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callout--general","text":"\u003Cp\u003E\u003Cb\u003EDATA GOVERNANCE DEFINED\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EData governance is the framework of policies, roles, processes and technology that ensures data is managed, protected and used consistently and responsibly across its lifecycle, including ownership, access, quality, classification and auditability.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_overview_0":{"id":"text-4562ec7539","text":"\u003Cp\u003EWhen something goes wrong with data — a compliance finding, a model trained on the wrong population, a metric that turns out to mean different things to different teams — organizations often cannot answer the questions that follow. Ownership is unclear, lineage is incomplete, access records don’t exist or don’t go back far enough. The problem surfaces in a regulator’s office or a post-incident review.\u003C/p\u003E\n\u003Cp\u003EData governance is the practice of building the ownership, classification, lineage and audit controls that make those answers available before they’re demanded. As data moves across systems, clouds, partners and AI workflows, the demands on governance continue to grow. In most organizations, answers about data are much harder to produce than they should be. This guide shows how to change that.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-data-governance":{"id":"title-v2-b656b96e28","additionalClasses":"anchor-title anchor-title--what-is-data-governance","type":"heading2","lines":["What is data governance?"],":type":"snowflake-site/components/title-v2"},"text_what-is-data-governance_0":{"id":"text-472614d18e","text":"\u003Cp\u003EData governance is the system of policies, roles, processes and technology that defines how an organization manages data across its lifecycle. It establishes who owns data, what it means, how it’s classified, who can access it, how quality is measured and how usage is audited. At a practical level, data governance is an operating model for making data trustworthy, protected and usable at scale.\u003C/p\u003E\r\n\u003Cp\u003EA mature data governance program answers four questions:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EWhat data exists, and what does it mean?\u003C/li\u003E\r\n\u003Cli\u003EWho owns it, and who is accountable for its use?\u003C/li\u003E\r\n\u003Cli\u003EWho can access it, share it or use it in AI workflows?\u003C/li\u003E\r\n\u003Cli\u003ECan the organization prove how it was transformed, protected and used?\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EAnswering those questions depends on metadata, data stewardship, data quality, privacy controls, compliance processes and clear accountability. Without these pieces working together, teams cannot reliably govern data.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-data-governance-matters-now":{"id":"title-v2-fa20a93b50","additionalClasses":"anchor-title anchor-title--why-data-governance-matters-now","type":"heading2","lines":["Why data governance matters now"],":type":"snowflake-site/components/title-v2"},"text_why-data-governance-matters-now_0":{"id":"text-fd3f07b0b5","text":"\u003Cp\u003EData governance has become harder because data no longer stays inside one reporting environment. A product usage table might feed analytics, customer support workflows, partner reporting, machine learning features and executive dashboards. A \u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/\"\u003Edata governance policy\u003C/a\u003E that works for one dashboard doesn’t automatically govern every downstream copy, transformation or AI prompt that touches the same data.\u003C/p\u003E\r\n\u003Cp\u003EThis fragmentation is one of the biggest barriers to both effective governance and scalable AI. “When you keep your data in one place for one thing, another place for another thing, governing and securing that data becomes really difficult,” says Baris Gutelkin, Snowflake’s VP of Product, AI. He argues that investment in a single, standardized data foundation across the organization enables more powerful generative AI use cases while simplifying governance and security.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_why-data-governance-matters-now_0":{"id":"quote-item-aad620e929","alignment":"left","layout":"simple","quoteSourceName":"Baris Gutelkin","quoteSourceTitle":"Snowflake’s VP of Product, AI","showQuoteIcon":true,"quote":"When you keep your data in one place for one thing, another place for another thing, governing and securing that data becomes really difficult.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_why-data-governance-matters-now_1":{"id":"text-cc84def527","text":"\u003Cp\u003EMaking that kind of foundation work in practice requires more than centralizing data — it requires consistent, scalable governance. Teams need to classify sensitive columns, attach ownership, trace lineage, enforce masking, monitor freshness and audit usage without relying on tribal knowledge or disconnected spreadsheets. Governance done well makes trusted data easier to find and safer to use — without adding friction for teams that need it.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-data-governance-matters-now_0":{"id":"text-30569a2ada","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003ECOMMON PITFALL\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EOrganizations often treat data governance as a onetime project, rather than an ongoing operating discipline embedded in day-to-day workflows. This leads to unclear ownership, incomplete metadata and controls that fail to propagate as data moves across systems and AI use cases — making issues difficult to trace and audits hard to satisfy.