Significance of Data Governance

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 governance is hitting a critical tipping point - and there are three big problems (and solutions) you can’t ignore: 1️⃣ Governance is Always an Afterthought: Often, governance only becomes important once it's too late. Fix: Embed governance from the start. Show quick wins so it's viewed as an enabler, not just cleanup. 2️⃣ AI Exposes - and Amplifies - Flaws: AI governance introduces exponential complexity. Fix: Proactively manage risks such as bias and black-box decisions. Automate data lineage and compliance checks. 3️⃣ Nobody Wants to ‘Do’ Governance: Mention "governance" and expect resistance. Fix: Make it invisible. Leverage AI to auto-document metadata and embed policies directly into everyday workflows, allowing teams to confidently consume data without friction. Bottom Line: → Plan governance early - late-stage fixes cost significantly more. → Use AI to do the heavy lifting - ditch manual spreadsheets. → Tie governance clearly to business outcomes like revenue growth and risk mitigation so it’s championed by leaders. Governance done right isn’t just compliance; it’s your strategic advantage in the AI era.

If data quality is about being fit for purpose, then why don't data leaders use business KPI's as data quality metrics? Most DQ frameworks still obsess over the attributes of data - completeness, accuracy, timeliness - without ever asking the most important question: Did the data help the 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐛𝐞𝐭𝐭𝐞𝐫? We’ve had the tools for decades - regression analysis, causal inference - yet few organizations are connecting DQ to the efficiency of the business processes that the data supports. That’s a huge miss. Because until you tie data quality to real-world business outcomes, your governance remains incomplete. Worse yet, it may be misleading. Bad data in analytics? Maybe. But in operations? That exact same data might be perfectly fit for purpose. A rigid, one-size-fits-all DQ standard leads to finger-pointing ("this data is garbage!") when the real issue is a lack of contextual awareness. What's fit for one use may not be fit for another, and vice versa. It’s time we evolve: ✅ Our Governance frameworks must become more adaptive - where there are different sets of data quality rules/policies depending on how the data is used. At a minimum, our policies should adapt to support three contexts: functional/domain, cross-functional, and enterprise-wide. The data mesh movement was all about empowering domains - which is fine, but we cannot also ignore the need to govern data at 'higher' levels of the organization. ✅ Quality metrics that reflect how data impacts business performance must exist, and must also be connected to more 'traditional' DQ metrics, like consistency and accuracy. For example - if there is a duplicate customer record, how does that negatively affect marketing effectiveness? ✅ Recognition that DQ must support both operational and analytical use cases, and that what is 'fit' for one purpose may not be fit for the other. We are quickly approaching a point where quality data is no longer negotiable. Yet, our DQ frameworks - and our general mindset around data quality - are insufficient to support our rapidly evolving business needs. What is necessary is a change of perspective - where the 'quality' of data is measured, in part, by its ability to support our business goals. So... What would it take for your org to start measuring data quality in terms of business outcomes? #dataquality #datagovernance #datamanagement

🚨 #HonestNoBS: Data governance has a branding problem. It’s been labeled as boring, bureaucratic, and the team of “No. But here’s the truth: Governance is finally exciting because it’s the carrot for AI. It’s not about slowing things down, it’s about safe speed. If your data isn’t: ✅ Understood (semantics) ✅ Trusted (business value) ✅ Usable (data products with clear context) ✅ Delivering fast wins (iterative, targeted effort) …then it’s not ready for AI. Here’s how to make governance actually work: 🔥 1. Minimum Valuable Governance Just enough governance to unlock value quickly. No overkill. What this looks like: • Start with the use case, not the policy manual. • Define only what’s necessary (clear terms, roles, semantics). • Engage the right stakeholders early, not everyone all at once. • Allow just enough access and quality to meet the goal. • Use an iterative approach — show quick wins, improve from there. 🛑 No more “boil the ocean” governance programs. ✅ Yes to fit-for-purpose, low-friction, value-first moves. 💡 2. Embedded Governance Built into how people already work, not a separate compliance layer. What this looks like: • Co-design with the business. Front office defines the “what & why,” back office enables the “how.” • Think like an energy company: governance is safety, and everyone owns it. • Governance pioneers = internal personal trainers. Empower, don’t enforce. • Bake governance into tools, workflows, and daily habits — not just into frameworks. Governance isn’t a team: it’s a culture. 📦 3. Data Products & Marketplace Reusable, governed assets people can actually find and use. What this looks like: • Define clear product boundaries and ownership. • Wrap data in contracts: semantics, SLAs, and accountability. • Focus on usability, build for the consumer, not the committee. • Measure impact: usage, satisfaction, business value. And at the center of it all? Metadata. But not stale, siloed metadata. We’re talking: Graphs. Context. Shareability. Here’s what kills governance efforts: ❌ Overengineering & scope creep ❌ Weak communication ❌ No ownership or accountability This is the talk that Tim Gasper and I will be giving at Snowflake today. Our thinking and POV comes from talking to hundreds of day leaders and practitioners, our Catalog & Cocktails Podcast guests (special shoutout to Rebecca O'Kill and Winfried Adalbert Etzel )

