How AI boosts data governance and quality

Thoughts from our panel discussion on how to ensure high quality and trusted data for AI, at Data Management Summit New York: -Moving from reactive /rules based to pro-active -Apply AI across the DQ process - not necessarily replacing existing rues but augmenting - AI helps with proactive Managing Unstructured Data: -Growth & opportunity but the risks is higher -Historical tools are focused on structured data -Unstructured Quality KPIs - lineage is key -Ensure vendors meet formatting standards -we're still early in the Unstructured data quality journey -Raw document to JSON - enables applying traditional DQ rules Automating unstructured remediation at scale? -Agentic AI can flag - automate the fix - then test that the fix is valid - what's missing? First step - Structure the data - by transforming into JSON - then apply analytics -e.g. Take 50 records for manual assessment - apply AI and compare how it performed - Human in the Loop is always necessary Key org challenge for establishing the Ground truth: -Used to be 100% correct before data hit production - we're now probability based meaning 90% of the data is correct going into prod - creating greater oversight challenges so demands Probabilistic tools are great for probabilistic answers - not for 100% accuracy Using AI to streamline data management: -dealing with scale -identifying missing / incomplete data -detecting issues earlier in the pipeline A governance framework can fail if everyone interprets and executes differently - essentially creating multiple frameworks If governance slows you down, it's a cost center, if it's keeping you safe, it's an asset - Does a speed limit slow you down or keep you safe? Where AI should NOT be used: -Where bias in data -Don't use AI where you need a believable answer -Critical decision making Think of it as an excellent assistant Implementing new DQ monitoring and observability tools tops Priority actions Key takeaways: -Culture -Trust framework -Governance model - we have new tools but the good data management skillset is probably more important than ever - we can do it at scale and faster #DMSNYC #datamanagement #data #AI #GenAI #LLMs #dataquality #governance #unstructureddata BNY SMBC Group Datactics Informatica Context Analytics, Inc. (CA) Brian Greenberg Manal Alimari Eugene Coakley, OLY Amy Horowitz Joe Gits, CFA

The AI regulatory landscape is fundamentally transforming how organizations think about data management, and it's creating seismic shifts in the data governance and catalog market. 𝐓𝐡𝐞 𝐍𝐞𝐰 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐕𝐞𝐧𝐝𝐨𝐫𝐬: 𝐅𝐫𝐨𝐦 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐭𝐨 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧: Data lineage and provenance tracking have evolved from "nice-to-have" catalog features to regulatory necessities. Vendors are rapidly repositioning their solutions around compliance-first narratives. 𝐀𝐈-𝐑𝐞𝐚𝐝𝐲 𝐃𝐚𝐭𝐚 𝐚𝐬 𝐚 𝐌𝐚𝐫𝐤𝐞𝐭 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐲: The traditional data catalog is expanding into comprehensive AI governance platforms. Vendors are integrating bias detection, model transparency tools, and automated compliance reporting directly into their core offerings. 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐑𝐞𝐢𝐦𝐚𝐠𝐢𝐧𝐞𝐝: Data management vendors are shifting focus from simple discovery to end-to-end governance workflows that support explainable AI. The ability to trace data from source to AI outcome is becoming table stakes. 𝐌𝐚𝐫𝐤𝐞𝐭 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: 𝐂𝐨𝐧𝐬𝐨𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐞𝐬𝐬𝐮𝐫𝐞: Organizations want fewer vendors managing their compliance story. This is driving M&A activity as companies seek integrated platforms rather than point solutions. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Vendors are developing industry-specific compliance modules for financial services, healthcare, and other heavily regulated sectors where AI decisions carry high stakes. 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The definition of "good metadata" now includes AI model training data, algorithmic decision points, and bias testing results, expanding the traditional data catalog scope dramatically. 𝐓𝐡𝐞 𝐕𝐞𝐧𝐝𝐨𝐫 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Leading data management companies aren't just adding AI features, they're rebuilding their platforms around the concept of "trustworthy data supply chains" that can withstand regulatory scrutiny while enabling innovation. 𝐊𝐞𝐲 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧: As AI regulations tighten globally, which data management approach will emerge as the standard: comprehensive platforms or best-of-breed specialized tools? The companies that crack this code won't just survive the regulatory wave; they'll define the next generation of enterprise data architecture. Stijn (Stan) Christiaens Stephen Zisk Prabhat Mishra #datagovernance #datacatalog #AIgovernance #datamanagement #enterprisedata #regulatorycompliance #datalineage #dataquality