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_data-governance-for-ai":{"id":"title-v2-c7a6b25fde","additionalClasses":"anchor-title anchor-title--data-governance-for-ai","type":"heading2","lines":["Data governance for AI"],":type":"snowflake-site/components/title-v2"},"text_data-governance-for-ai_0":{"id":"text-a879afa8ec","text":"\u003Cp\u003EAI raises the stakes for data governance because governed data may now be retrieved, summarized, transformed or acted on by models and agents. When an AI agent acts on a user’s behalf, questions multiply: which rows did it retrieve, what did it include in a prompt and is there an audit trail for any of it?\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/ai/\"\u003EData governance for AI\u003C/a\u003E focuses on the data AI systems use. This includes training-data provenance; PII and sensitive-data classification; consent and permitted-use controls; bias and representativeness of source data; lineage between data sets, features, prompts, outputs and downstream decisions; and agent access controls and audit trails for retrieval, prompt context and generated outputs.\u003C/p\u003E\r\n\u003Cp\u003EThis is related to \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/\"\u003EAI governance\u003C/a\u003E but not the same thing. Data governance for AI governs the data that AI systems use. AI governance governs the model or system itself: model approvals, evaluations, model cards, monitoring, drift, human oversight and \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-risk-management/\"\u003Erisk management\u003C/a\u003E. The two programs need to connect. A high-risk AI workflow cannot be governed well if the organization does not know which data sources feed it, which sensitive fields may appear in retrieval, who approved the data use and whether outputs can be traced back to governed sources.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003ESee how Snowflake helps govern data for trusted AI:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_data-governance-for-ai_0":{"id":"embed-76686c5ff3","youtubeVideoId":"42GWL5x3Xpg","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_data-governance-principles":{"id":"title-v2-34ccb3e8ad","additionalClasses":"anchor-title anchor-title--data-governance-principles","type":"heading2","lines":["Data governance principles"],":type":"snowflake-site/components/title-v2"},"text_data-governance-principles_0":{"id":"text-0f0d0c5044","text":"\u003Cp\u003EData governance principles guide the choices a program makes about policies, ownership, technology and process. Common principles include:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EAccountability:\u003C/b\u003E Every critical data asset has a named owner responsible for access decisions, quality issues and definition disputes.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETransparency:\u003C/b\u003E Users can see definitions, lineage, quality signals and policy context for data assets they work with.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EData quality:\u003C/b\u003E Governed data is measured against explicit expectations, not assumed to be reliable.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrivacy and security:\u003C/b\u003E Sensitive data is classified, protected and monitored throughout its lifecycle.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EStewardship:\u003C/b\u003E Named stewards maintain definitions, resolve issues and support responsible use across domains.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EStandardization:\u003C/b\u003E Terms, policies and controls are consistent across domains, with exceptions documented and approved.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAuditability:\u003C/b\u003E The organization can demonstrate how data was accessed, changed, shared and used.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EEthical use:\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/data-ethics/\"\u003EData ethics\u003C/a\u003E means that data is used in ways that are fair, non-discriminatory and aligned with user expectations, with mechanisms to identify and mitigate harmful or unintended outcomes.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThese principles show up in concrete mechanisms: ownership fields in a catalog, sensitivity tags on columns, masking policies attached to regulated data, lineage paths for critical reports and audit logs for access reviews.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-frameworks-and-standards":{"id":"title-v2-0b1e80ca76","additionalClasses":"anchor-title anchor-title--data-governance-frameworks-and-standards","type":"heading2","lines":["Data governance frameworks and standards"],":type":"snowflake-site/components/title-v2"},"text_data-governance-frameworks-and-standards_0":{"id":"text-747b78e697","text":"\u003Cp\u003EData governance frameworks and standards help organizations structure a program, define capabilities and decide what to implement first. Some focus on data management maturity, while others focus on IT governance, architecture, quality or cloud controls.