SogetiLabs’ Post

🚀 The future of data stewardship with AI agents In the third article of our four-part series, Maria C Villar, Mike Alvarez, Beth Hiatt, and Christine Legner examine how the Digital Data Steward (DDS), powered by AI Agents, augments four critical areas of data governance: ✅ Data Quality ✅ Metadata Management ✅ Master Data ✅ Data Retention From anomaly detection to metadata orchestration and retention compliance, discover how AI agents are shifting from simple automation to cross-agent orchestration that predicts, alerts, corrects — and empowers human stewards to focus on higher-value work. 👉 Read the full article here: https://hubs.ly/Q03G8J1p0

AI Forward Data Platforms: The Future of Data Platforms The enterprise data landscape stands at an inflection point. As artificial intelligence becomes central to business operations, organizations are grappling with the reality that their siloed, fragmented data architectures are fundamentally incompatible with the unified, accessible data infrastructure that AI demands. The path forward requires dismantling technical barriers while maintaining essential governance controls — a delicate balance that will determine which organizations thrive in the AI-driven future. Read more here: https://lnkd.in/gdrQATKm

Almost all of us are talking about AI right now but Database Trends and Applications is talking about Quest Software and it's worth a read. Quest just launched its Data Management Platform. The real point goes way beyond the branding, it’s whether companies finally treat data management as part of AI instead of a back-office chore. 👉 Worth a read: https://lnkd.in/gD7dZhVB What do you think? Are more execs connecting AI success back to data architecture yet? #AI #DataManagement #QuestSoftware #TrustedData #DigitalTransformation

Thoughts from our CDO panel at Data Management Summit New York: -We're still in the hype cycle - new hires are asking about the GenAI strategy -ROI might not be the biggest question - 90% of business use cases have no ROI - it's like tryig to explain the ROI for Internet back in the early 90s. -be ready to "fail fast" - speed of evolution / adoption -Talk about AI-Ready data - quality, observability, lineage - AI Strategy == How are you supporting business objectives -Building training sets with structured & unstructured data Unstructured data governance: -Sharepoint sites have suddenly become valuable - 70% of enterprise knowledge is in unstructured data - vendors are trending this way -Semantic model overlays - using GenAI to extract structure from unstructured data with human oversight -Holding models to a standard higher than human error rate -how do you integrate this derived structured data int the process? Data governance frameworks haven't caught up with structured data - e.g. PII data hidden in a document - Using GenAI to automatically tag & classify unstructured data -Firms that successfully implement unstructured data governance will lead Integrate banks policies and procedures - allow business user to ask a single question - not dealing with multiple tools -Datamesh/Datafabric - centralized tech strategy - single comprehensive data catalogue - decentralized execution Managing risks of misclassified data - Purge if not required for regulation - Accountability for data use -If not sure about data quality - test, test -Quality challenge at-scale - velocity and timing requirements for large-scale AI/ML -Data and AI are inextricably linked - falling under the same management umbrella -Data quality management and accountability must be distributed Cloud brings improved immunity to scale challenges - legacy stacks limit and restrict -People-first is key to adoption Next 6-12 months: Pushing "Data Product" Intersection of AI with Diplomacy & Trade - meeting regulatory requirements Cloud journey from local on-prem data warehouses Integration across the enterprise at scale #DMSNYC #datamanagement #data #AI #CDO #dataquality #datamesh #datagovernance #datafabric Peggy tsAI Jean-Christophe Lionti Vanessa Jones-Nyoni CJ Jaskoll Andrew Foster, CFA

“Trusted data is table stakes. It’s the foundation for every strategic data solution that our customers have built. But today, trust alone isn’t enough." Introducing the Semarchy Data Platform: helping enterprises accelerate the delivery of data products, reduce risk in AI initiatives, and unlock greater value from their data investments. 📢 Read the release in our newsroom and register to join our launch event at 11am ET today https://lnkd.in/ea3KxhcX

The role of the Chief Data Officer has never been more critical — or more complex. For years, CDOs were measured on governance fundamentals: catalog coverage, data quality, and compliance reporting. These were the foundations of trust. But the rise of GenAI has shifted the terrain. Practically overnight, every employee has become a data consumer. The scale of demand is unlike anything we’ve seen before. And this is where old models break down. Ticket-based approvals that take weeks? They don’t work when thousands of employees — and AI agents — are requesting access at scale. Governance teams drown, business slows, and innovation stalls. That’s why data provisioning is emerging as the CDO’s new mandate. Provisioning is where governance meets business velocity. It’s about enabling the right people to get the right data, securely and quickly, without friction. And it’s measurable. 𝐍𝐞𝐰 𝐊𝐏𝐈𝐬 𝐚𝐫𝐞 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐭𝐨 𝐝𝐞𝐟𝐢𝐧𝐞 𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐂𝐃𝐎 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: ⏱️ Average time from request to access (weeks → hours) 📈 Percentage of requests handled automatically through policy or AI 🔒 Share of sensitive requests requiring exception handling 👥 Proportion of the workforce actively provisioned with governed data These aren’t just operational stats — they’re indicators of how effectively an enterprise is using data to make decisions (I am spending time writing a data provisioning guide for Data Governance Book of Knowledge - coming soon! -- and I plan to get it integrated into conversational AI). At Immuta, we’ve reimagined how to make this possible. By meeting data consumers in the tools they already use, routing approvals intelligently, and leveraging AI to cut through bottlenecks, we help CDOs transform provisioning from a pain point into a competitive advantage. Because here’s the truth: the future of data doesn’t belong to those who store it, secure it, or even catalog it. 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐛𝐞𝐥𝐨𝐧𝐠𝐬 𝐭𝐨 𝐭𝐡𝐨𝐬𝐞 𝐰𝐡𝐨 𝐜𝐚𝐧 𝐩𝐫𝐨𝐯𝐢𝐬𝐢𝐨𝐧 𝐢𝐭. And it’s the CDO who sets that future in motion. Read more here --> https://lnkd.in/eSy2C6d9

