Approaches to Data-Driven Distributed Governance

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

Over the past 10+ years, I’ve had the opportunity to author or contribute to over 100 #datagovernance strategies and frameworks across all kinds of industries and organizations. Every one of them had its own challenges, but I started to notice something: there’s actually a consistent way to approach #data governance that seems to work as a starting point, no matter the region or the sector. I’ve put that into a single framework I now reuse and adapt again and again. Why does it matter? Getting this framework in place early is one of the most important things you can do. It helps people understand what data governance is (and what it isn’t), sets clear expectations, and makes it way easier to drive adoption across teams. A well-structured framework provides a simple, repeatable visual that you can use over and over again to explain data governance and how you plan to implement it across the organization. You’ll find the visual attached. I broke it down into five core components: 🔹 #Strategy – This is the foundation. It defines why data governance matters in your org and what you’re trying to achieve. Without it, governance will be or become reactive and fragmented. 🔹 #Capability areas – These are the core disciplines like policies & standards, data quality, metadata, architecture, and more. They serve as the building blocks of governance, making sure that all the essential topics are covered in a clear and structured way. 🔹 #Implementation – This one is a bit unique because most high-level frameworks leave it out. It’s where things actually come to life. It’s about defining who’s doing what (roles) and where they’re doing it (domains), so governance is actually embedded in the business, not just talked about. This is where your key levers of adoption sit. 🔹 #Technology enablement – The tools and platforms that bring governance to life. From catalogs to stewardship platforms, these help you scale governance across teams, systems, and geographies. 🔹 #Governance of governance – Sounds meta, but it’s essential. This is how you make sure the rest of the framework is actually covered and tracked — with the right coordination, forums, metrics, and accountability to keep things moving and keep each other honest. In next weeks, I’ll go a bit deeper into one or two of these. For the full article ➡️ https://lnkd.in/ek5Yue_H

Data access isn’t just a technical challenge; it’s a foundation for responsible innovation across the enterprise. As organizations scale data, AI, and analytics initiatives, the ability to balance agility, security, and compliance becomes a boardroom conversation. RBAC (Role-Based Access Control) has been the workhorse for access management, straightforwardly granting permissions based on defined roles, think “Finance Analyst” or “HR Manager.” It’s clear, easy to audit, and effective for static user groups and simple business logic. But the real world rarely fits within fixed roles. This is where ABAC (Attribute-Based Access Control) in Databricks makes a difference. ABAC uses dynamic attributes such as time, geographic region, and data classification to govern access in real time. Suddenly, granting temporary collaboration rights for a cross-border team or restricting access to confidential records based on sensitivity becomes seamless, reducing the risk of overexposure and manual error. For data practitioners, this means less firefighting and more time building. For executives, it means a governance model that adapts to change, whether responding to new regulations, organizational shifts, or growth into new markets. The interplay between RBAC and ABAC in platforms like Unity Catalog gives organizations the best of both worlds: clarity, accountability, and agility. In practice, RBAC establishes the baseline (“who can access what”), while ABAC adds context and flexibility (“under what conditions”). This layered approach not only future-proofs data and AI governance, but it also unlocks new possibilities enabling secure data sharing, collaborative AI, and compliant innovation at scale. #ABAC #RBAC #DataGovernance #UnityCatalog #Databricks

