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Data Governance Frameworks:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-d8660853ad","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/cobit/\"\u003ECOBIT\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/dama-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/fair-principles/\"\u003EFAIR Principles\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/togaf/\"\u003ETOGAF\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_549182334":{"id":"flexible-column-container-566284b33e","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text":{"id":"text-701c69e665","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDATA GOVERNANCE FRAMEWORKS DEFINED\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EData governance frameworks provide the structure organizations use to manage, protect and govern data consistently across the enterprise. They define the roles, policies, processes, controls and metrics that guide how data decisions are made, enforced and measured at scale.\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-2b9300e664","text":"\u003Cp\u003EMost governance programs have more documentation than accountability. Policies are published, a catalog is populated, a council is formed — but data still moves through the organization in ways that leave ownership unclear, policy enforcement inconsistent and audit evidence scattered across teams and tools.\u003C/p\u003E\r\n\u003Cp\u003EA data governance framework helps organizations address the operating problem. It defines who makes decisions about data, how those decisions become enforceable controls and how governance scales across domains, platforms and AI workloads instead of teams relying on informal coordination. The framework is the structure that connects policy intent to what actually happens in practice.\u003C/p\u003E\r\n\u003Cp\u003EThis guide covers the role of data governance frameworks, the core components most programs need, how major frameworks differ and how organizations can choose and implement a framework that fits their architecture, industry and maturity level.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-data-governance-framework":{"id":"title-v2-d3b0fa025f","additionalClasses":"anchor-title anchor-title--what-is-a-data-governance-framework","type":"heading2","lines":["What is a data governance framework?"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-data-governance-framework_0":{"id":"text-8ae8145fab","text":"\u003Cp\u003EA data governance framework is a structured model for defining how an organization manages, protects, uses and measures data across its environment. It typically includes the governance principles, roles, policies, standards, processes, controls, technologies and metrics that determine how data is handled from creation through retention or deletion.\u003C/p\u003E\r\n\u003Cp\u003EA framework is not the same thing as a \u003Ca href=\"https://snowflake.com/en/data-governance/policy/\"\u003Epolicy\u003C/a\u003E, standard or control. A framework provides the overarching structure for how an organization governs data. Policies, internal standards, procedures, and controls typically sit within the framework and make it operational. Additional, formal external standards, such as ISO standards, may also inform or even serve as the framework.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EA policy\u003C/b\u003E states what the organization expects, such as who may access regulated customer data.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAn internal standard\u003C/b\u003E defines how that expectation should be applied, such as classification rules for customer identifiers, transaction records or protected health information.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EA control\u003C/b\u003E enforces or verifies the requirement, such as \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rbac/\"\u003Erole-based access control\u003C/a\u003E (RBAC), dynamic masking, encryption, access reviews or audit logging.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EA formal external standard\u003C/b\u003E provides recognized guidance or requirements that help shape a framework or even serve as a framework — such as ISO standards for security, \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-privacy/\"\u003Eprivacy\u003C/a\u003E or data management.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThis distinction is important because many governance programs fail due to lack of operating structure.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-framework-purpose-and-value":{"id":"title-v2-f2e79d4f21","additionalClasses":"anchor-title anchor-title--data-governance-framework-purpose-and-value","type":"heading2","lines":["Data governance framework purpose and value"],":type":"snowflake-site/components/title-v2"},"text_data-governance-framework-purpose-and-value_0":{"id":"text-ff4fc4f013","text":"\u003Cp\u003EData governance frameworks help organizations make governance decisions consistently across teams, systems and data domains. Without a framework, the customer data team might manage definitions one way, finance might use a different review process, and the AI team might track training data provenance in a spreadsheet that no one outside the project can audit.