Rethinking Data Contracts for AI-Driven Systems

🔎 The semantic layer is more than theory—it's the key to making your AI and analytics efforts actually work at scale: https://bit.ly/3Gdm3lb In this on-demand session, discover how to: 🔷 Align architecture with business outcomes 🔷 Enable governed, AI-ready data access 🔷 Simplify your enterprise data stack CData Software #CData #SemanticLayer #DataVirtualization #AI #DataOps #DataGovernance #OnDemand

The AI Data Integration Gap In my previous post I argued MCP might evolve from plumbing into orchestration. But here's the deeper issue: Today's "agent memory" systems (Mem0, LangChain Memory, Zep) store AI-generated state: conversation history, embeddings, learned preferences. That's valuable — but it's only half the data universe. The other half is enterprise data: customer records, payments, tickets, transactions, analytics. And today, AI agents can't see it unless someone builds a one-off bridge. Your "intelligent" customer service agent may remember every conversation style but miss that the customer has three escalated tickets. That leaves us with two paths forward: * Path 1 — Protocol bridges (MCP or similar). Agents call each system directly through standard connectors: Jira MCP, Snowflake MCP, Postgres MCP. This is the microservices model applied to data: fast and composable, but still siloed. * Path 2 — Data unification. A shared layer abstracts and orchestrates across systems: deciding placement, semantics, access, governance. This could be an evolution of MCP — or it might emerge elsewhere in the stack. The choice is critical. Protocol bridges give agents reach, but leave organizations with fragmented governance. A unification layer provides context and control — but also consolidates power in the data stack. Which path do you think wins?

“We want to configure; not code. Every line of code we write is an admission of defeat.” One of many standout quotes during a super exec roundtable lunch for data leaders last week in Sydney, co-hosted by Coalesce.io and INTELLIGEN. This comment was from the CDO of a major Australian bank and is indicative of where the future lies in data management. Lots of honest, insightful conversation about the challenges and opportunities enterprise orgs are working through in the age of AI and exponential data growth. It’s clear the need for governance that is right-sized for today and scalable for tomorrow has never been more important. 💡🍴🍷 Erin Evans | Benjamin Portelli | Siobhan Kerrigan | Andrew Grochocki | Scott Shellien-Walker

Three industry leaders are converging on Zero Copy architectures and open standards to deliver immediate, contextual, and governed data for AI use cases that unlock new levels of business value and responsibility.

What if your unstructured docs could be converted to structured data automatically? At Unstructured, our mission has always been high-fidelity document transformation. Now we’re getting ready to take the next big step: structured extraction. If you go high level, there are only a few broad use cases with your unstructured data. Parsing makes docs readable. RAG makes them searchable. Structured extraction makes them actionable. That’s the promise of our new Structured Data Extractor. Organizations don’t just want documents parsed—they need specific fields, consistently and reliably, mapped to their own data models. Until now, that’s been brittle, costly, and hard to scale. Here’s what’s coming: - Define your own structured data model (JSON schema, database fields, etc.) - Automatically extract matching fields from unstructured documents - Outputs delivered with consistency and reliability And the impact goes far beyond clean outputs: - Power process automation by feeding extracted data straight into workflows or databases - Enable agentic flows where AI agents can act directly on structured fields - Unlock faster, more reliable pipelines for compliance, analytics, and decision-making No more brittle regexes or one-off scripts. Soon, your documents will yield clean, structured data aligned to your business needs—and ready to automate. #DocumentAI #DataExtraction #StructuredData #EnterpriseAI #Automation #AgenticAI #StructuredDataExtraction #UnstructuredData #WorkflowAutomation #IDP #Agents

🚀 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

𝗔𝗜-𝗖𝗮𝗽𝗮𝗯𝗹𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Most organisations talk about being AI-ready. Few are building the enterprise data architecture that actually makes AI trustworthy, scalable, and resilient. 𝗔𝗜-𝗰𝗮𝗽𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗼𝗿 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀. 𝗜𝘁 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀: ✅ Resilient by design – architectures that adapt to change without breaking. ✅ Automation – policy-as-code, metadata-driven controls, continuous monitoring. ✅ Evidence – regulatory and business assurance built into the data fabric. ✅ Scalability – models and data products that can expand globally. ✅ Trust – explainability, lineage, and embedded ethics. This is the foundation of my patent-pending Resilient by Design Data Governance (RbDDG) framework. Without these design principles, AI efforts remain tactical and fragile. The real question for data leaders is not “Are we AI-ready?” It is “Is our architecture AI-capable by design?” 𝗪𝗵𝗮𝘁 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗱𝗼 𝘆𝗼𝘂 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝗱𝗲𝗳𝗶𝗻𝗲 𝗮𝗻 𝗔𝗜-𝗰𝗮𝗽𝗮𝗯𝗹𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲? #DataArchitecture #AIReady #Resilience #Automation #DataGovernance #AI #NIDG

