How to Integrate Data Governance in Transformations

Too many teams accept data chaos as normal. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North take a different path - eliminating silos, reducing wasted effort, and unlocking real business value. Here’s the playbook they’ve used to break down silos and build a scalable data strategy: 1️⃣ Empower domain teams - but with a strong foundation. A central data group ensures governance while teams take ownership of their data. 2️⃣ Create a clear governance structure. When ownership, documentation, and accountability are defined, teams stop duplicating work. 3️⃣ Standardize data practices. Naming conventions, documentation, and validation eliminate confusion and prevent teams from second-guessing reports. 4️⃣ Build a unified discovery layer. A single “Google for your data” ensures teams can find, understand, and use the right datasets instantly. 5️⃣ Automate governance. Policies aren’t just guidelines - they’re enforced in real-time, reducing manual effort and ensuring compliance at scale. 6️⃣ Integrate tools and workflows. When governance, discovery, and collaboration work together, data flows instead of getting stuck in silos. We’ve seen this shift transform how teams work with data - eliminating friction, increasing trust, and making data truly operational. So if your team still spends more time searching for data than analyzing it, what’s stopping you from changing that?

🚨 #DataGovernance is too often fragmented. Different tools, inconsistent policies, poor user adoption, and lots of wasted effort. I loved this article by my colleagues Abhinav Batra, Laura Montealegre, and Shreekant Agrawal. It lays out a clear, practical, and truly integrated approach to solving that. Their message is to treat governance not as a patchwork of tools, but as a unified system: ✅ Make the enterprise data catalog the center of gravity ✅ Use the same metastore and open formats across platforms ✅ Connect DQ, access, observability, lineage, and business rules ✅ Let automation and AI drive syncing, tagging, and policy enforcement ✅ Focus on governance by design, not as an afterthought 👉 To highlight one specific piece from the article, see the attached image. This diagram shows a reference architecture for unified data governance. Instead of scattering responsibilities across isolated tools, it organizes them into a connected system: > The enterprise data catalog (e.g., Alation, Collibra, Atlan) is the hub, supporting data discovery, rule-setting, and access management. > A shared technical metastore (like Amazon Web Services (AWS) Glue or Unity Catalog) feeds it metadata, and ensures all platforms can reference the same table definitions, using open formats like Delta or Parquet, reducing duplication and keeping the catalog in sync with real-time schema updates. > Data quality, observability, and business rules are wired into the governance fabric as connected flows. Observability tools monitor #data freshness, pipeline health, and volume anomalies, pushing alerts and usage signals into the catalog in real-time, empowering users to assess trust and take action in one place. This is what modern data governance could look like... connected, contextual, and designed with the end-user in mind. 📎 I’ll drop the link to the full article in the comments. It’s well worth the read. #MetadataManagement #DataQuality #DataCatalog #AI ZS

🔓 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.

🚨 #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 )

Many organizations are adopting a "shift-left" mindset when it comes to data governance. So what exactly does this mean? "Shift left" refers to a proactive approach where governance practices are integrated earlier in the data lifecycle, typically starting from the data creation or acquisition phase and moving towards the data consumption and analysis phases. (While this is the latest branding, I can't help but think of Matthew Roche's Maxim on data transformation already covering the general concept 🤷♂️ ). Applying the shift-left approach involves bringing governance closer to the data source and promoting a culture of responsibility and awareness across the organization. Here are some of the key aspects of a shift-left approach: 1️⃣ Early Integration of Governance:   - Instead of addressing data governance concerns only after data has been collected or analyzed, the shift-left approach involves integrating governance practices at the inception of data-related activities. This helps identify and address issues early in the data lifecycle. 2️⃣ Embracing Data Producers:   - Data producers (some consider data engineers Producers, some consider Producers the business users or systems that actually generate operational data) are brought into the analytical data world to help show the downstream impacts of their work. This improves accountability and ownership upstream. 3️⃣ Data Quality at the Source:   - Emphasis is placed on ensuring data quality at the source. By addressing data quality issues early in the process, organizations can avoid downstream problems that may arise if poor-quality data propagates through various stages. 4️⃣ Increased Collaboration:   - Shift left encourages collaboration between different teams involved in the data lifecycle. Data governance becomes a shared responsibility among data engineers, data scientists, data stewards, and other relevant stakeholders, fostering a collaborative and cross-functional approach. 5️⃣ Automated Governance Controls:   - Automation is leveraged to embed governance controls directly into data pipelines and workflows. This can include automated checks for compliance, data quality, and security, reducing the need for manual intervention and ensuring consistent adherence to policies. By embracing the shift-left concept, organizations can build a more resilient and proactive data governance framework. This approach aligns with the broader trend of integrating governance into the mesh/fabric of data management practices, ensuring that governance is not an afterthought but an integral part of the entire data lifecycle. #datagovernance #shiftleft #activemetadata #data #analytics #datamesh #ai #genai Atlan