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EFramework or standard\u003C/th\u003E\r\n\u003Cth\u003EBest fit\u003C/th\u003E\r\n\u003Cth\u003EWhat it helps govern\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/dama-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EBroad data management program design\u003C/td\u003E\r\n\u003Ctd\u003EData management knowledge areas, roles and disciplines\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EEnterprise data management maturity\u003C/td\u003E\r\n\u003Ctd\u003EOperating model, controls, accountability and maturity\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003ECloud data management controls\u003C/td\u003E\r\n\u003Ctd\u003EGovernance controls for cloud and hybrid data environments\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/cobit/\"\u003ECOBIT\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EIT governance alignment\u003C/td\u003E\r\n\u003Ctd\u003ERisk, controls, accountability and enterprise governance\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/togaf/\"\u003ETOGAF\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EEnterprise architecture alignment\u003C/td\u003E\r\n\u003Ctd\u003EData architecture, application dependencies and architecture governance\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/fair-principles/\"\u003EFAIR Principles\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EScientific and research data reuse\u003C/td\u003E\r\n\u003Ctd\u003EFindability, accessibility, interoperability and reuse\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.iso.org/standard/81745.html\"\u003EISO 8000\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EData quality and master data\u003C/td\u003E\r\n\u003Ctd\u003EQuality requirements, data exchange and master data practices\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://datagovernance.com/the-dgi-data-governance-framework/\"\u003EDGI Data Governance Framework\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EGovernance program design\u003C/td\u003E\r\n\u003Ctd\u003EDecision rights, accountability and policy processes\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EA healthcare organization might use DAMA-DMBOK to define core data management capabilities, CDMC to map cloud controls and HIPAA’s data governance requirements to define access, retention and audit expectations.\u003C/p\u003E\r\n\u003Cp\u003EFrameworks provide structure, but a program still needs owners, metadata, classification, quality rules, access policies, audit processes and technology that can apply those decisions where data is used.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/\"\u003ELearn more about data governance frameworks →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-operating-models":{"id":"title-v2-a97fd0efc9","additionalClasses":"anchor-title anchor-title--data-governance-operating-models","type":"heading2","lines":["Data governance operating models"],":type":"snowflake-site/components/title-v2"},"text_data-governance-operating-models_0":{"id":"text-dc7396816e","text":"\u003Cp\u003EA governance program needs an operating model that matches how the organization works. A global enterprise with dozens of business units cannot govern every table through one central team, but a fully decentralized model could result in inconsistent definitions, duplicated policies and uneven control.\u003C/p\u003E\n\u003Cp\u003EMost organizations choose one of three models:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EModel\u003C/th\u003E\u003Cth\u003EHow it works\u003C/th\u003E\u003Cth\u003EBest fit\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003ECentralized\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EA central governance team defines policies, standards and approvals\u003C/td\u003E\u003Ctd\u003ESmaller programs, highly regulated data or early-stage governance\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EFederated\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EDomains own data locally while following shared governance standards\u003C/td\u003E\u003Ctd\u003ELarge enterprises with strong domain ownership\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EHybrid\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EA central team sets policy and platform standards while domains handle day-to-day stewardship\u003C/td\u003E\u003Ctd\u003EMost mature enterprise programs\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003EA hybrid model is often the most practical. A central team defines classification standards, policy templates, catalog requirements and audit expectations. Domain teams own their data products, maintain definitions, resolve quality issues and approve access based on local context.\u003C/p\u003E\n\u003Cp\u003EWhichever model an organization uses, decision rights need to be explicit. If two teams define “active customer” differently, the governance model should specify who resolves the conflict. If a partner requests access to a sensitive data set, the model should identify who approves the request, what evidence is required and how the decision is logged.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_data-governance-operating-models_0":{"id":"card-v2-061047f9f4","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EThe Town of Gilbert launched 11 public-facing dashboards with Snowflake to improve transparency across services such as crime prevention, mental health response, pothole repairs and recycling. The town uses governance and security capabilities such as role-based access control, metadata fields and dynamic data masking to support trusted access to sensitive public-sector data.