How can you unlock data value and build AI readiness without tech and cost overwhelm? Data consolidation is a key first step to reduce bloat and introduce efficiencies with more focus. That’s where a modern data platform comes in. It is a composable technology stack that allows organisations to collect and manage all their data from a single, well-governed environment. You get: 👉 Increased innovation capabilities thanks to built-in ML/AI features 👉 Reduced operational costs by eliminating existing redundancies 👉 Security and compliance uplift through centralised, consistent data governance ......and much, much more. At V2 AI, we have partnered with leading data platform providers so we can support our customers in accelerating business outcomes in a tech-agnostic manner. Read more about what data platforms are and how they are driving meaningful business outcomes across sectors through practical, real-world examples. https://lnkd.in/gAe67F7t Is your organisation using a data platform? How has it benefited the business? Let us know in the comments below!

Data Strategy ≠ IT Strategy. Data Strategy = Business Strategy. Stop scrolling if you still think enterprise data strategy is just about technology it’s not. Too many organizations mistake tools for transformation. But true data & AI strategy is about confidence at scale - confidence that every decision, every workflow, and every interaction is powered by trusted intelligence. The shift forward is clear: 1. From pipelines → products → reusable, governed, monetizable data assets. 2. From dashboards → agents → AI copilots embedded in business flows. 3. From compliance → confidence → governance as an innovation catalyst. 4. From IT projects → business strategy → outcomes driving integration, not the other way around. The old world of brittle ETL and siloed warehouses left teams waiting weeks sometimes months for answers. The new world demands real time, interoperable, business outcome driven ecosystems where generative and agentic AI are not experiments but daily decision makers. 👉 If you agree, tag a data professional who’s leading this shift.

🚀 Data Leaders Digest – Issue #7 is here! From AI-first platforms to Uber’s mind-blowing 150M reads/second cache, this edition dives into the strategies, frameworks, and innovations shaping the future of data. Inside this issue: 🔹 Building an AI-first platform strategy 🔹 Generative AI for data analytics 🔹 Data life cycle: stages, importance & best practices 🔹 Data & analytics governance – backbone of AI adoption 🔹 Data governance strategy 2025: modern frameworks 🔹 How Uber scales with stronger cache consistency guarantees Whether you’re building platforms, governing data, or scaling distributed systems, this digest brings you practical insights + real-world stories to stay ahead. 👉 Dive in & explore: https://lnkd.in/gcM_edAe #DataAnalytics #AI #DataGovernance #GenerativeAI #PlatformEngineering

You can’t scale AI unless you scale trust in the data behind it. As CIO highlights, developing a strong AI-ready data culture isn’t just about technology—it’s about mindset, ownership, and rigor. Here are five key practices organisations can adopt: Treat data as a product, not a byproduct — giving datasets clear ownership, defined lifecycles, and usability across teams.  Embed observability and traceability — know where data comes from, how it’s transformed, and how it’s used.  Bake governance into the foundation — policies around access, retention, classification, quality, etc., enforced not as an afterthought.  Make data literacy everyone’s job — across roles and levels, ensure people understand quality expectations, analytics basics, and how data impacts outcomes.  Integrate structured and unstructured data — bring together traditional systems data and the messy, rich sources like documents, images or emails so AI gets a fuller picture. #AIReady #DataCulture #DataGovernance #TrustInData #EnterpriseAI #DigitalTransformation https://lnkd.in/d6-kJn3G

