How to build an AI-ready data lake in financial firms

↔️ Shift-Left vs Shift-Right in Data Governance: Who Owns Trust—and Who Builds It? ➡️ When Alation started the data catalog, it was all about engagement and adoption, shifting the work of data management to the right.  ⬅️ Then, with the modern data stack, data engineering teams pulled governance towards the left, moving quality controls, contracts, metadata, and validation upstream, closer to the source, with the premise that engineers could bake in trust from Day 1. ➡️ Today, LLMs and AI empower less-technical stewards & analysts to scale rapidly. Raluca Alexandru called it a Shift‑Right moment. ❓If you had to choose one, which one would you choose? 👈 Shift‑Left: Governance as code, embedded in pipelines, ensuring data quality before downstream risk. 👉Shift‑Right: Governance embedded in applications, revalidations at consumption, trust on demand—especially where AI-generated outputs are concerned. ❗ Why it matters: - Shift-Left gives you proactive guardrails, fewer data surprises, and more efficiency. - Shift-Right gives users embedded assurance when and where they need it—especially essential for LLM-driven workflows. Sanjeev Mohan and Guido De Simoni what are your thoughts on this one? #datagovernance #datatrust

🚦 𝘚𝘩𝘪𝘧𝘵-𝘓𝘦𝘧𝘵. 𝘚𝘩𝘪𝘧𝘵-𝘙𝘪𝘨𝘩𝘵. But… 𝘄𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲? This is not the first article I have seen on the shift-left vs. shift-right debate in data governance (and it certainly won’t be the last). The framing is valuable: should governance live in pipelines (shift-left) or in applications (shift-right)? Both matter: proactive guardrails upstream and embedded assurance downstream. But let’s be honest: neither will succeed without the people side of governance. 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻, 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗮𝗿𝗲 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲 𝘁𝗿𝘂𝘀𝘁 𝗿𝗲𝗮𝗹. Governance “as code” or “in the app” is powerful, yes, but without business stewards, analysts, and decision-makers seeing their role and being supported in it, it risks becoming just another technical layer. 👉 The real shift is not left or right. It is 𝘁𝗼𝘄𝗮𝗿𝗱𝘀 𝗽𝗲𝗼𝗽𝗹𝗲 𝗳𝗶𝗿𝘀𝘁. Because that is where trust is built, and where governance truly holds. So here is the question: 𝘪𝘧 𝘱𝘦𝘰𝘱𝘭𝘦 𝘢𝘳𝘦𝘯’𝘵 𝘪𝘯 𝘵𝘩𝘦 𝘱𝘪𝘤𝘵𝘶𝘳𝘦, 𝘪𝘴 𝘪𝘵 𝘳𝘦𝘢𝘭𝘭𝘺 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘢𝘵 𝘢𝘭𝘭?🤔 #DataGovernance #InformationGovernance #DataManagement #DataTrust #PeopleFirst #DataLeadership

🚀 Excited to share insights on the evolving world of data management this August 2025! As organizations increasingly rely on data to drive decisions, staying ahead of the curve is critical. This month, the spotlight is on AI-driven data governance and real-time data integration. Key trends shaping the landscape: 1️⃣ AI-Powered Data Governance: With regulations tightening and data volumes exploding, AI is transforming how we ensure compliance, security, and quality. Automated tools are now smarter, catching anomalies and ensuring trust in data like never before. 2️⃣ Real-Time Data Integration: Businesses are moving beyond batch processing to real-time pipelines, enabling faster insights and agile decision-making. Solutions like Apache Kafka and cloud-native platforms are leading the charge. 3️⃣ Data Fabric Adoption: The rise of data fabric architectures is simplifying complex ecosystems, unifying disparate data sources, and empowering seamless access across hybrid environments. As we navigate this dynamic space, the focus is clear: leverage automation, prioritize security, and embrace scalability. What's your take on these trends? How is your organization tackling modern data management challenges? Let's connect and discuss! 💬 #DataManagement #AI #DataGovernance #RealTimeData #DataFabric #TechTrends #PriyankSompura #Facilloc

5 actions to build an AI-ready data culture - CIO: Dun & Bradstreet monitors over 85 billion data quality observability points with homegrown tools DataShield and DataWatch. The former enforces ...

🔎 Let’s clear up the confusion around warehouses, lakes, and lakehouses. A real data warehouse isn’t just a fast database. It’s defined by: ✔️ Subject orientation (built around business concepts) ✔️ Integration (consistent keys and definitions) ✔️ Historic data persistence (true history, not overwrite) That’s the foundation for enterprise data integrity. Without it, AI and analytics run on shifting sand. Over time, the lines blurred. Analytical databases were called “warehouses.” Then came data lakes. Then lakehouses. All powerful technologies — but let’s not mistake them for the discipline of a true warehouse. 👉 A lakehouse on its own is not subject-oriented, integrated, historized persistence. The missing link? Data Vault modeling. By making your integration layer subject-oriented, deduped, and historized, you give the lakehouse the persistence and trustworthiness of a true warehouse. With this, AI and analytics can finally rely on it without compromise. 💡 The takeaway: Any modern platform can become reliable — but only when paired with a modeling approach like Data Vault. That’s when you unlock a real foundation for analytics and AI. At Sudar.io, we make adding Data Vault effortless.