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer story: Town of Gilbert"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/town-of-gilbert/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Read the full case study"},"image":{"id":"image","alt":"Town of Gilbert Logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--83f793a0-df9c-45fe-8949-f4e33942489a/gilbert%25403x.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"},"title_the-core-components-of-data-governance":{"id":"title-v2-528b5ab737","additionalClasses":"anchor-title anchor-title--the-core-components-of-data-governance","type":"heading2","lines":["The core components of data governance"],":type":"snowflake-site/components/title-v2"},"text_the-core-components-of-data-governance_0":{"id":"text-10f9cb813b","text":"\u003Cp\u003EA data governance program is based on principles and frameworks, but it runs through specific operating components. These components make governance visible in the systems people use every day: catalogs, tags, lineage graphs, access policies, quality checks, stewardship workflows and audit logs.\u003C/p\u003E\r\n\u003Ch3\u003EMetadata management\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/metadata-management/\"\u003EMetadata\u003C/a\u003E is the context that tells people and systems what a data asset is, where it came from and how it should be used. It can describe a table name, column type, owner, business definition, sensitivity label, freshness target, lineage path, usage pattern or cost profile.\u003C/p\u003E\r\n\u003Cp\u003EMost governance programs rely on three types of metadata:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EBusiness metadata\u003C/b\u003E covers definitions, owners, domains, glossary terms and certification status — it helps teams understand whether a data asset is relevant and approved for use.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETechnical metadata\u003C/b\u003E covers schemas, data types, transformations, dependencies and lineage — it helps engineers and architects understand how data moves and changes.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EOperational metadata\u003C/b\u003E covers freshness, usage, cost, quality results and access patterns — it helps teams monitor whether data is current, trusted and being used appropriately.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/vs-technical-governance/\"\u003ELearn the difference between data governance and technical governance →\u003C/a\u003E\u003C/p\u003E\r\n\u003Ch3\u003EData classification\u003C/h3\u003E\r\n\u003Cp\u003EData classification assigns labels to data based on sensitivity, domain, regulation, including \u003Ca href=\"https://www.snowflake.com/en/fundamentals/what-is-data-sovereignty/\"\u003Edata sovereignty\u003C/a\u003E, or permitted use. For example, a column may be tagged as PII, protected health information, payment card data, confidential financial data or approved training data. Those labels then drive access reviews, masking policies, retention rules, sharing approvals and AI-use restrictions.\u003C/p\u003E\r\n\u003Cp\u003EClassification is especially important because sensitive data is rarely isolated in one place. Email addresses, customer IDs, diagnosis codes, geolocation fields and transaction details often move across pipelines, dashboards and application tables. A governance program needs to identify those fields before it can consistently protect them.\u003C/p\u003E\r\n\u003Ch3\u003EData catalog\u003C/h3\u003E\r\n\u003Cp\u003EA \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-catalog/\"\u003Edata catalog\u003C/a\u003E is the searchable inventory that makes governance usable. It gives analysts, engineers, stewards and business users a place to find data assets, read definitions, review lineage, check owners, inspect quality signals and request access.\u003C/p\u003E\r\n\u003Cp\u003EModern catalogs surface certified data products, attach policy context, show whether a table is fresh enough to use and help teams avoid duplicating similar data sets. A good catalog answers practical questions before someone writes a query: What does this table mean? Who owns it? Is it approved? What downstream assets depend on it? Does it contain sensitive data?\u003C/p\u003E\r\n\u003Ch3\u003EData lineage\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-lineage/\"\u003EData lineage\u003C/a\u003E traces data from source to consumption. It shows how a field, table or metric moves through ingestion, transformation, modeling, reporting, sharing and AI workflows. Lineage can operate at the table level, showing how tables depend on other tables or sources; at the column level, showing how specific fields are transformed or reused; or across systems, showing how data moves across tools, clouds or platforms.\u003C/p\u003E\r\n\u003Cp\u003EIf a regulated column feeds a report, a model or an external data product, lineage helps show where it came from, how it changed and what may be affected if the source changes.\u003C/p\u003E\r\n\u003Ch3\u003EPolicy management\u003C/h3\u003E\r\n\u003Cp\u003EPolicy management is where governance rules become enforceable controls. It includes access policies, masking policies, row-level restrictions, retention rules, data sharing rules, permitted-use policies and exception workflows.\u003C/p\u003E\r\n\u003Cp\u003EA policy should define who can access which data, under what conditions, for what purpose and with what review process. Strong policy management also includes exceptions: some users may need temporary access for an audit, migration or incident response. Governance should capture who approved the exception, why it was granted and when it expires.