\u003C/p\u003E\r\n\u003Cp\u003EThe value of a framework is that it turns governance from a collection of local practices into an enterprise operating model. It helps organizations:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EAssign accountability for data domains, tables, metrics, policies and exceptions\u003C/li\u003E\r\n\u003Cli\u003EDefine common language for quality, ownership, classification, \u003Ca href=\"https://snowflake.com/en/data-governance/data-lineage/\"\u003Elineage\u003C/a\u003E and risk\u003C/li\u003E\r\n\u003Cli\u003EAlign governance work with compliance, analytics, AI, security and operational goals\u003C/li\u003E\r\n\u003Cli\u003ESupport more consistent application of controls across data products, pipelines, applications and models\u003C/li\u003E\r\n\u003Cli\u003EDocument access decisions, policy exceptions and remediation activity for audit purposes\u003C/li\u003E\r\n\u003Cli\u003EMeasure whether governance is improving, not just whether governance artifacts exist\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EA framework also helps governance teams avoid two common extremes: a policy-heavy program that produces documentation but little enforcement, and a tool-heavy program that catalogs assets without clarifying who makes decisions. The most useful frameworks connect the operating model to the technical environment, so a steward's approval, a classification tag, a lineage path and an access policy can work together.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_data-governance-framework-purpose-and-value_0":{"id":"quote-item-db46b03967","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"“The true driver of AI and data governance is already in place — customers require it.”","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_core-components-of-a-data-governance-framework":{"id":"title-v2-4da9470029","additionalClasses":"anchor-title anchor-title--core-components-of-a-data-governance-framework","type":"heading2","lines":["Core components of a data governance framework"],":type":"snowflake-site/components/title-v2"},"text_core-components-of-a-data-governance-framework_0":{"id":"text-ba6b6cfed7","text":"\u003Cp\u003EMost data governance frameworks use different language, but they tend to cover a common set of components. The exact model depends on the organization, but a practical framework should usually define the following areas:\u003C/p\u003E\r\n\u003Ch3\u003EGovernance strategy and principles\u003C/h3\u003E\r\n\u003Cp\u003EThe framework should explain why \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Edata governance\u003C/a\u003E exists and which business outcomes it supports. That might include regulatory compliance, trusted analytics, AI readiness, data product adoption, operational resilience or secure data sharing. Principles give teams a way to make decisions when a policy does not cover every scenario, such as whether a sensitive attribute should be masked, tokenized, excluded from a model feature set or made available only through an approved view.\u003C/p\u003E\r\n\u003Ch3\u003ERoles and responsibilities\u003C/h3\u003E\r\n\u003Cp\u003EGovernance depends on clear accountability. A framework should define the responsibilities of data owners, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-stewardship/\"\u003Edata stewards\u003C/a\u003E, data custodians, governance council members, security teams, privacy teams, platform teams and business stakeholders. It should also define decision rights, such as who approves a new data domain, who resolves a metric definition conflict and who can grant an exception to a retention or access rule.\u003C/p\u003E\r\n\u003Ch3\u003EPolicies, standards and procedures\u003C/h3\u003E\r\n\u003Cp\u003EPolicies set the rules for how data is created, classified, accessed, used, shared, retained and disposed of. Standards make those rules concrete by defining approved classification levels, naming conventions, quality thresholds, metadata requirements, retention categories and access models. Procedures describe how teams perform the work, such as onboarding a new data product, reviewing access requests or resolving a quality issue.\u003C/p\u003E\r\n\u003Ch3\u003EData quality management\u003C/h3\u003E\r\n\u003Cp\u003EThe framework should define how quality is measured, monitored and remediated. Common dimensions include accuracy, completeness, consistency, timeliness, validity and uniqueness. In practice, this means identifying critical data elements, assigning quality rules, monitoring failures and defining who investigates when a revenue metric changes because a source field stopped updating.\u003C/p\u003E\r\n\u003Ch3\u003EData classification and metadata management\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-classification/\"\u003EClassification\u003C/a\u003E and metadata provide context intended to support appropriate and compliant data use. A framework should define how the organization captures sensitivity, business meaning, ownership, lineage, freshness, usage and policy context. A table name alone rarely tells a user whether a column contains customer identifiers, whether the data is approved for AI training or whether the metric definition has changed since the last reporting cycle.\u003C/p\u003E\r\n\u003Ch3\u003EData privacy, security and access controls\u003C/h3\u003E\r\n\u003Cp\u003EGovernance frameworks should connect policy intent to security controls. That includes identity and access management, least-privilege access, encryption, masking, row-level security, monitoring, retention controls and compliance with privacy requirements. The framework should also define how access is requested, approved, reviewed and revoked.\u003C/p\u003E\r\n\u003Ch3\u003EData lifecycle management\u003C/h3\u003E\r\n\u003Cp\u003EData has a lifecycle: creation, ingestion, transformation, storage, use, sharing, retention, archiving and deletion. A governance framework should define how data moves through those stages, which retention rules apply, how legal holds are handled and what evidence proves that data was retained or disposed of according to policy.\u003C/p\u003E\r\n\u003Ch3\u003EPrimary and reference data management\u003C/h3\u003E\r\n\u003Cp\u003EGovernance programs need consistent definitions for key entities such as customers, products, employees, vendors, locations and financial accounts. Primary and reference data management define the trusted sources, survivorship rules and stewardship processes that keep those entities consistent across systems and reporting use cases.