, trusted and governed consistently across the enterprise.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-metadata-management","text_what-is-metadata-management_0","title_why-metadata-management-matters","text_why-metadata-management-matters_0","quote_why-metadata-management-matters_0","text_why-metadata-management-matters_1","title_types-of-metadata","text_types-of-metadata_0","card_v2_types-of-metadata_0","title_putting-metadata-management-to-work","text_putting-metadata-management-to-work_0","callout_putting-metadata-management-to-work_0","text_putting-metadata-management-to-work_1","title_metadata-management-on-snowflake","text_metadata-management-on-snowflake_0","callout_metadata-management-on-snowflake_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":items":{"container":{"additionalClasses":"sticky-sidebar","columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-9309e385ff",":items":{"text_943981956_copy_":{"id":"text-b5c45237df","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EIn This Guide\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-6707cd7be4","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-metadata-management\"\u003EWhat is metadata management?\u003C/li\u003E\u003Cli data-anchor=\"why-metadata-management-matters\"\u003EWhy metadata management matters\u003C/li\u003E\u003Cli data-anchor=\"types-of-metadata\"\u003ETypes of metadata\u003C/li\u003E\u003Cli data-anchor=\"putting-metadata-management-to-work\"\u003EPutting metadata management to work\u003C/li\u003E\u003Cli data-anchor=\"metadata-management-on-snowflake\"\u003EMetadata management on Snowflake\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/container"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_132267907":{"id":"flexible-column-container-791915bc70","propertiesId":"hub-faq","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-faq-intro",":items":{"title_v2_copy":{"id":"title-v2-15f1a02436","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Frequently Asked Questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-b4cbf56cd8","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EYour common questions about metadata management, answered by Snowflake experts.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-4bbc917a09","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"What is the difference between metadata management and a data catalog?","richText":"\u003Cp\u003EA data catalog is a tool — a searchable interface for finding and understanding data assets, typically surfacing metadata such as descriptions, owners, lineage and usage statistics. Metadata management is the broader practice that determines whether the metadata in that catalog is accurate, current and governable.\u003C/p\u003E\r\n"},{"title":"What are the three types of metadata?","richText":"\u003Cp\u003ETechnical metadata describes the structure and physical characteristics of data assets: schemas, data types, row counts, update timestamps. Business metadata explains what data means in an organizational context: glossary definitions, domain classifications, ownership, certification status. Operational metadata captures how data moves and gets used: lineage records, pipeline execution history, access patterns, audit logs.\u003C/p\u003E\r\n"},{"title":"How does metadata management support GDPR compliance?","richText":"\u003Cp\u003EGDPR compliance requires that organizations know what personal data they hold, where it resides, who has access, how long it’s retained and where it travels. Metadata management provides the operational foundation for each of these requirements.\u003C/p\u003E\r\n"},{"title":"What happens when metadata management is neglected?","richText":"\u003Cp\u003ENeglected metadata management produces predictable failures. Analysts duplicate work because they cannot find existing assets. Business teams distrust data because they cannot verify its meaning or freshness. Compliance teams spend weeks reconstructing lineage manually for audits that should take hours. AI systems cannot be audited because training \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-lineage/data-provenance/\"\u003Edata provenance\u003C/a\u003E was never captured.\u003C/p\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_623474982":{"id":"flexible-column-container-62b2b82199","propertiesId":"hub-explore-resources-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-1661a2cb4a",":items":{"title_v2":{"id":"title-v2-876f780c67","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Explore Data Governance 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