\u003C/p\u003E\r\n\u003Ch3\u003EData quality\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/a-practical-guide-to-data-quality/\"\u003EData quality\u003C/a\u003E measures whether data is accurate, complete, consistent, current, unique and valid enough for its intended use. A product table used for internal experimentation may have a different quality threshold than a revenue table used for financial reporting or a claims table used in healthcare analytics.\u003C/p\u003E\r\n\u003Cp\u003EA table can have an owner, glossary definition and access policy, but if its records are stale or incomplete, users cannot rely on it. Modern programs shift quality earlier in the lifecycle through data contracts, pipeline tests and continuous monitoring.\u003C/p\u003E\r\n\u003Ch3\u003EData privacy and security\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-privacy/\"\u003EData privacy\u003C/a\u003E governs how personal and sensitive data is collected, used, retained, shared and deleted. Data security governs how data is protected from unauthorized access, misuse or exposure. Both depend on classification, ownership, policies and auditability, which is why they are typically managed within the same governance framework.\u003C/p\u003E\r\n\u003Cp\u003EPrivacy controls may include consent management, data subject request workflows, retention rules, tokenization and masking. Security controls may include role-based access control, row access policies, encryption, monitoring and incident response procedures. Governance connects those controls to data assets — showing which tables contain sensitive data, who can access them, what policies apply and whether usage can be reviewed later.\u003C/p\u003E\r\n\u003Ch3\u003EData sharing and collaboration\u003C/h3\u003E\r\n\u003Cp\u003EGoverned data has to support safe reuse across domains, partners and external ecosystems, not just control access within a single environment.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EData mesh assigns domain ownership while preserving federated governance standards.\u003C/li\u003E\r\n\u003Cli\u003EData products package data with an owner, definition, quality target and lifecycle.\u003C/li\u003E\r\n\u003Cli\u003EData contracts define expectations between producers and consumers, including schema, freshness and quality.\u003C/li\u003E\r\n\u003Cli\u003EClean rooms allow parties to collaborate on governed data without exposing raw records.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EEvery shared data asset carries assumptions: who owns it, what it means, whether it is current, what policies apply and whether the recipient is allowed to use it for the intended purpose. Governance helps make those assumptions explicit and enforceable.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-core-components-of-data-governance_0":{"id":"card-v2-bb3e06b76e","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003ESiemens, a global technology leader, built an enterprise-wide data mesh with Snowflake — enabling 600+ data projects, integrating thousands of data warehouses, and creating a scalable Data Cloud that powers AI, automation and cross-business collaboration.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer story: Siemens"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/siemens-1/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Read the full case study"},"image":{"id":"image","alt":"Siemens logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--1500d7c2-4d82-475e-8f03-5e5d3bef0d83/siemens-transparent.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"},"title_data-stewardship-and-governance-roles":{"id":"title-v2-b2c55e4acd","additionalClasses":"anchor-title anchor-title--data-stewardship-and-governance-roles","type":"heading2","lines":["Data stewardship and governance roles"],":type":"snowflake-site/components/title-v2"},"text_data-stewardship-and-governance-roles_0":{"id":"text-797a2b88b1","text":"\u003Cp\u003EData governance depends on named people with clear decision rights. \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-stewardship/\"\u003EData stewardship\u003C/a\u003E is the operating layer that keeps governance decisions connected to day-to-day data work. In a mature program, stewards work with data owners, custodians, privacy leaders, security teams and a governance council to maintain definitions, monitor quality, review access patterns and escalate conflicts across domains.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ERole\u003C/th\u003E\r\n\u003Cth\u003EGovernance responsibility\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EChief Data Officer\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ESets enterprise data strategy, sponsors the governance program and owns executive accountability for data outcomes\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData owner\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EHolds business authority over a data domain, data product, metric or critical data set\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData steward\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EMaintains definitions, quality expectations, metadata, access guidance and issue resolution for a domain or asset\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData custodian\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EManages the technical environment where data is stored, processed, secured and maintained\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EData protection officer\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EOversees privacy obligations for regulated personal data, especially