Data Governance Day to Day
Manage priorities, incidents, and everyday decisions inside a governance program.
Open simulator
Extract Value from Your Data
Data Governance
Strategy
Turn data into strategic assets by orchestrating people, processes, and technology end-to-end.
Corporate Mantra
"Governance for People, Value from Data"
DAMA International Standard
Our Methodology
Based on the prestigious DAMA framework, our methodology ensures that governance is not an isolated event, but a continuous corporate function that guarantees the availability, integrity, and security of the data asset.
Framework & Strategy
The Pillars of Data Governance
Governance Foundations
(The Compass)
Establish the solid foundation your organization needs to know "how to decide" about its data with clear rules.
Policies
Definition of the General Data and Corporate Security Policy.
Roles & Responsibilities
RACI matrix setup mapping Data Owners, Data Stewards, and Custodians.
Data Criticality
Classification levels (Very Critical, Critical, Non-Critical) based on risks.
Use Case
(The Engine)
Apply data governance to a priority use case to demonstrate immediate end-to-end value.
Data Quality
Data profiling and quality rules aligned with business rules.
Metadata
Creation of a unified Enterprise Catalog and Business Glossary.
Control & Security
Classification levels (Sensitive, Private) and Role-Based Access Control (RBAC).
Change Management
(The Heart)
The most strategic pillar. Data Governance is about culture and organizational shifts, not just tech.
The Human Side
Address individual behavior. Break down corporate friction and complacency.
Adapted Language
Avoid technical jargon and communicate strictly in value and business outcomes.
Engagement
Focus on engagement, proving that proper data structures make work effortless.
Practice real decisions
Data Governance Simulators
Test your judgment in interactive scenarios covering operations, ownership, and data literacy.
Who Owns This Data?
Resolve an ownership conflict by balancing accountability, authority, and business value.
Open simulatorData Literacy
Interpret metrics, challenge visualizations, and strengthen evidence-based decisions.
Open simulatorReal stories. Shared lessons.
The Data Governance Confession Wall
An anonymous space to discover real data disasters, learn from what went wrong, and turn costly mistakes into better practices. Share your own experience so other teams do not have to learn it the hard way.
Practical Library
Downloadable Resources & Templates
Ready-to-use tools that turn data governance principles into concrete action.
Business case
PDF report
The Cost of Ignorance
Three numbers you already know — headcount, hours lost, average salary — turned into an annual cost, with the breakdown and a PDF report you can take to your leadership.
DAMA Framework
AI Governance
Playbooks and assessment
Three complete PDFs: the Data Governance Playbook, the AI Governance Playbook with its 5W2H canvas, and the printable maturity questionnaire. No form, in your language.
Editable Excel
Ready to use
Template library
Business glossary, ownership RACI, data quality issue log, asset inventory, council minutes and more: the spreadsheets a governance program actually runs on.
See the whole library, with the glossary and the maturity assessment
From the journal
Latest thinking on data governance
New writing on governance, culture, literacy, and responsible AI — practical enough to use on Monday.
Data Culture
From Data Tyranny to Data Democracy: Making Data Work for Everyone
Move from restrictive data bottlenecks to a balanced data democracy. Learn how federated governance, self-service analytics, and context-based access controls empower teams while maintaining security.
· 6 min read
Read articleData Quality
When Good Data Goes Bad: Why End-to-End Data Quality is the Backbone of Governance
Bad data doesn't just appear—it travels. Learn why continuous, end-to-end data quality checks across the entire data value chain are critical for effective governance.
· 8 min read
Read articleData Governance
Identifying and Addressing Data Pain Points: The First Step in Data Governance
Learn how identifying organizational data pain points (silos, quality issues, ownership gaps) is the foundational step toward a successful data governance strategy.
· 9 min read
Read articleDAMA Maturity Scorecard
Turn your answers into a roadmap
Complete the scorecard and automatically receive an executive view of your organization's maturity.
Automatic results upon completion
Discover your data maturity level
A practical DAMA-based scorecard that turns your answers into a visual, actionable diagnosis.
Start the maturity assessmentReport generated and emailed after completing the scorecard.
Instant Executive Report
Detailed diagnosis with your DAMA maturity level, from Level 1 to 5.
Personalized Radar Chart
A five-axis radar map to see strengths and bottlenecks at a glance.
3 Immediate Quick Wins
Priority actions focused on your lowest-scoring pillar.
Direct Answers
Frequently asked questions
Essential context on Data Governance, DAMA methodology, and corporate change management.
What is Data Governance according to the DAMA framework?
DAMA calls it the exercise of authority and control over the management of data assets, which is precise and lands badly in a meeting. What it means day to day is deciding who decides: who owns a definition, who approves access, who is accountable when a number turns out to be wrong. Policies, roles and rules are how those decisions get written down so they still hold six months after the meeting.
What is the difference between Data Governance and Data Management?
Governance decides, management builds. Governance sets the strategy, the policies and the decision rights: who owns the customer definition, who approves access, what quality level is good enough. Data management is the technical work that carries those decisions out across architecture, integration and security. You need both. One without the other is either a policy nobody implements or a platform nobody agrees on.
How do you measure the ROI of a Data Governance project?
In three places, and you can usually count all three. Cost: the hours your people spend cleaning data, reconciling reports and rebuilding numbers nobody trusts. Revenue: the analytics and AI models you can act on because what feeds them is dependable. Risk: the fines, breaches and audit findings you did not have. If you want a figure to open the conversation with, put your own rework hours into the cost of bad data calculator on this site.
How long does a Data Governance implementation project take?
A diagnostic plus a first governed use case usually runs 8 to 12 weeks: a few weeks to see clearly where it hurts, the rest to fix one thing end to end. What you have at the end is not a finished program. It is a working example and a plan the organization believes, which is what you need before taking on the next domain.
SB
Data Leader
Sandy Bradbury
Lead Consultant
LinkedIn Partner
Who’s behind this site
Hi, I'm Sandy Bradbury. I do data governance for a living and I'm CDMP-certified through DAMA International. When I first got interested in this, decent information was genuinely hard to find. Years later, with the experience behind me, I caught myself explaining the same things over and over to people looking for exactly what I hadn't been able to find.
So I started writing them down. That's what this site is — articles, a glossary, templates, playbooks, a cost-of-bad-data calculator and three simulators for practising the hard calls. No forms to fill in, no paywall, and all of it in three languages. If you want to build your governance yourself, there's enough here to do it.
And if you need help? That's what the advisory work is for. Some people read this, apply it and I never hear from them again — genuinely fine by me. Others would rather have someone alongside them when a decision has to hold up in front of a steering committee, when the glossary finally needs sorting out, or when somebody has to run the workshop. If that's you, write to me and we'll talk it through.
DAMA Certifications
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