Data governance
Data governance is the organizational framework of policies, roles, responsibilities, processes, and controls that directs how data is managed, accessed, protected, and used.
Generative AI in Action
Data governance is the framework of policies, responsibilities, and controls for managing data quality, access, use, and compliance.
- How it is used
- Guides responsible and secure handling of enterprise data across generative AI workflows.
- How deep it goes
- Identifies policy concerns and operational enforcement points, but does not provide a detailed policy template or implementation procedure.
- Examples
- Uses confidential RAG indexes and orchestrated processing, storage, and use as concrete control points.
- What you can do after
- Practice/perform it by setting ownership, privacy, and security policies, then checking data handling and index maintenance against them.
Architecture Modernization: Socio-technical alignment of software, strategy, and structure
Data governance is the system of policies, responsibilities, and controls for managing data quality, security, consistency, and appropriate use.
- How it is used
- Balances team autonomy with consistent handling of sensitive data and changing regulatory obligations.
- How deep it goes
- Frames governance as holistic and adaptable, without specifying policies, responsibilities, or technical controls.
- Examples
- Uses national financial-data retention differences and a growing HR-tech company's sensitive payroll data.
- What you can do after
- Evaluate/understand why greater team autonomy calls for attention to data consistency and governance.
Architecting an Apache Iceberg Lakehouse
Data governance comprises the policies, processes, and controls for ensuring that data is managed, protected, documented, and used appropriately.
- How it is used
- Guides lakehouse architecture by translating stakeholder, compliance, metadata, and operational requirements into enforceable platform choices.
- How deep it goes
- Explains requirements, catalog tradeoffs, federated metadata, and snapshot references, but stops short of a complete policy implementation.
- Examples
- Uses stakeholder interviews, a multinational healthcare deployment, REST catalog requirements, and an Iceberg refs query.
- What you can do after
- Evaluate and design governance capabilities that balance trust, compliance, interoperability, usability, and operational constraints.
Read how Architecting an Apache Iceberg Lakehouse explains data governance →
Digital Twins in Action
Governance is the framework of ownership, accountability, decision rights, controls, and processes that directs and oversees a system.
- How it is used
- Establishes accountability for data and decisions as a digital twin crosses organizational boundaries and enters production.
- How deep it goes
- Identifies owners, approvals, provenance, validation, access controls, and audit trails, but defers a full governance and security treatment.
- Examples
- Contrasts informal provenance for home utility data with document revisions, accountable owners, and expert validation for industrial data.
- What you can do after
- Operate/configure it by assigning owners and approvals, preserving traceability, and distinguishing access to viewing, model changes, and physical actions.
Effective Platform Engineering
Governance is the set of policies, controls, and decision processes that direct and constrain organizational technology practices.
- How it is used
- Directs platform engineering toward enforceable controls for security, audit, compliance, data access, and organizational trust.
- How deep it goes
- Connects governance requirements to platform guardrails and automation, but stops short of specifying a complete governance operating model.
- Examples
- Uses sensitive dataset authorization, deployment evidence for SOX, cluster security baselines, and deployment-integrated secrets management.
- What you can do after
- Operate/configure platforms with baseline controls that automate security, compliance, governance, and authorized data access.
Read how Effective Platform Engineering explains data governance →
Machine Learning System Design
A data governance framework is a coordinated set of roles, responsibilities, policies, and processes for managing data quality and use.
- How it is used
- Aligns data validation, quality management, and corrective action with business objectives and an ML use case.
- How deep it goes
- Describes roles, responsibilities, processes, monitoring, and alerts, but stops short of specifying organizational structures or policies.
- Examples
- Uses a recurring data validation pipeline, dimension-level quality report, acceptable ranges, and reliability alerts.
- What you can do after
- Operate a governed validation process with assigned responsibilities, actionable alerts, and evolving quality constraints.
Read how Machine Learning System Design explains data governance →
Introduction to Generative AI, Second Edition
Data governance is the set of policies, processes, roles, and controls that determine how data is collected, managed, used, protected, and overseen.
- How it is used
- Centers responsible AI development on lifecycle-wide oversight rather than treating privacy compliance as an afterthought.
- How deep it goes
- Traces risks from collection through outputs and names organizational controls, without detailing their implementation.
- Examples
- Uses AI training data, model outputs, student information, vendor agreements, and institutional privacy protocols as concrete cases.
- What you can do after
- Evaluate AI data practices by identifying lifecycle risks, accountability gaps, privacy obligations, and suitable governance controls.
Read how Introduction to Generative AI, Second Edition explains data governance →
First-Party Data Activation
Data governance is the set of policies, roles, controls, and practices that manage data quality, access, security, and responsible use.
- How it is used
- Protects marketing data operations by formalizing policies, documentation, auditability, recovery, and responsible activation.
- How deep it goes
- Explains operational controls, logging, rollback, training, and recovery choices, stopping short of detailed policy design or implementation instructions.
- Examples
- Uses flawed mappings, outdated files, corrupted segments, migration logs, legacy-report comparisons, and real-time campaign activation as examples.
- What you can do after
- Operate/configure governed marketing data workflows with audit trails, rollback procedures, training, and recovery choices aligned to business risk.
Read how First-Party Data Activation explains data governance →
Snowflake Data Engineering
Data governance is the framework of policies, roles, standards, and controls for managing data availability, quality, security, and compliance.
- How it is used
- Integrates authorization and data-protection requirements into Snowflake pipelines serving analytics, reporting, data science, and other consumers.
- How deep it goes
- Explains RBAC, row access policies, and masking policies with business-unit and sensitive-data scenarios, without detailing broader governance operations.
- Examples
- Uses a bakery with data-engineer and data-analyst roles, separate business units, restricted rows, and masked personal data.
- What you can do after
- Operate/configure Snowflake RBAC, row access policies, and masking policies for role, business-unit, and sensitive-data restrictions.
Read how Snowflake Data Engineering explains data governance →
Kafka for Architects
Data governance is the organizational discipline of defining and enforcing policies for managing data and its use.
- How it is used
- Organizes ownership, requirements, schemas, topics, and deployment artifacts before event-driven workloads scale.
- How deep it goes
- Moves from governance rationale to schemas, compatibility, automation, versioning, and managed Kafka artifacts, without low-level implementation detail.
- Examples
- Uses ProfileService and TransactionService fact events, optional schema fields, topic configurations, connector settings, and streaming queries.
- What you can do after
- Operate/configure Kafka projects with documented ownership, validated event contracts, compatible schemas, and consistently managed infrastructure.
Related concepts
- data contractpart of · A data contract is an agreement specifying the structure, meaning, and expectations for data exchanged between producers and consumers.
- AI Actconfigured by · The AI Act is a European Union regulation establishing harmonised, risk-based requirements for the development, placing on the market, and use of artificial intelligence systems.
- GDPRconfigured by · The General Data Protection Regulation (GDPR) is a European Union regulation governing the processing of personal data and protecting individuals’ rights concerning that data.
- lakehouse catalogconfigured by · A catalog is a service or interface that registers lakehouse tables, resolves their metadata, and coordinates table updates across data engines.
- first-party data strategypart of · A coordinated plan for collecting, storing, governing, analyzing, and activating an organization’s first-party data.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.