where laws require a formal privacy role\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EChief Privacy Officer\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELeads broader privacy strategy, policy and risk management across the organization\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EGovernance analyst\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ESupports policy documentation, catalog maintenance, reporting, issue tracking and governance metrics\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EGovernance council\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EResolves cross-domain disputes, approves standards and prioritizes governance work\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EThe exact role stack varies by organization, but what is consistent is that governance needs both business authority and technical custody. Large enterprises often formalize this through a governance council, documented escalation paths and domain-level stewardship.\u003C/p\u003E\r\n\u003Cp\u003EThe practical details matter. If two teams disagree on a metric definition, the program should define who decides. If a regulated field needs a new masking policy, the steward should know which security or privacy partner to involve. If a data quality issue affects a downstream report, lineage should show the impact and stewardship should determine who owns the fix.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-process-and-strategy":{"id":"title-v2-93b97b1c29","additionalClasses":"anchor-title anchor-title--data-governance-process-and-strategy","type":"heading2","lines":["Data governance process and strategy"],":type":"snowflake-site/components/title-v2"},"text_data-governance-process-and-strategy_0":{"id":"text-b05d467da6","text":"\u003Cp\u003EA practical rollout of a \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance-strategy/\"\u003Edata governance strategy\u003C/a\u003E typically follows this sequence:\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003EChoose a priority domain:\u003C/b\u003E Start with customer 360, financial reporting, regulated data, supply chain analytics or AI training data — wherever the business risk or compliance pressure is highest.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EInventory critical data assets:\u003C/b\u003E Identify the tables, views, files, metrics and reports that matter most in that domain.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EClassify sensitive and regulated data:\u003C/b\u003E Tag PII, PHI, payment data, confidential records and other controlled data types.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAssign owners and stewards:\u003C/b\u003E Name who is accountable for definitions, access decisions, quality expectations and issue resolution.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDefine policies:\u003C/b\u003E Establish rules for access, masking, retention, sharing, AI use and exceptions.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECapture lineage and quality signals:\u003C/b\u003E Trace critical data flows and monitor freshness, completeness and validity.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EReview access and usage:\u003C/b\u003E Use audit records to validate who accessed sensitive data and whether policies worked as intended.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EExpand domain by domain:\u003C/b\u003E Reuse standards, templates and lessons learned as the program grows.\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Cp\u003EUseful success metrics include catalog adoption, percentage of critical data assets with owners, classification coverage, policy coverage, quality issue resolution time, access review completion and audit finding reduction.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/best-practices/\"\u003ELearn more about data governance best practices →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-and-regulatory-compliance":{"id":"title-v2-0b662c9ae3","additionalClasses":"anchor-title anchor-title--data-governance-and-regulatory-compliance","type":"heading2","lines":["Data governance and regulatory compliance"],":type":"snowflake-site/components/title-v2"},"text_data-governance-and-regulatory-compliance_0":{"id":"text-fbca23daa7","text":"\u003Cp\u003ERegulators may ask whether an organization can show what regulated data it holds, who accessed it, how it was protected, how long it was retained and whether required controls were applied. Governance supports \u003Ca href=\"https://www.snowflake.com/en/fundamentals/governance-risk-compliance/\"\u003EGRC\u003C/a\u003E by connecting data assets to policies, owners, controls and audit trails. \u003C/p\u003E\r\n\u003Cp\u003EHere are some examples:\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EGovernance obligation\u003C/th\u003E\r\n\u003Cth\u003EExample regulations or standards\u003C/th\u003E\r\n\u003Cth\u003EWhat governance helps prove\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EProtect personal and sensitive data\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EGDPR, CCPA/CPRA, LGPD, PDPA, HIPAA\u003C/td\u003E\r\n\u003Ctd\u003EWhat personal data exists, where it lives, who can access it and how rights requests are handled\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMaintain reporting integrity\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ESOX, BCBS 239, Basel III\u003C/td\u003E\r\n\u003Ctd\u003EHow financial or risk data is defined, transformed, controlled and reconciled\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EProtect