Real Data Governance doesn’t start with a model, but with a scar. Fix one wound, prove the value, and then scale out. Traditional governance frameworks like DAMA-DMBOK or COBIT often collapse under their own weight bureaucratic, slow, and disconnected from business outcomes. The result? low adoption, workarounds, and mounting data debt. The scar-driven model flips the script. By anchoring governance in real scars high visibility failures already on the executive dashboard it transforms theory into action. Value is created in 3–6 months, executive sponsorship emerges organically, and adoption climbs because business units pull governance in rather than resist it. A Conditional “Go” Adopt scar-driven governance as the execution model, but reinforce it with an Architectural Governance Overlay. This lean layer preserves long-term principles cloud-first, API-centric, mesh ready so tactical fixes don’t become strategic debt. 🔥 What makes this approach different? - Failures like a broken forecast or biased AI model fund governance you’ve been asking for. - Every scar becomes an “antibody”—a control or automation that strengthens resilience. - Scars propagate across pipelines, MLOps, orchestration, and apps, forcing integrated solutions. - GDPR, NIS2, ISO 27001, and AI Act controls forged in incidents not just on paper. - Controlled failures (chaos engineering for data) proactively harden future-critical systems. 🎯 Why this matters for CDOs For Chief Data Officers, scar-driven governance is more than incident responseit’s a strategic weapon: - It reframes governance as value creation instead of cost or compliance overhead. - It secures executive sponsorship without selling—because scars are already business priorities. - It creates data products people trust, fueling AI, analytics, and digital transformation. - It proves ROI with measurable impact (3:1+ within 18 months). And it positions the CDO not as a gatekeeper, but as the architect of resilience and innovation. Conclusion Scar-driven governance turns painful failures into power plays. For executives it’s about ROI and resilience. For data pros it’s about traction and tooling. And for CDOs, it’s the chance to lead with impact to transform governance from bureaucracy into competitive advantage. 👉 Read the full analysis here: https://lnkd.in/eF6VhdsR

𝐓𝐡𝐞 𝟐𝟎𝟐𝟓 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐃𝐚𝐭𝐚 𝐂𝐚𝐭𝐚𝐥𝐨𝐠 & 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Most enterprises struggle because data is everywhere, but context is missing. That’s why data catalog and metadata management need to work together. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐠𝐮𝐢𝐝𝐞 𝐜𝐨𝐯𝐞𝐫𝐬: Core capabilities you should expect: automated harvesting, business glossary integration, column/job/dashboard-level lineage, and sensitive data classification. KPIs to measure success: time-to-insight, incident resolution speed, data adoption, and % of decisions made using certified assets. AI/LLM readiness – why metadata (definitions, lineage, freshness, sensitivity) is the foundation for trusted AI outcomes. 𝟗𝟎-𝐝𝐚𝐲 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐛𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐭𝐨 𝐫𝐨𝐥𝐥 𝐭𝐡𝐢𝐬 𝐨𝐮𝐭 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲. 📌 Read the full guide here: https://lnkd.in/gtVtj3EK At Decube, we unify catalog + metadata management with data lineage and quality in a single platform—so you don’t just manage data, you build data trust.

Enterprise buyers are rethinking their data foundations as AI adoption accelerates. The right data platform isn’t just about storage — it’s about scale, governance, and enabling advanced analytics to unlock business value. ISG’s 2025 Buyers Guide for Data Platforms provides a clear view of leading providers, key differentiators, and the innovations shaping tomorrow’s enterprise data strategies. Whether you’re modernizing legacy systems or seeking a cloud-native solution, this guide helps decision-makers cut through the noise and identify the best fit for their digital roadmap. 📖 Explore the guide here: https://bit.ly/48lRG7r #DataPlatforms #EnterpriseAI #DigitalTransformation

🚨 𝗧𝗵𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗽𝗶𝗲𝗰𝗲 𝗶𝗻 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗼𝘀𝗮𝗯𝗹𝗲 𝗖𝗗𝗣𝘀? 𝗗𝗮𝘁𝗮 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀. Most vendors think “plugging tools together” = composability. In reality, they’re just moving bad data faster, leaving data architects and engineers cleaning up the mess downstream. True composability starts with data readiness:   • Cleansing & deduplication   • Continuous identity resolution   • Enrichment & unification in real time That’s how you stop rework, avoid duct-tape fixes, and build systems that scale with AI and future channels. 👉 Redpoint’s Data Readiness Hub ensures data is always fit for purpose — so composability finally works as promised. https://hubs.li/Q03KYR1R0 #DataQuality #DataReadiness #composablecdp #AI