The new game is unstructured data > structured data > pre-built analysis > action. The unstructured data portion before was very difficult and not too cost effective to parse and structure for effective use at scale. You typically had to be a large enterprise to even get useable structured data from the unstructured data. Even then, no guarantee you'd get positive ROI. Now the value line has shifted to the speed of action because the unstructured data can be parsed well enough with very little effort (AI with guardrails). This is fundamentally changing our business (since we've historically put more resources for clients toward the data side of things). Now the biggest levers are less data engineering and more process/strategy driven.

The traditional methods of entity resolution are rapidly being outpaced. As of Q3 2024, 75% of data leaders have shifted to semantic entity resolution to enhance accuracy and automation. This approach, leveraging language models, transforms the challenge of schema alignment, matching, and merging records through advanced representation learning. Instead of relying on simplistic string distance metrics or static rules, businesses are utilizing knowledge graph factories to fundamentally automate data clean-up processes. This shift is not just a trend but a necessity for maintaining data integrity and operational efficiency in an increasingly data-driven environment. The implications for executives are profound: adopting semantic entity resolution can significantly reduce operational friction, increase data accuracy, and foster more nuanced insights. Leading organizations are already observing a 30% improvement in data processing efficiency after transitioning to this methodology, signaling a crucial competitive edge. As you consider your own data strategies, how do you foresee the integration of semantic entity resolution impacting your data accuracy and operational efficiency? What steps might you take in the coming months to leverage this technology? Share your thoughts on how semantic technologies could reshape your data strategies! What are the specific challenges you've faced in implementing entity resolution? #SemanticEntityResolution,#DataAutomation,#KnowledgeGraphs,#DataIntegrity,#BusinessStrategy

𝐅𝐫𝐨𝐦 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 𝐭𝐨 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐌𝐞𝐬𝐡: 𝐇𝐨𝐰 𝐌𝐂𝐏 𝐢𝐬 𝐑𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞   Traditional data mesh architectures promise decentralized ownership with federated access, but in practice still require custom integration work for each data source.   MCP introduces a standardized protocol layer that abstracts away the underlying API differences between heterogeneous data products. Our implementation sits between MCP clients and our existing data products (TCGA, clinical trials, biomarkers, etc.), handling discovery, authentication, and data transformation through a common interface.   The technical shift is significant: instead of building point-to-point integrations, we're seeing emergence of what might be called an "agentic mesh" - where AI agents can computationally discover and query federated data sources without knowing their underlying schemas or APIs. This reduces the integration complexity from O(n²) to O(n) when connecting multiple data products.   Key architectural lessons (We are still learning) Here are some of the challenges we faced: - Connection pooling and caching become critical when serving multiple concurrent agent requests. - Data product adapters need to handle version differences gracefully. Streaming support is essential for large datasets that agents typically request.   The broader implication is that MCP may become the missing application layer for federated data architectures. Teams building data products should consider MCP compatibility as a first-class requirement, not an afterthought, as it determines whether their data can participate in automated cross-domain analysis workflows. Let us know your thoughts or if you want to try out our server (https://lnkd.in/ei8s4-tT)

How your ML projects might fall victim to elusive 'perfect data' - TechTalks: Ultimately, this approach treats data quality as a starting input, not an insurmountable blocker. Instead of asking if the data is perfect, the ...

🔥 After 8+ years of turning data chaos into business wins, here's what I've learned about data governance in 2025... I've seen it all: ↳ Cut healthcare claim rejections by 30% across 50K+ monthly claims at Synergen Health ↳ Built retail AI quality controls at Trax that caught failed predictions before they hit production ↳ Spotted $2M+ in hidden savings in financial data at Veradigm ↳ Transformed scattered spreadsheets into actionable insights at Jeneva The secret sauce? Data governance. Not the boring, bureaucratic kind. The kind that: ✅ Turns 85% data accuracy into 98% reliability ✅ Cuts executive reporting time from hours to minutes ✅ Prevents costly compliance failures before they happen ✅ Makes your dashboards trustworthy enough for 150+ daily users With AI regulations tightening and data complexity exploding, 2025 is the year governance separates winners from losers. Just published my deep dive on "Data Governance in 2025: Keeping Your Data Clean and Compliant" — sharing the real-world playbook I've used across healthcare → retail AI → finance. 📖 What's your biggest data governance challenge right now? Drop it below 👇 #DataGovernance #DataAnalytics #DataQuality #AI #DataStrategy https://lnkd.in/g2v6pvXd