𝗔𝗜-𝗖𝗮𝗽𝗮𝗯𝗹𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Most organisations talk about being AI-ready. Few are building the enterprise data architecture that actually makes AI trustworthy, scalable, and resilient. 𝗔𝗜-𝗰𝗮𝗽𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗼𝗿 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀. 𝗜𝘁 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀: ✅ Resilient by design – architectures that adapt to change without breaking. ✅ Automation – policy-as-code, metadata-driven controls, continuous monitoring. ✅ Evidence – regulatory and business assurance built into the data fabric. ✅ Scalability – models and data products that can expand globally. ✅ Trust – explainability, lineage, and embedded ethics. This is the foundation of my patent-pending Resilient by Design Data Governance (RbDDG) framework. Without these design principles, AI efforts remain tactical and fragile. The real question for data leaders is not “Are we AI-ready?” It is “Is our architecture AI-capable by design?” 𝗪𝗵𝗮𝘁 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗱𝗼 𝘆𝗼𝘂 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝗱𝗲𝗳𝗶𝗻𝗲 𝗮𝗻 𝗔𝗜-𝗰𝗮𝗽𝗮𝗯𝗹𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲? #DataArchitecture #AIReady #Resilience #Automation #DataGovernance #AI #NIDG

Accurate data management constitutes the unseen foundation of infrastructure, which is essential for the smooth functioning of diverse sectors. Financial institutions, insurance companies, and regulatory authorities have all encountered the harsh reality that lapses in data governance compromise trust more rapidly than any system failure. When initiating transformation initiatives, five essential principles are paramount: - Emphasising hygiene - Ensuring validation - Conducting comprehensive testing - Enabling rollback capabilities - Implementing rigorous controls These pillars, fundamentally anchored in the principles of ownership and accountability, constitute the foundation of prudent decision-making processes. The omission of these fundamentals may result in the erosion of trust and the disintegration of dependable decision-making frameworks. #DataGovernance #AIReady #Resilience #Automation #NIDG Please read my other post below https://lnkd.in/eEpVmhky Call To Action To what extent has your organisation developed these five non-negotiables? Kindly share your experiences or challenges in the comments section.

𝗗𝗮𝘁𝗮 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗻𝗼𝗶𝘀𝗲. That's why metadata management isn't optional - it's the difference between data lakes and data swamps. Metadata answers the critical questions that make data usable: • 𝗪𝗵𝗼 𝗼𝘄𝗻𝘀 𝘁𝗵𝗶𝘀 𝗱𝗮𝘁𝗮𝘀𝗲𝘁? • 𝗪𝗵𝗲𝗻 𝘄𝗮𝘀 𝗶𝘁 𝗹𝗮𝘀𝘁 𝗿𝗲𝗳𝗿𝗲𝘀𝗵𝗲𝗱? • 𝗪𝗵𝗮𝘁 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝘂𝗹𝗲𝘀 𝗮𝗽𝗽𝗹𝘆? • 𝗖𝗮𝗻 𝘄𝗲 𝘁𝗿𝘂𝘀𝘁 𝗶𝘁 𝗳𝗼𝗿 𝗔𝗜 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴? • 𝗪𝗵𝗲𝗿𝗲 𝗱𝗶𝗱 𝗶𝘁 𝗰𝗼𝗺𝗲 𝗳𝗿𝗼𝗺 𝗼𝗿𝗶𝗴𝗶𝗻𝗮𝗹𝗹𝘆? Three essential types: 🔧 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 - Schemas, formats, source systems 💼 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 - Definitions, owners, policies ⏰ 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 - Freshness, quality, lineage Organizations with strong metadata management can implement AI governance frameworks faster than those starting from scratch. Reality check: How does your organization track metadata today - formal tools or tribal knowledge? #MetadataManagement #DataGovernance #AISafety #DataLineage #ResponsibleAI