\u003C/p\u003E\r\n\u003Ch3\u003EData architecture and integration standards\u003C/h3\u003E\r\n\u003Cp\u003EA framework should align with the organization's architecture, not sit beside it. That includes standards for modeling, ingestion, transformation, interoperability, API use, data sharing, semantic layers and data product design. Architecture standards help governance scale because teams can apply consistent patterns rather than designing every pipeline or domain boundary from scratch.\u003C/p\u003E\r\n\u003Ch3\u003ECompliance, risk and audit management\u003C/h3\u003E\r\n\u003Cp\u003EGovernance frameworks should define how regulatory obligations, internal controls, risks, exceptions and audit evidence are tracked. This is especially important in industries where regulators expect organizations to show not only that controls exist, but also that they are operating as intended.\u003C/p\u003E\r\n\u003Ch3\u003ETools and technology\u003C/h3\u003E\r\n\u003Cp\u003ETechnology does not create governance on its own, but it helps governance operate at scale. \u003Ca href=\"https://snowflake.com/en/data-governance/data-catalog/\"\u003EData catalogs\u003C/a\u003E, lineage tools, access governance systems, data quality platforms, policy engines and monitoring tools can capture metadata, enforce controls, surface issues and preserve evidence. The framework should clarify which systems hold authoritative metadata and how governance decisions are written back into technical controls.\u003C/p\u003E\r\n\u003Ch3\u003EMetrics and continuous improvement\u003C/h3\u003E\r\n\u003Cp\u003EA framework should include measures that show whether governance is working. Useful metrics might include catalog coverage, classification coverage, data quality scores, access review completion, exception aging, issue resolution time, policy compliance rates and the percentage of critical data elements with assigned owners.\u003C/p\u003E\r\n\u003Cp\u003EA simple way to group these components is people, policies, processes, data controls, technology and measurement.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_examples-of-data-governance-frameworks":{"id":"title-v2-ca84c84a94","additionalClasses":"anchor-title anchor-title--examples-of-data-governance-frameworks","type":"heading2","lines":["Examples of data governance frameworks"],":type":"snowflake-site/components/title-v2"},"text_examples-of-data-governance-frameworks_0":{"id":"text-148872499d","text":"\u003Cp\u003EDifferent frameworks emphasize different problems. Some are broad bodies of knowledge, some are maturity models, some are architecture methods and some are designed for cloud, research or regulatory contexts.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EFramework\u003C/th\u003E\r\n\u003Cth\u003EPrimary focus\u003C/th\u003E\r\n\u003Cth\u003EBest fit\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://dama.org/learning-resources/dama-data-management-body-of-knowledge-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EComprehensive data management body of knowledge\u003C/td\u003E\r\n\u003Ctd\u003ELarge organizations that need broad coverage across data management disciplines\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.isaca.org/resources/cobit\"\u003ECOBIT\u003C/a\u003E\u003Cp\u003E \u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003EGovernance and management of enterprise IT\u003C/td\u003E\r\n\u003Ctd\u003EOrganizations that want to connect data governance to IT governance, controls, risk and audit\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://edmcouncil.org/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EData management capability maturity\u003C/td\u003E\r\n\u003Ctd\u003EFinancial services and other regulated organizations that need maturity assessment and capability improvement\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.opengroup.org/togaf\"\u003ETOGAF\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EEnterprise architecture alignment\u003C/td\u003E\r\n\u003Ctd\u003EOrganizations embedding data governance into architecture, transformation and operating model work\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://www.go-fair.org/fair-principles/\"\u003EFAIR\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EFindable, accessible, interoperable and reusable data\u003C/td\u003E\r\n\u003Ctd\u003EResearch, scientific, public sector and data sharing environments\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca href=\"https://edmcouncil.org/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003ECloud data management capabilities\u003C/td\u003E\r\n\u003Ctd\u003ECloud-first, hybrid cloud and multi-cloud organizations that need controls for cloud data management\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Ch3\u003EDAMA-DMBOK\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/dama-dmbok/\"\u003EDAMA-DMBOK\u003C/a\u003E is a broad data management body of knowledge that helps organizations structure data management disciplines and align them with business strategy, compliance and technology change. DAMA describes the DMBOK as a resource for structuring, governing and optimizing data assets across areas such as strategy, governance, quality, metadata and architecture.\u003C/p\u003E\r\n\u003Cp\u003EDAMA-DMBOK is often useful when an organization needs a comprehensive reference model rather than a narrow control framework. It can help teams define the scope of a data management program, identify gaps and create a common vocabulary across governance, architecture, quality and stewardship teams.\u003C/p\u003E\r\n\u003Ch3\u003ECOBIT\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/cobit/\"\u003ECOBIT\u003C/a\u003E, from ISACA, is a governance framework for enterprise information and technology. It’s often used by organizations that need to connect technology governance, risk, control objectives and audit practices. ISACA’s broader focus on IT governance, audit, risk, privacy and security makes COBIT relevant when data governance needs to align closely with enterprise IT oversight.\u003C/p\u003E\r\n\u003Cp\u003ECOBIT can be useful when data governance must fit into an established control environment. For example, a financial services organization might use COBIT-aligned governance processes to connect data access reviews, change management, control testing and audit evidence.