🚀 New Blog Alert on Medium 🚀 “Data Governance Simplified: A Beginner’s Guide for 2025” In today’s data-driven world, organizations struggle with data silos, poor quality, and compliance challenges. Without a strong data governance framework, scaling AI and analytics becomes nearly impossible. At Segmetriq Analytics LLP, we break down the essentials of Data Governance in simple terms: ✅ Metadata Management ✅ Data Quality Rules ✅ Data Lineage ✅ Business Glossary ✅ AI & Agentic AI Integration 👉 Read the full beginner-friendly guide here: 🔗 - https://lnkd.in/dRYq4quB 📊 Because in 2025, businesses that govern data well will lead the digital race. #DataGovernance #DataStrategy #AIinBusiness #AgenticAI #DigitalTransformation #DataQuality #MetadataManagement #SegmetriqAnalytics

💡 Data Governance is no longer optional — it’s the backbone of data-driven decisions. I recently wrote a blog on “Data Governance Simplified”, where I break down: ✔ Metadata Management ✔ Data Quality Rules ✔ Lineage & Glossary ✔ AI-driven Governance 👉 Here’s the link: https://lnkd.in/dARSY4ic Curious to know — in your organizations, which area do you find most challenging right now? #DataGovernance #DataQuality #CDO

𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗱𝗲𝗮𝗱 - 𝗶𝘁 𝗷𝘂𝘀𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗸𝗻𝗼𝘄 𝗶𝘁 𝘆𝗲𝘁 Remember when data lakes were supposed to solve everything? Store all your data in one magical repository and analytics would flow like water downstream. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱: Your data lake became a data swamp. Studies show up to 80% of data lake initiatives fail to deliver expected business value. Poor governance turned your strategic investment into an expensive graveyard of unusable datasets. The fundamental problem isn't technology - it's centralization. When one team controls all data access, you create bottlenecks. When domain experts can't manage their own data products, quality suffers. When AI projects need clean, discoverable data, they find neither. 𝗧𝗵𝗲 𝗽𝗮𝘁𝗵 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝗶𝘀 𝗱𝗮𝘁𝗮 𝗺𝗲𝘀𝗵. Instead of centralizing everything, data mesh distributes ownership to domain teams. Each business unit becomes responsible for their own high-quality "data products" while maintaining universal standards for discoverability and compliance. Think of it this way: your data lake tried to be a central warehouse. Data mesh creates a network of specialized shops, each with expert owners who understand their customers' needs. 𝗙𝗼𝗿 𝗔𝗜 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻, 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴: • Domain teams ensure data quality because they own the outcomes • Self-service infrastructure accelerates innovation cycles • Built-in governance prevents compliance nightmares • Data becomes discoverable and trusted across the organization Before you embark on your next AI project, ask this critical question: 𝗜𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗲𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗼𝗿 𝗯𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀? Most enterprises discover their biggest AI blocker isn't algorithms or compute power - it's data that doesn't work when you need it to. The companies winning with AI aren't just buying better tools. They're fixing their foundations first. #datamesh, #datamaturity, #aireadiness Parallaxis

It is almost impossible to implement good AI-enable processes without the data layer to support it. It was always important for me to make sure the our data was: Complete, Available, and Trustworthy. This article expands on that hierarchy of maturity to add: Insightful, Decisive, and Autonomous (Self Actualizing). That last aspect was the AI-oriented layer of data. It will feel urgent to get to the top level of maturity immediately, as we are all apparently trying to use, to implement, or to perfect the AI within the workplace. But the data journey is not clean and easy, nor is it skippable. It takes work on the definitions, the governance, the architectures, and the usability in order to scale this data maturity. In order to be AI-ready, we need to tackle the data foundations AI will be built upon. Check out the original article: https://lnkd.in/ggaKiUA9