A data governance framework is a coordinated set of roles, responsibilities, policies, and processes for managing data quality and use.
Data governance keeps data validation aligned with business objectives by assigning responsibility for data quality, improvement, and problem resolution. section 14.3.6
A practical governance setup connects validation to operational response:
Define data constraints and acceptable quality ranges. section 14.3.6
Run a recurring validation pipeline that reports quality by dimension. section 14.3.6
Alert responsible teams when quality leaves the acceptable range, then track fixes and improve the constraints over time. section 14.3.6
How Machine Learning System Design covers it
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 it assumes
Assumes defined data constraints and acceptable quality ranges while establishing responsibilities and resolution processes.
What you can do after
Operate a governed validation process with assigned responsibilities, actionable alerts, and evolving quality constraints.
How it connects
Connects data quality management with data validation, monitoring, reliability, alerts, and business objectives.
Generative AI in ActionData governance is the framework of policies, responsibilities, and controls for managing data quality, access, use, and compliance.
Architecting an Apache Iceberg LakehouseData governance comprises the policies, processes, and controls for ensuring that data is managed, protected, documented, and used appropriately.
Digital Twins in ActionGovernance is the framework of ownership, accountability, decision rights, controls, and processes that directs and oversees a system.
Effective Platform EngineeringGovernance is the set of policies, controls, and decision processes that direct and constrain organizational technology practices.
Introduction to Generative AI, Second EditionData governance is the set of policies, processes, roles, and controls that determine how data is collected, managed, used, protected, and overseen.
First-Party Data ActivationData governance is the set of policies, roles, controls, and practices that manage data quality, access, security, and responsible use.
Snowflake Data EngineeringData governance is the framework of policies, roles, standards, and controls for managing data availability, quality, security, and compliance.
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.
This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.