🤔 Weekend Reflections 👉 As we head into the #AIActionSummit, the idea of creating a CERN for AI—both in Europe and beyond—continues to gain momentum. This call has been further amplified by SoftBank's recent $500 billion investment announcement in the US and the release of Deepseek in China 🌍. 🤔 But is a centralized, CERN-style model the only way forward for sustained, responsible innovation in AI? 👉 In my piece for Frontiers Policy Labs, I proposed a different path: a polycentric, distributed approach to AI and science. This model addresses three key challenges in the current AI ecosystem: 1️⃣ Access to computational resources 💻 2️⃣ Access to high-quality data 📊 3️⃣ Access to purposeful AI modeling 🤖 🔗 Read my full article here: https://lnkd.in/ezXxaX_Z 👉 The same rationale can be applied to AI governance, much like the distributed internet governance model I proposed earlier. 🤔 Distributed governance offers a more resilient, flexible, and inclusive framework with several key advantages: ✅ Facilitates cooperation among existing and emerging actors without the need for new bureaucratic structures. It encourages decentralized dialogue on key issues, fostering more flexible and creative solutions to emerging issues and applications than a top-down, centralized system. ✅ Acts as a “routing” function, enabling interoperability and collaboration by adopting shared standards and common ontologies. This approach empowers dispersed actors to contribute innovative solutions, shifting decision-making power to communities and experts who might otherwise be excluded. ✅ Promotes information-sharing and evidence-based decision-making. Distributed governance networks prioritize data-driven approaches, allowing stakeholders to accurately evaluate the effectiveness of governance initiatives across different regions and contexts. ✅ Allows for both granularity (localization) and scale (globalization). Issue- or expert-based organizing principles help coordinate decisions at the local, national, regional, and global levels. This ensures local actors are included in global conversations and prevents issues from escalating unnecessarily (This will also be discussed on Tuesday at our event on Aligning Local and Global AI Governance - See https://lnkd.in/eb8xfJh9). Q How to design AI governance—not as a monolithic institution, but as a dynamic, interconnected network of nodes working toward a common good? 🔗 Read my paper: A Distributed Model for Internet Governance (and eager to hear how it may apply to similar challenges of AI governance): https://lnkd.in/ejyUtset #AIActionSummit #OpenScience #DistributedGovernance #AIInnovation #Collaboration #PolycentricAI #AIgovernance #Deepseek #CERN

Traditional data governance was about control. But the next era is about activation. Forrester’s latest Wave on Data Governance lays this out clearly: the most successful data organizations have stopped asking “How do we govern data?” and started asking “How do we accelerate trust in data & AI?” Some themes that stood out: 1️⃣ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐬 𝐬𝐩𝐥𝐢𝐭𝐭𝐢𝐧𝐠 𝐢𝐧𝐭𝐨 𝐭𝐰𝐨 𝐜𝐚𝐦𝐩𝐬- 𝐀𝐈-𝐧𝐚𝐭𝐢𝐯𝐞 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐯𝐬. 𝐥𝐞𝐠𝐚𝐜𝐲 𝐭𝐨𝐨𝐥𝐬. Forrester highlights a growing divide between platforms that simply manage metadata and those that activate it- embedding intelligence, automation, and collaboration into daily workflows. It’s about being truly agentic, beyond just marketing claims, where governance drives real, actionable outcomes. 2️⃣ 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐨𝐟 𝐬𝐮𝐜𝐜𝐞𝐬𝐬. Forrester makes it clear: the best tools are the ones people actually use. Modern governance isn’t a set of static rules- it’s an ecosystem of processes, platforms, and practices that business and technical teams adopt because it helps them move faster. 3️⃣ 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐯𝐚𝐥𝐮𝐞 𝐢𝐬 𝐧𝐨𝐰 𝐭𝐡𝐞 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤. Forrester introduced a customer feedback dimension in this year’s evaluation, signaling that the time for governance initiatives with zero or unclear ROI is over. Delivering measurable value to users is the new standard. 4️⃣ 𝐒𝐞𝐥𝐟-𝐝𝐫𝐢𝐯𝐢𝐧𝐠 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐬 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐚𝐬𝐩𝐢𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥. The report points to a vision of “agentic AI” for governance- systems that anticipate risks, enforce policies proactively, and reduce manual oversight. This isn’t about dashboards and reports; it’s about governance that runs in the background, scaling as fast as your AI initiatives. 5️⃣ 𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐬 𝐛𝐞𝐜����𝐦𝐢𝐧𝐠 𝐜𝐞𝐧𝐭𝐫𝐚𝐥, 𝐧𝐨𝐭 𝐨𝐩𝐭𝐢𝐨𝐧𝐚𝐥. Data leaders are no longer treating AI governance as a separate layer. It’s being baked into enterprise governance strategies- covering everything from lineage of models to unstructured data handling- because AI without governance is just experimentation at scale. 6️⃣ 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐧𝐨𝐰 𝐭𝐡𝐞 𝐛𝐚𝐭𝐭𝐥𝐞𝐠𝐫𝐨𝐮𝐧𝐝. Governance can’t live in IT anymore. Forrester emphasizes role-based personalization- giving data scientists, business users, and stewards tailored ways to engage with policies and workflows. This shift from gatekeeping to enablement is what makes governance stick. The message is clear: Governance has moved beyond compliance. It’s becoming the operating system for AI-native enterprises.

