How to Build Data Trust in European Organizations

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

In my previous post, I explored the hidden costs of data silos. Today, I want to share practical steps that deliver value without requiring immediate organisational restructuring or technology overhauls. The journey from siloed to integrated data follows a maturity curve, beginning with quick wins and progressing toward more substantial transformation. For immediate progress: 1) Identify your "golden datasets": Focus on the 20% of data driving 80% of decisions. Prioritise customer, product, and financial datasets that cross departmental boundaries. 2) Create a simple business glossary: Document how terms differ across departments. When Finance defines "revenue" differently than Sales, capturing both definitions creates transparency without forcing uniformity. 3) Implement read-only integration patterns: Establish one-way flows where analytics platforms access source data without disrupting existing systems. These connections create cross-silo visibility with minimal risk. 4) Build a culture of trust: Reward cross-departmental collaboration. Create incentives that make data sharing a path to recognition rather than a threat to influence or expertise. 5) Establish cross-functional data forums: Host regular meetings where data users share challenges and use cases, building relationships while identifying practical integration opportunities. As these initiatives gain traction, organisations can advance to more substantial approaches: 6) Match your approach to complexity: Smaller organisations often succeed with centralised data management, while larger enterprises typically require domain-centric strategies. 7) Apply bounded contexts: Map where business domains have distinct needs and terminology, creating clear translation points between areas like Sales, Finance, and Operations. 8) Adopt a data product mindset: Designate product owners for critical datasets who treat data as a product with clear consumers and quality standards rather than simply an asset to be stored. 9) Develop a federated metadata approach: Catalogue not just what exists, but how data relates across domains, making relationships between siloed systems explicit. 10) Maintain disciplined data modelling: Well-structured data within domains makes integration between them far more manageable, regardless of your architectural approach. This stepped approach delivers immediate value while building momentum for more sophisticated strategies. The most successful organisations pair technical solutions with cultural transformation, recognising that effective data integration is ultimately about people collaborating across boundaries. In my next post, I'll explore how governance models evolve with data integration maturity. What approaches have you found most effective in addressing data silos? #DataStrategy #DataCulture #DataGovernance #Innovation #Management

The Dark Truth About Data Most Won't Admit: Most organizations treat data like a forbidden vault: • Collect everything blindly • Obscure the mechanics • Pray for trust But here's the reality - trust isn't given, it's earned through visibility. When you embrace data transparency, three powerful forces emerge: 1. Confidence Cascade Clear practices lead to deeper trust Deeper trust enables richer engagement Richer engagement yields superior insights 2. Protection Multiplier Visible processes catch issues early Early detection enables rapid response Rapid response prevents major breaches 3. Innovation Accelerator Open systems encourage bold experiments Experiments generate fast learning Learning creates breakthrough solutions The winners in today's landscape? Not those hoarding the most data. It's those who handle it with radical transparency. DFFT (Data Free Flow with Trust) isn't just another acronym. It's the bedrock of sustainable data operations in our connected world. First introduced at the 2019 G20 Osaka Summit, DFFT established the blueprint for trustworthy cross-border data flow while unlocking unprecedented economic potential. Because in our complex reality: • Secrecy breeds doubt • Transparency cultivates trust • Trust fuels exponential growth The mandate is clear: Build systems that explain themselves. Create processes that invite scrutiny. Establish practices that build confidence. Your data will flourish. Your users will commit. Your business will thrive. The future belongs to those who embrace transparency not as a burden, but as a competitive advantage. Are you ready to build systems that last through trust? Share if you believe in creating transparent data ecosystems that endure. #DataTransparency #DFFT #DataTrust #Innovation

After years of hearing organizations ask, “Can we trust this data?”—even after massive investments in governance—it's clear we need a different approach. As data volume and velocity grow, and the business depends more than ever on trustworthy data, we have to stop reacting to data problems after the fact. We need to prevent them at the source. That’s why forward-thinking CDOs are shifting from reactive governance to proactive, contract-driven approaches that guarantee consistency and quality. Here are three strategic moves I see making the biggest impact: → Create data contracts first using shift left principles → Automate compliance with governance embedded at the source → Evolve your data team from firefighters to innovation enablers Starting with data contracts sets a foundation of trust. When governance is built into the contract and travels with the data through CI/CD pipelines, compliance becomes self-enforcing. No manual policing—just clean, reliable data products that accelerate innovation instead of slowing it down. I wrote about how CDOs can start a governance transformation in this CDO Magazine article. https://lnkd.in/eW7gSJ5m When it comes to data, do you feel like you are constantly fighting fires? #DataGovernance #DataReliability #CDOMagazine