payment data\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EPCI DSS\u003C/td\u003E\r\n\u003Ctd\u003EWhere cardholder data appears and which controls apply\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EManage operational resilience\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDORA, NIS2\u003C/td\u003E\r\n\u003Ctd\u003EHow critical systems, third parties and information and communication technology (ICT) risks are monitored\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EGovern AI-related data use\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EEU AI Act and emerging AI laws\u003C/td\u003E\r\n\u003Ctd\u003EWhat data is used in AI systems, whether it is appropriate and how high-risk use is controlled\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EThe \u003Ca rel=\"noopener noreferrer\" href=\"https://artificialintelligenceact.eu/\" target=\"_blank\"\u003EEU AI Act\u003C/a\u003E is a useful example of why governance timelines matter. The regulation applies progressively, with general provisions and prohibitions applying from Feb. 2, 2025, rules for general-purpose AI applying from Aug. 2, 2025, and a broader rollout continuing through Aug. 2, 2027. For organizations using governed enterprise data in AI systems, that creates a practical need to understand training-data provenance, sensitive-data classification, access permissions and auditability.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EWatch to learn how Snowflake’s capabilities —such as sensitive data monitoring — make it easy to detect and gain a comprehensive view of your sensitive data in just a few clicks.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_data-governance-and-regulatory-compliance_0":{"id":"embed-ff3b580535","youtubeVideoId":"HeZUJ1aBuz0","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_why-run-data-governance-on-snowflake":{"id":"title-v2-6fc5ca78cc","additionalClasses":"anchor-title anchor-title--why-run-data-governance-on-snowflake","type":"heading2","lines":["Why run data governance on Snowflake?"],":type":"snowflake-site/components/title-v2"},"text_why_run_data_go":{"id":"text-1df3810d57","text":"\u003Cp\u003EData governance is easier to sustain when policies, metadata, lineage, quality monitoring and access controls sit close to where data is stored, processed, shared and used. Snowflake's governance capabilities are built into the same environment where organizations manage data, applications and AI workloads — rather than applied through a separate tool that has to stay in sync.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EUnified catalog with built-in lineage:\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E provides catalog, column-level lineage, active metadata and policy enforcement in a single surface. Snowflake Horizon Catalog can reduce the need for a separate catalog tool because governance context lives close to the data.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ECompliance-aware by design:\u003C/b\u003E \u003Ca href=\"https://trust.snowflake.com/\"\u003ESnowflake's Compliance Center\u003C/a\u003E provides security posture monitoring alongside attestations supporting standards such as \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.hhs.gov/hipaa/index.html\"\u003EHIPAA\u003C/a\u003E, PCI DSS, SOC 2 Type II, ISO 27001, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.fedramp.gov/\"\u003EFedRAMP\u003C/a\u003E Moderate and IRAP. Certain capabilities and customer configurations may be required depending on implementation.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPolicy-as-code for sensitive data:\u003C/b\u003E Dynamic Data Masking, row access policies, tag-based masking and External Tokenization apply protection controls at the data layer. With proper configuration, classification can help policies apply consistently across queries, applications, sharing and AI workflows.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAuditable usage:\u003C/b\u003E Access History and Query History can help capture detailed access and transformation records that support audit and regulatory review. When properly configured, organizations can more readily identify who accessed a sensitive column and when.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EBuilt with governance controls for AI workflows:\u003C/b\u003E Cortex Guard applies policy controls to LLM inputs and outputs to help reduce the risk of sensitive data entering inappropriate model contexts. Data metric functions can be used to monitor training-data quality over time, so the data feeding AI systems meets the same standards as the data feeding reports.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EGoverned sharing without copies:\u003C/b\u003E Secure Data Sharing, listings and Data Clean Rooms let organizations share insights with partners and external collaborators without moving raw data across security boundaries. Governance controls can remain easier to apply consistently because the data does not leave the platform.\u003C/p\u003E\r\n\u003Cp\u003ETogether, these capabilities provide one governance surface across warehouses, data lakes, open table formats including Iceberg, applications and AI — so controls don't have to be rebuilt every time data moves into a new environment.