Everyone celebrates the AI skyline. Almost no one wants to invest in the foundation. That foundation is data governance. Not as a policy exercise, but as an operating discipline. When governance is weak, AI looks impressive at first: fast demos clever outputs early wins Then reality shows up: inconsistent answers hidden bias teams arguing over whose data is “right” leaders quietly losing trust in the system That’s not an AI failure. It’s a foundation failure. Here’s the practical playbook I’ve helped organizations use to fix it: 1) Assign real ownership, not committees Every critical data domain needs a clear owner with actual decision rights. If no one owns the data, the model ends up guessing. → Leader question: Who is accountable when this data misleads a decision? 2) Define “good data” in business terms Quality only matters in context. Accuracy, timeliness, and completeness must be tied to how the data is used, not how it’s stored. → Leader question: What decision breaks if this data is wrong or late? 3) Design guardrails before scale Not every dataset should feed every model. Governance is about boundaries: what AI can see, what it can influence, what it can automate. → Leader question: Where must humans stay in the loop, no matter how good the model gets? 4) Treat data pipelines like production systems Monitoring, lineage, versioning, and rollback aren’t optional. If you can’t trace an output back to its source, you can’t trust it. → Leader question: Could we explain this answer six months from now? 5) Build governance where work actually happens Policies on slides don’t scale. Embedded checks in workflows do. → Leader question: Is governance preventing rework later, or just slowing teams down today? AI doesn’t fail because it’s too advanced. It fails because the groundwork was never finished. If you want a skyline that lasts, build where no one is looking. 📌 Save this if AI reliability is now a leadership issue 🔁 Repost to shift the conversation from demos to durability 👤 Follow Gabriel Millien for grounded insight on Enterprise AI and transformation

Many executive teams are treating AI governance as something new. New committees. New AI policies. New risk frameworks. The reality: If your data governance is weak, your AI governance is performative. AI governance isn’t a separate program. It is the direct expression of your data governance maturity. And the organizations pulling ahead understand that. 1/ You Cannot Govern What You Cannot Trace AI amplifies the foundation it sits on. If your data is: → Fragmented → Poorly classified → Inconsistently defined → Lacking lineage visibility Your AI outputs will be: → Hard to explain → Difficult to audit → Risky to scale If you cannot trace where data originated, how it was transformed, and who owns it, you cannot credibly govern AI built on top of it. 2/ Data Ownership Determines AI Accountability AI governance often focuses on bias and oversight. But accountability starts earlier. → Who owns the data feeding the model? → Who defines quality thresholds? → Who approves usage rights? If those answers are unclear, AI accountability will be too. Clear data ownership creates clear AI accountability. 3/ Governance Must Move From Documentation to Execution Policy-heavy governance collapses under AI velocity. Leading organizations embed: → Automated classification → Real-time lineage tracking → System-enforced access controls → Policy execution within workflows Governance must operate in the system. 4/ Unification Reduces Hidden Risk When data definitions differ across business units, outputs become inconsistent. When systems are fragmented, risk visibility becomes partial. Unifying definitions, taxonomies, and metadata reduces hidden risk and accelerates deployment. 5/ AI-Specific Controls Only Work on a Strong DG Foundation With mature DG, AI governance becomes achievable: → Human-in-the-loop review for regulated decisions → Bias and drift monitoring → Model performance tracking → Audit trails linking outputs to source data Without strong DG, these controls are cosmetic. 6/ Trust Is Built on Data Discipline AI adoption is fundamentally a trust issue. Employees won’t rely on outputs they can’t explain. Boards won’t scale what they can’t see. Data governance builds: → Accuracy → Transparency → Reproducibility Trust is a structural outcome of disciplined governance. 7/ Governance Maturity Drives Risk-Adjusted Speed Governance is often treated as a cost center. But governance maturity determines AI velocity. Organizations with strong DG can: → Deploy AI faster → Scale it safely → Withstand scrutiny → Respond quickly to issues Their innovation is not just faster; it’s safer. Instead of asking: “Do we have AI governance?” Ask: “Is our data governance mature enough to support AI at scale?” Save this for future reference.