\u003C/p\u003E\r\n\u003Ch3\u003EDCAM\u003C/h3\u003E\r\n\u003Cp\u003EThe Data Management Capability Assessment Model (\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/dcam/\"\u003EDCAM\u003C/a\u003E), developed by the EDM Council, is a best practice framework for assessing and improving data management capabilities. EDM Council describes DCAM as a framework for establishing, sustaining and improving a mature data management discipline, with expanded support for AI and cloud in the current version.\u003C/p\u003E\r\n\u003Cp\u003EDCAM is especially relevant for financial services and other regulated industries because it emphasizes maturity, capability assessment and evidence-based improvement. It helps organizations identify where governance capabilities are strong, where they are inconsistent and which gaps should be prioritized.\u003C/p\u003E\r\n\u003Ch3\u003ETOGAF\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/togaf/\"\u003ETOGAF\u003C/a\u003E, maintained by The Open Group, is an enterprise architecture methodology and framework. The Open Group describes TOGAF as a detailed method and set of supporting tools for developing enterprise architecture, with a standard used by many large organizations.\u003C/p\u003E\r\n\u003Cp\u003ETOGAF is not a data governance framework in the narrow sense, but it’s valuable when governance must be embedded into architecture. It can help align data governance with business architecture, application architecture, data architecture and technology architecture so governance decisions are reflected in how systems are designed and changed.\u003C/p\u003E\r\n\u003Ch3\u003EFAIR principles\u003C/h3\u003E\r\n\u003Cp\u003EThe \u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/fair-principles/\"\u003EFAIR principles\u003C/a\u003E define a model for making data findable, accessible, interoperable and reusable. The principles were published in 2016 to improve the management and stewardship of digital assets, especially in scientific and research contexts.\u003C/p\u003E\r\n\u003Cp\u003EFAIR is particularly useful when the goal is responsible data sharing and reuse. A research institution, healthcare organization or public sector agency might use FAIR to improve metadata, persistent identifiers, interoperability standards and reuse conditions for data products.\u003C/p\u003E\r\n\u003Ch3\u003ECDMC\u003C/h3\u003E\r\n\u003Cp\u003ECloud Data Management Capabilities (\u003Ca href=\"https://www.snowflake.com/en/data-governance/frameworks/cdmc/\"\u003ECDMC\u003C/a\u003E), developed by the EDM Council, focuses on managing and controlling data in cloud environments. EDM Council describes CDMC as a framework for managing data securely in cloud and multi-cloud environments, and its model includes auditable evidence, scoring and cloud-specific control capabilities.\u003C/p\u003E\r\n\u003Cp\u003ECDMC is useful when organizations are moving governed data workloads into cloud, hybrid cloud or multi-cloud architectures. It gives teams a way to evaluate controls for data governance and accountability, cataloging and classification, data accessibility, protection, privacy, lifecycle management and technical architecture.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-evaluate-and-choose-a-data-governance-framework":{"id":"title-v2-1d832c408d","additionalClasses":"anchor-title anchor-title--how-to-evaluate-and-choose-a-data-governance-framework","type":"heading2","lines":["How to evaluate and choose a data governance framework"],":type":"snowflake-site/components/title-v2"},"text_how-to-evaluate-and-choose-a-data-governance-framework_0":{"id":"text-305ad9da7e","text":"\u003Cp\u003EChoosing a framework or frameworks starts with the organization’s operating problem. Use the following criteria to evaluate fit:\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EEvaluation criterion\u003C/th\u003E\r\n\u003Cth\u003EQuestions to ask\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EBusiness goal\u003C/td\u003E\r\n\u003Ctd\u003EIs the primary need compliance, analytics trust, AI readiness, cloud migration, data sharing or enterprise architecture alignment?\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EIndustry context\u003C/td\u003E\r\n\u003Ctd\u003EDoes the framework support sector-specific expectations for financial services, healthcare, government, research or public sector data?\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EGovernance maturity\u003C/td\u003E\r\n\u003Ctd\u003EDoes the organization need foundational roles and policies, or does it need maturity measurement, control testing and optimization?\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EArchitecture fit\u003C/td\u003E\r\n\u003Ctd\u003EDoes the framework reflect the organization’s data estate, including cloud, hybrid cloud, multi-cloud, open table formats, applications and AI workloads?\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EControl requirements\u003C/td\u003E\r\n\u003Ctd\u003EDoes the framework translate into enforceable controls such as access policies, masking, classification, lineage and retention?\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EEvidence needs\u003C/td\u003E\r\n\u003Ctd\u003EDoes the organization need auditable proof of approvals, exceptions, quality checks, access reviews and policy enforcement?\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EImplementation effort\u003C/td\u003E\r\n\u003Ctd\u003ECan the organization operationalize the framework with the people, technology and executive sponsorship it has available?\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ETooling alignment\u003C/td\u003E\r\n\u003Ctd\u003ECan existing catalogs, lineage systems, policy engines, data quality tools and platform controls support the framework?\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EMany organizations blend frameworks. For example, an enterprise might use DAMA-DMBOK for broad data management scope, DCAM for maturity assessment, CDMC for cloud controls and NIST AI RMF for AI risk governance.