Data Governance: Frameworks, Roles and Data Quality
Practical guides on Data Governance: frameworks, roles, data catalogs, lineage, data quality, and AI Act compliance. For professionals worldwide.
The AI Act turns what used to be best practice into a legal obligation: documenting the origin of training data, proving its quality, and maintaining a full lineage of transformations.
How to Implement an Effective Data Governance Framework in the AI Act Era
Roles, architecture, lineage and data quality: a practical guide to building an auditable, AI Act-compliant framework, with tools and a final checklist.
Read →Roles and Responsibilities of a Data Governance Team: The Minimum Structure for the AI Act
What each Data Governance role does, what profile it needs, and the minimum structure for governance to work and comply with the AI Act.
Read →What a Data Catalog Is and What It's Really For
What a data catalog is, how it works internally, what tools exist in 2026, and why most implementations fail before the six-month mark.
Read →What Data Lineage Is and Why the AI Act Requires It
What data lineage is, types of lineage, tools to automate it, and why Article 10 of the AI Act makes it a legal obligation for high-risk systems.
Read →Data Quality Management: What It Is, How to Measure It and Why the AI Act Cares
Data quality dimensions, how to implement rules as code, tools in 2026, and what the AI Act requires regarding training data quality.
Read →How to Measure Data Quality: KPIs, Thresholds and Dashboards
The 7 fundamental data quality KPIs, how to set thresholds with the business, how to build a dashboard someone with authority actually checks weekly, and where to measure in the pipeline.
Read →The 6 Dimensions of Data Quality, Explained With Real Cases
Completeness, accuracy, consistency, timeliness, validity and uniqueness: what each one measures, why it matters, and real cases that illustrate the impact when it fails.
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