This is what Data Governance MUST look like in 2026. Not one centralized governance team controlling everything. Our team sync today spanned Utah → Texas → Virginia → Florida → Bangalore → Hyderabad → Kenya and beyond.... Different backgrounds. Different perspectives. One shared mission. And this mirrors exactly how modern Data & AI Governance needs to operate. For years, governance meant: • Centralized committees • Spreadsheet inventories • Quarterly policy reviews • Endless documentation But governance doesn't work that way anymore. Data is distributed. Teams are distributed. Decisions are distributed (and increasingly agentic). Governance must evolve or become a bottleneck. The future demands three core shifts (backed by what industry leaders are seeing in 2026): 1️⃣ Distributed Ownership Embed accountable data owners across domains and business units. Move from top-down control to federated models (think data mesh principles) where domain teams own their data as products, with centralized standards for consistency. This democratizes access, reduces silos, and scales governance without creating chokepoints. 2️⃣ Community & Collaboration Governance thrives when it's cross-functional: business, risk, tech, compliance, and even regulators co-build. It's not siloed policy, it's ongoing dialogue, shared metadata, and ecosystem partnerships that align on governance and data standards. 3️⃣ AI-Driven Automation Manual processes can't keep up. AI agents now continuously monitor, certify, and validate critical data/assets/models in real-time. They automate lineage tracking, quality checks, policy enforcement, anomaly detection, and context engineering for agentic workflows turning governance from a drag into an enabler of faster, trusted innovation. The result? Governance that doesn't slow the business, it accelerates it safely. Teams built like this are global, mindset-aligned, leveraging AI and are how the shift happens. We're living it at CoComply AI, curious how your org is tackling this evolution? What's one change you're making for 2026 governance? Drop thoughts below 👇 #DataGovernance #AIGovernance #Fintech #RegTech #AIinBanking #AgenticAI #GlobalTeams #CoComply