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-to-evaluate-and-choose-a-data-governance-framework_0":{"id":"card-v2-4f1406ca56","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","image":{"id":"image","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--83f793a0-df9c-45fe-8949-f4e33942489a/gilbert%25403x.png?preferwebp=true&quality=85","alt":"Town of Gilbert Logo","width":"624","height":"351",":type":"snowflake-site/components/image"},"type":"content-card","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"},":type":"snowflake-site/components/card-v2"},"title_the-6-phases-of-data-governance-framework-implementation":{"id":"title-v2-19ef801ccc","additionalClasses":"anchor-title anchor-title--the-6-phases-of-data-governance-framework-implementation","type":"heading2","lines":["The 6 phases of data governance framework implementation"],":type":"snowflake-site/components/title-v2"},"text_the-6-phases-of-data-governance-framework-implementation_0":{"id":"text-51a2e676a3","text":"\u003Cp\u003EA data governance framework is most useful when it changes how data is managed day to day. Implementation should start with a focused scope, prove the operating model and then expand.\u003C/p\u003E\n\u003Ch3\u003EPhase 1: Establish governance leadership\u003C/h3\u003E\n\u003Cp\u003EStart by securing executive sponsorship and defining the decision-making structure. This usually includes a governance council, domain-level data owners, data stewards, platform owners, security and privacy stakeholders, and business representatives.\u003C/p\u003E\n\u003Cp\u003EA RACI matrix can help document who is responsible, accountable, consulted and informed for each governance process. For example, a data owner may be accountable for a customer data domain, a steward may be responsible for metadata quality, the security team may be consulted on access policy design and downstream analytics teams may be informed when a metric definition changes.\u003C/p\u003E\n\u003Ch3\u003EPhase 2: Conduct data inventory and classification\u003C/h3\u003E\n\u003Cp\u003EThe next step is to identify the data that matters most. Inventory critical tables, views, data products, pipelines, reports, AI training data sets and externally shared assets. Then classify data based on sensitivity, business meaning, regulatory relevance, ownership, usage and lifecycle requirements.\u003C/p\u003E\n\u003Cp\u003EThis phase should prioritize high-value and high-risk data first. Customer identifiers, financial reporting data, regulated healthcare data, model training data and board-level metrics usually require more urgent governance than low-risk operational logs.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-6-phases-of-data-governance-framework-implementation_0":{"id":"text-292ac6d14b","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EMany teams complete a data inventory and classification exercise, then leave the results sitting in a spreadsheet. This creates visibility, but not accountability. If a column is tagged as sensitive, regulated or approved for limited use, that classification should trigger specific controls, such as masking, row-level access, approval routing, retention rules or audit logging.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_the-6-phases-of-data-governance-framework-implementation_1":{"id":"text-16ade478e1","text":"\u003Ch3\u003EPhase 3: Define policies, standards and procedures\u003C/h3\u003E\r\n\u003Cp\u003EOnce the organization knows which data it’s governing, it can define the rules. Policies should cover access, classification, quality, retention, sharing, acceptable use, AI use and exception handling. Standards should translate those policies into specific requirements, such as mandatory metadata fields, approved classification levels, naming conventions, data quality thresholds and retention categories.\u003C/p\u003E\r\n\u003Cp\u003EProcedures should describe how teams perform governance work. For example, a procedure might define how a new data product is approved, which metadata fields are required before publication and how an access request is routed for review.\u003C/p\u003E\r\n\u003Ch3\u003EPhase 4: Implement governance controls and technical enforcement\u003C/h3\u003E\r\n\u003Cp\u003EFramework implementation needs technical enforcement. This is where classification tags, access policies, masking policies, row-level controls, encryption, lineage, data quality monitoring and audit logging become part of the governed environment.\u003C/p\u003E\r\n\u003Cp\u003EIn Snowflake, for example, governance capabilities are supported through \u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E, which provides built-in governance, discovery and security capabilities for the AI Data Cloud. Horizon is designed to connect compute engines and formats, provide consistent metadata and permissions views, and extend governance metadata into supported data-sharing workflows.\u003C/p\u003E\r\n\u003Cp\u003EThe practical goal is to reduce the distance between governance intent and platform behavior. If a column is tagged as sensitive, organizations may configure access and masking controls to align with that classification rather than relying solely on manual policy enforcement.\u003C/p\u003E\r\n\u003Ch3\u003EPhase 5: Pilot and validate governance controls\u003C/h3\u003E\r\n\u003Cp\u003EPilot the framework in one or two high-value domains before scaling it across the enterprise. A pilot might focus on customer data, financial reporting, regulated healthcare data or an AI training data pipeline.\u003C/p\u003E\r\n\u003Cp\u003EThe pilot should test whether roles are clear, metadata is complete, policies are enforceable, quality issues are routed correctly and audit evidence can be produced. It should also reveal where the framework is too complex, where stewardship work is under-resourced and where technical controls need adjustment.