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_build-governance-as-an-operating-discipline":{"id":"title-v2-cf458b9576","additionalClasses":"anchor-title anchor-title--build-governance-as-an-operating-discipline","type":"heading2","lines":["Build governance as an operating discipline"],":type":"snowflake-site/components/title-v2"},"text_build-governance-as-an-operating-discipline_0":{"id":"text-9cd4bee0a3","text":"\u003Cp\u003EGovernance maturity is not a binary state. Most programs have coverage in some domains and gaps in others. The gaps tend to become visible at the worst possible moment. A compliance finding might expose a column that was never classified. Or an AI output might get questioned, but no one can trace which data contributed to it.\u003C/p\u003E\n\u003Cp\u003EThe organizations that avoid those situations are not the ones that finished a \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/implementation/\"\u003Egovernance implementation\u003C/a\u003E. They are the ones that built ownership, lineage, classification and audit controls into how they operate — so that when the question comes, the answer already exists.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/use-cases/\"\u003ELearn about data governance use cases →\u003C/a\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_build-governance-as-an-operating-discipline_0":{"id":"text-2387dcfb5d","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EKEY TAKEAWAY\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EData governance is not a onetime project but an ongoing discipline that makes data trustworthy, usable and auditable at scale. By establishing clear ownership, consistent policies, and visibility into lineage, quality and access, organizations can answer critical questions about their data before issues arise. As data and AI use expand, embedding governance into everyday workflows — not treating it as a separate control layer — is what enables teams to move faster while reducing risk.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout_what-is-data-governance_0","text_overview_0","title_what-is-data-governance","text_what-is-data-governance_0","title_why-data-governance-matters-now","text_why-data-governance-matters-now_0","quote_why-data-governance-matters-now_0","text_why-data-governance-matters-now_1","callout_why-data-governance-matters-now_0","title_data-governance-for-ai","text_data-governance-for-ai_0","yt_data-governance-for-ai_0","title_data-governance-principles","text_data-governance-principles_0","title_data-governance-frameworks-and-standards","text_data-governance-frameworks-and-standards_0","title_data-governance-operating-models","text_data-governance-operating-models_0","card_v2_data-governance-operating-models_0","title_the-core-components-of-data-governance","text_the-core-components-of-data-governance_0","card_v2_the-core-components-of-data-governance_0","title_data-stewardship-and-governance-roles","text_data-stewardship-and-governance-roles_0","title_data-governance-process-and-strategy","text_data-governance-process-and-strategy_0","title_data-governance-and-regulatory-compliance","text_data-governance-and-regulatory-compliance_0","yt_data-governance-and-regulatory-compliance_0","title_why-run-data-governance-on-snowflake","text_why_run_data_go","title_build-governance-as-an-operating-discipline","text_build-governance-as-an-operating-discipline_0","callout_build-governance-as-an-operating-discipline_0"]},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":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-fb31743745",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-3f6f07eaf9","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-619a1a13f7","additionalClasses":"page-toc","text":"\u003Cul\u003E\r\n\u003Cli data-anchor=\"what-is-data-governance\"\u003EWhat is data governance?\u003C/li\u003E\r\n\u003Cli data-anchor=\"why-data-governance-matters-now\"\u003EWhy data governance matters now\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-governance-for-ai\"\u003EData governance for AI\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-governance-principles\"\u003EData governance principles\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-governance-frameworks-and-standards\"\u003EData governance frameworks and standards\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-governance-operating-models\"\u003EData governance operating models\u003C/li\u003E\r\n\u003Cli data-anchor=\"the-core-components-of-data-governance\"\u003EThe core components of data governance\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-stewardship-and-governance-roles\"\u003EData stewardship and governance roles\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-governance-process-and-strategy\"\u003EData governance process and strategy\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-governance-and-regulatory-compliance\"\u003EData governance and regulatory compliance\u003C/li\u003E\r\n\u003Cli data-anchor=\"why-run-data-governance-on-snowflake\"\u003EWhy run data governance on Snowflake?\u003C/li\u003E\r\n\u003Cli data-anchor=\"build-governance-as-an-operating-discipline\"\u003EBuild governance as an operating discipline\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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governance\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy_copy","text_copy_copy"]},"isActiveTOC":false,"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1337071273":{"id":"flexible-column-container-c84f1cee4b","propertiesId":"hub-explore-topics-grid","type":"3-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-4087ea1e1e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-3db21aa767","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003ECompare 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