Would you make critical business decisions without knowing if your data is accurate, accessible, or even trustworthy?   Many organizations do—because they lack effective data governance.   Governance isn’t just about compliance; it’s about unlocking the full potential of data. And in the age of generative AI, getting it right is more important than ever.   The 2025 Amazon Web Services (AWS) Chief Data Officer study highlights this urgency:   ➝️ 39% cite data cleaning, integration, and storage as barriers to generative AI adoption. ➝️ 49% are working on data quality improvements. ➝️ 46% are focusing on better data integration.   Effective data governance rests on four pillars:   1. Data visibility – Clarify available data assets so teams can make informed decisions. Without full transparency into what data exists and where it lives, AI models risk being trained on incomplete or irrelevant information, reducing their accuracy and reliability.   2. Access control – Balance security and accessibility to enable collaboration without increasing risk. AI adoption requires seamless yet governed data access, ensuring that sensitive information is protected while still being available for innovation.   3. Quality assurance – Ensure data is accurate and reliable for AI-driven insights. Poor data quality leads to hallucinations and flawed predictions, making robust data validation and cleansing essential for AI success.   4. Ownership – Secure leadership commitment to drive accountability and business-wide adoption. Without clear data ownership, AI initiatives struggle to scale, as governance policies remain fragmented and inconsistent across the organization.   Without a strong governance strategy, organizations risk unreliable insights, compliance issues, and missed AI opportunities.   How is your organization tackling data visibility challenges? Let’s discuss.   You can read more on Data Governance in the Age of Generative AI. https://go.aws/4j4F4ni  #DataGovernance #generativeAI #AWS #BuildOnAWS

🔓 Unlocking the Power of AI Through Data Governance: The Role of ISO42001 AIMS🔓 In discussions around AI, much of our focus is often on models, algorithms, and innovation. But what underpins these systems? The answer lies in a foundation often too overlooked: data governance. For organizations pursuing the deployment of an #ISO42001 based #AIMS, data governance is absolutely essential. ➡️ Why Data Governance Matters in AI AI systems are only as good as the data they consume. Poor data quality, biased datasets, or lack of provenance can compromise the integrity of AI outcomes, leading to unreliable insights, reputational harm, or even regulatory breaches. A robust data governance framework ensures data integrity, compliance, and trustworthiness, addressing key challenges such as: 🔸 #Bias and Representation: Without proper data governance, critical questions about data representativeness go unanswered. This leaves organizations vulnerable to producing biased AI models that perpetuate inequities. 🔸 Compliance and Accountability: Regulatory frameworks like the EU AI Act and voluntary standards like ISO42001 require demonstrable governance processes. Organizations must show how data is managed, processed, and protected at every stage of the AI lifecycle. 🔸 Data Lifecycle Management: AI systems rely on dynamic datasets. Data governance ensures every phase—from acquisition to decommissioning—adheres to organizational standards for quality and security. ➡️ Integrating Data Governance into ISO42001 AIMS ISO42001 provides a structured approach to managing AI risks, focusing on transparency, accountability, and ethical use. Data governance plays a pivotal role across its implementation, directly aligning with the standard’s principles: 🔸 Transparency Through Provenance: #ISO5259 highlights the importance of tracking data provenance. Provenance tells us who created the data, how it was modified, and how it has been used. Incorporating these records into your AIMS builds trust and auditability. 🔸 Quality Assurance: Adopting a data quality framework (as outlined in ISO5259-1) ensures that your datasets meet the necessary benchmarks for accuracy, completeness, and relevance. This improves AI model performance and mitigates risks. 🔸 Ethical Guardrails: Data governance enables organizations to monitor and address ethical concerns by embedding accountability measures within AIMS, ensuring datasets do not inadvertently harm or discriminate. ➡️ The Path Forward: The Data Governance Culture Implementing data governance within an AIMS requires both technical measures and a cultural shift: 🔸 Leadership Buy-In: Leaders must view data governance as an enabler of AI excellence, not a compliance burden. 🔸 Cross-Functional Collaboration: Data governance spans legal, technical, and ethical domains, necessitating collaboration across teams.