\u003C/p\u003E\r\n\u003Ch3\u003EPhase 6: Scale enterprise-wide and optimize continuously\u003C/h3\u003E\r\n\u003Cp\u003EAfter the pilot, expand the framework across additional domains, data products and business units. Scaling should include training, onboarding playbooks, automation, recurring metrics and feedback loops.\u003C/p\u003E\r\n\u003Cp\u003EContinuous improvement matters because data environments change. New applications, new AI use cases, new regulations, new cloud services and new business definitions can all create governance gaps. A mature framework gives teams a way to detect those gaps and adjust roles, policies, controls or metrics before trust erodes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_implementation-timeline":{"id":"title-v2-e3e1eb17e8","additionalClasses":"anchor-title anchor-title--implementation-timeline","type":"heading2","lines":["Implementation timeline"],":type":"snowflake-site/components/title-v2"},"text_implementation-timeline_0":{"id":"text-40851a2118","text":"\u003Cp\u003EImplementation timelines vary by organization size, regulatory burden, data complexity and maturity. A practical plan often looks like this:\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EPhase\u003C/th\u003E\r\n\u003Cth\u003ETypical duration\u003C/th\u003E\r\n\u003Cth\u003EPrimary output\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EEstablish governance leadership\u003C/td\u003E\r\n\u003Ctd\u003E4–6 weeks\u003C/td\u003E\r\n\u003Ctd\u003ESponsorship, governance council, role definitions and RACI matrix\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EConduct data inventory and classification\u003C/td\u003E\r\n\u003Ctd\u003E6–8 weeks\u003C/td\u003E\r\n\u003Ctd\u003ECritical data inventory, classification model and priority domains\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EDefine policies, standards and procedures\u003C/td\u003E\r\n\u003Ctd\u003E4–6 weeks\u003C/td\u003E\r\n\u003Ctd\u003EGovernance policies, standards and operating procedures\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EImplement governance controls\u003C/td\u003E\r\n\u003Ctd\u003E8–12 weeks\u003C/td\u003E\r\n\u003Ctd\u003EAccess rules, tags, masking, lineage, quality checks and audit logging\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EPilot and validate controls\u003C/td\u003E\r\n\u003Ctd\u003E6–8 weeks\u003C/td\u003E\r\n\u003Ctd\u003EPilot results, control validation and remediation plan\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EScale and optimize\u003C/td\u003E\r\n\u003Ctd\u003EOngoing\u003C/td\u003E\r\n\u003Ctd\u003EEnterprise rollout, metrics and continuous improvement cadence\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003E\u003Cspan style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003EThe first 90 days should focus on visible progress rather than enterprise completeness. Useful early wins include assigning owners for critical data domains, classifying sensitive columns, publishing a small set of approved metric definitions, implementing access reviews for high-risk data and creating a governance dashboard that shows coverage and open issues.\u003C/span\u003E\u003C/p\u003E\r\n\u003Cp\u003EEnterprise-scale maturity usually takes longer because governance requires operating change as much as tooling. The program has to become part of how teams design data products, approve access, build pipelines, share data and deploy AI systems.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-governance-frameworks-and-data-governance":{"id":"title-v2-4f47bd0722","additionalClasses":"anchor-title anchor-title--ai-governance-frameworks-and-data-governance","type":"heading2","lines":["AI governance frameworks and data governance"],":type":"snowflake-site/components/title-v2"},"text_ai-governance-frameworks-and-data-governance_0":{"id":"text-8c1869ec0d","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/\"\u003EAI governance\u003C/a\u003E depends on data governance because model behavior is shaped by the data used to train, tune, retrieve, evaluate and monitor the system. A model registry can track versions and evaluation results, but it cannot answer whether the training data was approved for the use case, whether sensitive columns were excluded, whether lineage is complete or whether a feature reflects a biased historical process.\u003C/p\u003E\n\u003Cp\u003ETraditional data governance frameworks are being extended for AI workloads in several ways. DAMA-DMBOK-style programs can expand metadata, quality and stewardship practices to include training data provenance, feature definitions and approved data sets. COBIT-style governance can connect AI controls to risk, audit and accountability. DCAM and CDMC can help teams assess whether cloud data controls and maturity practices are strong enough to support AI workloads.\u003C/p\u003E\n\u003Cp\u003EAI-specific frameworks are also being developed. The \u003Ca href=\"https://www.nist.gov/itl/ai-risk-management-framework\" target=\"_blank\"\u003ENIST AI Risk Management Framework\u003C/a\u003E is designed to help organizations managing AI systems address risks and improve trustworthiness across design, development, use and evaluation. For data governance teams, that means traditional controls need to connect with model governance, ML lineage, training data provenance, explainability, bias monitoring, responsible AI practices and algorithmic accountability.\u003C/p\u003E\n\u003Cp\u003EA data-centric AI governance approach asks concrete questions before a model reaches production:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EWhich data sets trained, tuned or grounded the model?\u003C/li\u003E\n\u003Cli\u003EWho approved those data sets for this use case?\u003C/li\u003E\n\u003Cli\u003EWhich lineage paths connect the source data to features, prompts, embeddings or retrieval indexes?\u003C/li\u003E\n\u003Cli\u003EWhich sensitive attributes or proxies were included, excluded or transformed?\u003C/li\u003E\n\u003Cli\u003EWhich quality rules, drift checks and bias monitoring processes apply?\u003C/li\u003E\n\u003Cli\u003EWhich outputs require human review, disclosure or audit logging?