Data Governance deserves a comprehensive strategic framework Because without thinking strategically, you are sure as hell DG will fail So here is my version with five components: 🎯 𝗦𝗲𝘁 𝗼𝘂𝘁 𝘁𝗵𝗲 ‘𝗪𝗵𝘆’ 𝟭. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗗𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗩𝗶𝘀𝗶𝗼𝗻 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘱𝘶𝘳𝘱𝘰𝘴𝘦 𝘰𝘧 𝘨𝘰𝘷𝘦𝘳𝘯𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘵𝘰 𝘦𝘯𝘢𝘣𝘭𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘷𝘢𝘭𝘶𝘦? 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘰𝘶𝘳 𝘦𝘯𝘥 𝘨𝘰𝘢𝘭? - Starts with a clear vision of how data will enable business objectives - This is about establishing ‘why data governance needs to exist’ 🧠 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝘁𝗵𝗲 𝗗𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝗮𝗹 ‘𝗛𝗼𝘄’ 𝟮. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘦𝘯𝘴𝘶𝘳𝘦 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘦𝘯𝘢𝘣𝘭𝘦𝘴 𝘥𝘢𝘵𝘢 𝘱𝘳𝘰𝘨𝘳𝘦𝘴𝘴 𝘪𝘯𝘴𝘵𝘦𝘢𝘥 𝘰𝘧 𝘪𝘯𝘩𝘪𝘣𝘪𝘵𝘪𝘯𝘨 𝘪𝘵? - The operational framework that provides clear, actionable guidance on how data should be handled. - Artefacts or policies would be built with these principles in mind 𝟯. 𝗧𝗲𝗮𝗺 𝗚𝗼𝗮𝗹𝘀 𝗮𝗻𝗱 𝗧𝗮𝗿𝗴𝗲𝘁𝘀 𝘞𝘩𝘢𝘵 𝘥𝘰 𝘸𝘦 𝘸𝘢𝘯𝘵 𝘋𝘢𝘵𝘢 𝘎𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘵𝘰 𝘢𝘤𝘩𝘪𝘦𝘷𝘦? 𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 𝘰𝘯 𝘵𝘩𝘦 𝘚𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘤 𝘋𝘪𝘳𝘦𝘤𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘝𝘪𝘴𝘪𝘰𝘯 𝘸𝘩𝘪𝘭𝘦 𝘦𝘮𝘣𝘰𝘥𝘺𝘪𝘯𝘨 𝘰𝘶𝘳 𝘖𝘱𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘗𝘳𝘪𝘯𝘤𝘪𝘱𝘭𝘦𝘴? - Set goals and targets within your governance programs - These must measure business impact, not just compliance metrics and be as concrete and measurable as possible 𝟰. 𝗛𝗶𝗴𝗵-𝗟𝗲𝘃𝗲𝗹 𝗥𝗼𝗹𝗲𝘀, 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀, & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝘞𝘩𝘰 𝘪𝘴 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘭𝘦 𝘧𝘰𝘳 𝘥𝘦𝘭𝘪𝘷𝘦𝘳𝘪𝘯𝘨 𝘵𝘩𝘦𝘴𝘦 𝘨𝘰𝘢𝘭𝘴 𝘢𝘯𝘥 𝘸𝘩𝘰 𝘯𝘦𝘦𝘥𝘴 𝘵𝘰 𝘣𝘦 𝘪𝘯𝘷𝘰𝘭𝘷𝘦𝘥? 𝘞𝘩𝘢𝘵 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘣𝘦 𝘴𝘦𝘵 𝘰𝘶𝘵 𝘵𝘰 𝘢𝘤𝘩𝘪𝘦𝘷𝘦 𝘵𝘩𝘪𝘴? - Roles and responsibilities are a core tenet of any successful governance program - And for good reason: accountability is the backbone of governance. Therefore, the structure must include well-defined roles that balance centralised coordination with distributed ownership. 📅 𝗔𝗿𝘁𝗶𝗰𝘂𝗹𝗮𝘁𝗲 ‘𝗪𝗵𝗮𝘁’ 𝗡𝗲𝗲𝗱𝘀 𝘁𝗼 𝗯𝗲 𝗗𝗼𝗻𝗲 𝟱. 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘦𝘹𝘦𝘤𝘶𝘵𝘦 𝘰𝘯 𝘰𝘶𝘳 𝘋𝘢𝘵𝘢 𝘎𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘪𝘯𝘪𝘵𝘪𝘢𝘵𝘪𝘷𝘦𝘴? 𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘬𝘦𝘺 𝘥𝘦𝘵𝘢𝘪𝘭𝘴 𝘵𝘰 𝘦𝘯𝘴𝘶𝘳𝘦 𝘴𝘶𝘤𝘤𝘦𝘴𝘴𝘧𝘶𝘭 𝘪𝘮𝘱𝘭𝘦𝘮𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯? - An execution roadmap is crucial for the success of any strategy - The roadmap should include a communication strategy to help establish buy-in across the organisation for governance activities Read my article this week on building a Data Governance Strategy (link in the comments) and let me know what you think!