𝐖𝐡𝐚𝐭 𝐝𝐨 𝐲𝐨𝐮 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐛𝐲 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞? Often, we've come across different versions of Governance - some see it as security and access, while some see it as management, and most often, granular factors of availability and quality like timeliness, native access, correctness, and discoverability are completely sidelined in governance conversations. 𝐔𝐥𝐭𝐢𝐦𝐚𝐭𝐞𝐥𝐲, 𝐠𝐨𝐨𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐛𝐨𝐢𝐥𝐬 𝐝𝐨𝐰𝐧 𝐭𝐨 𝐡𝐚𝐩𝐩𝐲 𝐝𝐚𝐭𝐚 𝐜𝐢𝐭𝐢𝐳𝐞𝐧𝐬 𝐰𝐡𝐞𝐫𝐞 𝐠𝐨𝐯𝐞𝐫𝐧𝐨𝐫𝐬 𝐞𝐧𝐬𝐮𝐫𝐞: 𝐀𝐫𝐞 𝐦𝐲 𝐝𝐚𝐭𝐚 𝐜𝐢𝐭𝐢𝐳𝐞𝐧𝐬 𝐚𝐛𝐥𝐞 𝐭𝐨 𝐮𝐬𝐞 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐥𝐨𝐧𝐠 𝐭𝐡𝐞 𝐬𝐞𝐭 𝐠𝐮𝐢𝐝𝐞𝐥𝐢𝐧𝐞𝐬? 🎭 Governing data citizens and territories is very close to governing actual human ecosystems, as both involve people, processes, and resources. This strong analogy or bridge to actual human ecosystems should never be forgotten when drafting governance strategies. And keeping citizens happy involves much more than just security and access protocols. We understand Governance as a huge Umbrella term instead of simply a capability that one tool or integration could provide. 🔐 Security: All things policies, compliances, and SLOs 🔐 Access: User-based access, as well as less considered elements like 1. Addressability: Are data citizens able to easily address assets and use them? 2. Understandability: Without understanding the data, you cannot use (access) 3. Similarly, lot of other elements fall under addressability, like 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 of the data, 𝐧𝐚𝐭𝐢𝐯𝐞 𝐚𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲, and 𝐢𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲. The data citizen's experience is also the responsibility of good governance, and thus, it involves the pillars of data quality (so naturally, data observability) and data availability come into the picture. 𝐓𝐡𝐞𝐬𝐞 𝐟𝐚𝐜𝐭𝐨𝐫𝐬 𝐞𝐧𝐬𝐮𝐫𝐞 𝐭𝐡𝐚𝐭 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐬𝐞𝐜𝐮𝐫𝐞 𝐟𝐨𝐫 𝐮𝐬𝐞 𝐚𝐧𝐝 𝐚𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐥𝐞 𝐛𝐲 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐚𝐠𝐞𝐧𝐭𝐬, 𝐛𝐮𝐭 𝐚𝐥𝐬𝐨 𝐢𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐔𝐒𝐀𝐁𝐋𝐄 𝐚𝐧𝐝 𝐞𝐯𝐞𝐧 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐚𝐭, 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐟𝐨𝐫 𝐮𝐬𝐞. 🧬 𝐈𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠𝐥𝐲, 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞, 𝐢𝐟 𝐲𝐨𝐮 𝐫𝐞𝐚𝐥𝐥𝐲 𝐬𝐭𝐚𝐫𝐭 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐨𝐧𝐚𝐥𝐢𝐭𝐢𝐞𝐬, 𝐢𝐬 𝐚 𝐬𝐮𝐛𝐬𝐞𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲. The Data Product Strategy comes with an additional business-purpose alignment layer, and the entire construct helps satisfy governance building blocks such as native accessibility, discoverability, security protocols, interoperability, and so on. Given we deploy data products as a core business, our stress on good governance and its understanding is extremely critical since it forms a huge subset of the data product strategy. Have you considered how Governance strategies and Data Product initiatives at your org could bloom into a powerful intersection? #datagovernance #datastrategy #datamanagement

There is no AI without data. 👉 What steps are you taking to ensure data governance of your AI initiatives?  Secured and governed data is a cornerstone in building a foundation of trustworthy AI. Navigating the complexities of data security and governance, especially with sensitive and unstructured data, is crucial for any organization aiming to leverage AI effectively.     One company leading the way in data governance is Cummins Inc. In business for more than a century, Cummins designs, manufactures, and distributes power generation products for a range of industries. It operates in more than 180 countries, each with different regulations and requirements for information governance. Cummins’ 75,000 employees generate large volumes of data daily, much of it highly sensitive, including human resources files, customer data, and intellectual property on product designs.     Some data must be retained for the life of a product, which could span decades. Most of the data is unstructured and created in its Microsoft 365 environment. Because it doesn’t conform to traditional data standards, this type of data is difficult to classify and manage.     With exponential data growth, the need for stringent compliance, and to prepare for AI adoption, Cummins turned to Microsoft Purview Information Protection and Data Lifecycle Management to centralize and update its information governance across disparate systems.     By automating data classification and managing document lifecycles, Cummins not only ensured compliance and security but paved the way for safe AI adoption. Their journey underscores the importance of robust data security governance in driving innovation and maintaining trust in the AI era.     Read the full story for the details on Cummins’ data governance approach: https://lnkd.in/gBuj5Ggb     And if you’re not familiar with Microsoft Purview, please check it out. It unifies data security, governance, compliance, and privacy to help you understand, assess, and take actions – which is essential in the era of AI with regulatory obligations of emerging laws like the EU AI Act.     https://lnkd.in/gF4YWavN     #MicrosoftPurview #DataGovernance  #AIRegulation    Earl Newsome Herain Oberoi