\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EThese questions make AI governance operational. They also show why a data governance framework cannot stop at reports and dashboards; it has to account for data as it moves into models, agents, applications and automated decisions.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_industry-specific-framework-guidance":{"id":"title-v2-0a1a847b43","additionalClasses":"anchor-title anchor-title--industry-specific-framework-guidance","type":"heading2","lines":["Industry-specific framework guidance"],":type":"snowflake-site/components/title-v2"},"text_industry-specific-framework-guidance_0":{"id":"text-8bc2ef3074","text":"\u003Cp\u003EDifferent industries use data governance frameworks for different reasons. The core components may look similar, but the control emphasis changes.\u003C/p\u003E\n\u003Ch3\u003EFinancial services\u003C/h3\u003E\n\u003Cp\u003EFinancial services organizations often need strong evidence around data quality, lineage, ownership, risk reporting and regulatory controls. DCAM is commonly relevant because it emphasizes data management maturity and capability assessment. Financial institutions may also align governance practices with BCBS 239, which focuses on risk data aggregation and risk reporting principles for banks.\u003C/p\u003E\n\u003Cp\u003EIn practice, financial services governance often centers on critical data elements, risk reporting lineage, data quality thresholds, access controls, retention requirements and audit evidence. The framework should make it clear who owns a risk metric, where it comes from, how it’s transformed and whether the data is fit for regulatory reporting.\u003C/p\u003E\n\u003Ch3\u003EHealthcare and life sciences\u003C/h3\u003E\n\u003Cp\u003EHealthcare organizations often need to govern protected health information, claims data, clinical data, research data and operational data under privacy and security obligations. \u003Ca href=\"https://www.hhs.gov/hipaa/index.html\" target=\"_blank\"\u003EHIPAA\u003C/a\u003E shapes privacy and security requirements in the U.S., while FAIR principles are often relevant for research data sharing, interoperability and reuse. The FAIR principles are especially useful when data needs to be discoverable and reusable by humans and machines without losing context.\u003C/p\u003E\n\u003Cp\u003EHealthcare governance frameworks should connect classification, consent, access, deidentification, retention and data sharing rules to the systems where clinical, operational and research data is used.\u003C/p\u003E\n\u003Ch3\u003EGovernment and public sector\u003C/h3\u003E\n\u003Cp\u003EGovernment and public sector organizations often need governance models that support transparency, security, records management, open data and compliance. \u003Ca href=\"https://www.fedramp.gov/\" target=\"_blank\"\u003EFedRAMP\u003C/a\u003E may be relevant when cloud services are used to process government data, while public data programs often require metadata, publication standards and reuse guidance.\u003C/p\u003E\n\u003Cp\u003EIn these environments, the framework should clarify which data can be public, which data requires restricted access, which metadata must accompany published data sets and how agencies preserve evidence for compliance, audits and public accountability.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_framework-maturity-assessment":{"id":"title-v2-e7b1bb583c","additionalClasses":"anchor-title anchor-title--framework-maturity-assessment","type":"heading2","lines":["Framework maturity assessment"],":type":"snowflake-site/components/title-v2"},"text_framework-maturity-assessment_0":{"id":"text-4e18e484e7","text":"\u003Cp\u003EA maturity model helps organizations assess how well governance is operating and where to invest next. A simple five-stage model can make progress visible without turning maturity into a paperwork exercise.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EStage\u003C/th\u003E\r\n\u003Cth\u003EDescription\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EInitial\u003C/td\u003E\r\n\u003Ctd\u003EGovernance is ad hoc. Ownership, definitions, access rules and quality checks vary by team.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EManaged\u003C/td\u003E\r\n\u003Ctd\u003EKey domains have assigned owners, basic policies and some repeatable processes.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EDefined\u003C/td\u003E\r\n\u003Ctd\u003EPolicies, standards, stewardship roles and technical controls are documented and applied across priority domains.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EMeasured\u003C/td\u003E\r\n\u003Ctd\u003EGovernance performance is tracked through quality scores, access metrics, issue resolution and control evidence.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EOptimized\u003C/td\u003E\r\n\u003Ctd\u003EGovernance is embedded into data product design, platform controls, AI workflows and continuous improvement processes.\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003EA quick self-assessment can help teams identify their current stage:\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003EDo critical data elements have named owners and approved business definitions?\u003C/li\u003E\r\n\u003Cli\u003EAre sensitive tables and columns classified with enforceable access and masking rules?\u003C/li\u003E\r\n\u003Cli\u003ECan teams trace lineage from source systems to reports, data products or AI models?\u003C/li\u003E\r\n\u003Cli\u003EAre data quality issues measured, assigned and remediated through a defined workflow?\u003C/li\u003E\r\n\u003Cli\u003ECan the organization produce audit evidence for access decisions, exceptions and policy enforcement?\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Cp\u003EMost organizations begin with uneven maturity across domains. Finance may have strong controls because reporting risk is high, while marketing, product or AI teams may have faster-moving data practices with less formal stewardship. The goal is not uniform maturity everywhere — it’s appropriate maturity based on business value, risk and usage.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bringing-structure-to-governance-decisions":{"id":"title-v2-92cd7496ac","additionalClasses":"anchor-title anchor-title--bringing-structure-to-governance-decisions","type":"heading2","lines":["Bringing structure to governance decisions"],":type":"snowflake-site/components/title-v2"},"text_bringing-structure-to-governance-decisions_0":{"id":"text-8f854d8195","text":"\u003Cp\u003EData governance frameworks are useful because they give organizations a tested structure for decisions that otherwise become inconsistent, manual or difficult to audit. A framework helps clarify who owns a data domain, which policies apply to a table or column, how access should be enforced, where lineage needs to be traced and which metrics show whether governance is actually working.\u003C/p\u003E\n\u003Cp\u003EThe right framework does not replace organizational judgment, however. DAMA-DMBOK, COBIT, DCAM, TOGAF, FAIR and CDMC each emphasize different parts of the governance problem, and many organizations adapt more than one model to fit their industry, architecture and maturity. As data moves into more shared products, cloud environments and AI workloads, governance frameworks give teams a way to keep policy connected to use.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_bringing-structure-to-governance-decisions_0":{"id":"text-00aa6dfed4","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EKEY TAKEAWAY\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EA data governance framework turns governance from a collection of policies into an operational system of accountability. By connecting ownership, controls, metadata, security and audit evidence, frameworks can help organizations apply governance practices more consistently across data platforms, cloud environments and AI workloads — while improving visibility, scalability and operational accountability.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["text","text_overview_0","title_what-is-a-data-governance-framework","text_what-is-a-data-governance-framework_0","title_data-governance-framework-purpose-and-value","text_data-governance-framework-purpose-and-value_0","quote_data-governance-framework-purpose-and-value_0","title_core-components-of-a-data-governance-framework","text_core-components-of-a-data-governance-framework_0","title_examples-of-data-governance-frameworks","text_examples-of-data-governance-frameworks_0","title_how-to-evaluate-and-choose-a-data-governance-framework","text_how-to-evaluate-and-choose-a-data-governance-framework_0","card_v2_how-to-evaluate-and-choose-a-data-governance-framework_0","title_the-6-phases-of-data-governance-framework-implementation","text_the-6-phases-of-data-governance-framework-implementation_0","callout_the-6-phases-of-data-governance-framework-implementation_0","text_the-6-phases-of-data-governance-framework-implementation_1","title_implementation-timeline","text_implementation-timeline_0","title_ai-governance-frameworks-and-data-governance","text_ai-governance-frameworks-and-data-governance_0","title_industry-specific-framework-guidance","text_industry-specific-framework-guidance_0","title_framework-maturity-assessment","text_framework-maturity-assessment_0","title_bringing-structure-to-governance-decisions","text_bringing-structure-to-governance-decisions_0","callout_bringing-structure-to-governance-decisions_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-09255a856a",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-6c61cac2e7","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-8a120d8827","additionalClasses":"page-toc","text":"\u003Cul\u003E\r\n\u003Cli data-anchor=\"what-is-a-data-governance-framework\"\u003EWhat is a data governance framework?\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-governance-framework-purpose-and-value\"\u003EData governance framework purpose and value\u003C/li\u003E\r\n\u003Cli data-anchor=\"core-components-of-a-data-governance-framework\"\u003ECore components of a data governance framework\u003C/li\u003E\r\n\u003Cli data-anchor=\"examples-of-data-governance-frameworks\"\u003EExamples of data governance frameworks\u003C/li\u003E\r\n\u003Cli data-anchor=\"how-to-evaluate-and-choose-a-data-governance-framework\"\u003EHow to evaluate and choose a data governance framework\u003C/li\u003E\r\n\u003Cli data-anchor=\"the-6-phases-of-data-governance-framework-implementation\"\u003EThe 6 phases of data governance framework implementation\u003C/li\u003E\r\n\u003Cli data-anchor=\"implementation-timeline\"\u003EImplementation timeline\u003C/li\u003E\r\n\u003Cli data-anchor=\"ai-governance-frameworks-and-data-governance\"\u003EAI governance frameworks and data governance\u003C/li\u003E\r\n\u003Cli data-anchor=\"industry-specific-framework-guidance\"\u003EIndustry-specific framework guidance\u003C/li\u003E\r\n\u003Cli data-anchor=\"framework-maturity-assessment\"\u003EFramework maturity assessment\u003C/li\u003E\r\n\u003Cli data-anchor=\"bringing-structure-to-governance-decisions\"\u003EBringing structure to governance decisions\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"]}},":itemsOrder":["container"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_132267907":{"id":"flexible-column-container-01bb8817d6","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",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy":{"id":"title-v2-cb78181bf3","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Frequently Asked Questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-8ed8c17358","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EYour common questions about data governance frameworks, 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"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-9a661c9620","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"What is the difference between a data governance framework and a data governance policy?","richText":"\u003Cp\u003EA framework organizes the overall governance operating model. 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