Future of Trusted Data Analytics

Pointing Claude at your warehouse is a trust problem you've deferred by two weeks. Week one: magic. Everyone gets answers in seconds. Week two: two execs ask the same question, get two different numbers. Claude joins the wrong tables and answers confidently anyway. Someone ships a decision off a metric fed by a pipeline that broke three days ago. You haven't accelerated your analytics function. You've replaced a slow, accurate one with a fast, unreliable one. I've seen this play out enough to know: the failure is always architecture. Trusted self-serve AI analytics is achievable. But it requires three things most teams skip: - Route to curated dashboards first. Most "analytics questions" are discovery questions. The right answer is a link, not a query. - Anchor ad-hoc analysis to a semantic layer. "Active user" should mean the same thing every time, full stop. - Run live data health checks before presenting results. A seasoned analyst doesn't just run the query. They check if the pipeline ran, if there's an open incident on that table. Claude doesn't have that institutional memory by default. Give it that capability and it starts telling you when not to trust the answer. That's the gap between an AI analyst demo and one you trust in production. We built this at Monte Carlo. It covers 100% of internal data inquiries across our 150-person company. Our analysts don't spend their days fielding ad-hoc SQL requests. They do the strategic work. The real metric is trust: how many answers can a decision-maker act on without picking up the phone? Lior Gavish shares more about how we did it. Link in comments 👇 #dataquality #aiobservability

Enterprise leaders must update their 2026-2027 AI strategies. This year brings major changes: AI agents and automation are outpacing governance, sharply increasing risk. "Sticking an AI on it" is insufficient; leaders must redesign how we augment human decision making (humans-in-the-loop) and automate at scale (human-on-the-loop). Governance practices and platforms are essential to avoid costly mistakes. Gartner predicts the by 2027, 25% of ungoverned decisions using large language models (LLMs) will cause financial or reputational loss due to human biases, insufficient critical thinking, and AI sycophancy. This stems from users' over-trusting confident-sounding LLM outputs. Leaders must govern decisions more carefully, as automation often scales the risks just as fast as it scales the gains! Most clients I speak with still focus on human decision makers being “data‑driven” by dashboards, analytics, and data, etc. However, this fails to overcome human biases, does not prevent "AI sycophancy," nor does it make major decisions transparent and accountable (the black box problem). As #AIAgents increasingly automate part of our businesses, the data-driven dogma (dashboard watching humans) really breaks down. Gartner research shows clients evolving from “data‑driven” to “decision‑centric,” where the business decision is modeled, monitored, and governed - that is why we are hearing much more about decision intelligence in 2026. The Magic Quadrant for Decision Intelligence Platforms offers leaders three key benefits: 1️⃣ Clarity on essential technical capabilities like decision modeling, monitoring, and governance. 2️⃣ A framework for vendor evaluation based on combining AI agents, data, analytics, ML, knowledge graphs, and context for strategic and operational decisions. 3️⃣ Evidence that a decision-centric approaches deliver results; explicitly modeled decisions will be five times more trusted and 80% faster than ungoverned ones. For instance, a client (major bank) leveraged this research to secure their budget, adopt a decision-centric vision, transform a large team into a DI division, and select a platform for governing regulated decisions - boosting their influence and providing a safer path to scale AI. Using LLMs for decision making without governance is an enterprise risk. Becoming decision-centric is the safest way to connect AI to enterprise data. Q. Are you still data-driven, or adopting a #DecisionCentric vision to govern AI-enabled decisions? If "data-driven" is where you are at, this Magic Quadrant shows how connecting data-to-decisions explains the deeper value of data. If you're already exploring #DecisionIntelligence, then let's explore it together. Which capabilities and platforms are on your 2026 roadmap. Now you know why I say that in 2026, “D is for Decisions”. Clients are reading Gartner's new Magic Quadrant for Decision Intelligence Platforms 🔗 https://lnkd.in/eMq4gynh (requires log in)

We’re all building amazing data products and driving AI, but deep down, we know the fragility. Here’s a stat that keeps me up at night: industry reports suggest that up to 45% of data-driven projects fail due to poor data quality or lack of trust. 🤯 And that trust issue usually boils down to one thing: lineage. We need to move past "I think this is correct" to "I know this is correct." #DataTrust 🤦♀️ The Headache: Why Table-Level Lineage Fails Us The real problem for data professionals like us is the silos of certainty: ❌ Compliance Nightmare: How can I prove that the social_security_number column was properly masked before it landed in the analytics table? Table lineage can’t tell you. 😨 Impact Blindness: My colleague is changing the calculation for the monthly_revenue column in our staging environment. Do I have to message everyone in the company to check their dashboards? Without granular visibility, the answer is often yes, leading to burnout and fear of change. ✨ The Solution: Lineage with Laser Focus 👉 Google Cloud has taken the guesswork out of data transformations. Instead of tracking the entire table (the 'potato sack'), we are now tracing the specific column (the 'single potato chip') as it moves and transforms across BigQuery. 👉 What does this mean for you? You get a simple, visual, interactive graph. You click the column, and the map instantly shows every single upstream source and every single downstream consumer. No more SQL spelunking required! 🗺️ 📈 The Benefits: From Fear to Confidence 👉 Moving from object-level to column-level isn't just a technical upgrade; it's a massive shift in how we handle our jobs and drive business value. 👉 For AI & ML Teams: You can finally audit that your critical training features (e.g., a specific fraud_score column) originate from the trusted, audited system and not a questionable source. This is the bedrock of responsible, non-biased AI. 🤖 #ResponsibleAI 👉 For Compliance & Audit: You can instantly generate proof of compliance, tracing PII from ingestion to deletion/masking with surgical precision. This is what makes auditors happy and helps you sleep better at night. 😴 #Governance 👉 For Incident Response: When a metric is wrong, you can trace the column back to the exact transformation step that introduced the error in minutes, not days. Faster root cause analysis = less downtime. ⏰ 👉 The era of trusting the whole table is over. If we want to build resilient, scalable data platforms and leverage the power of AI, we need certainty down to the smallest detail. 👉 Dataplex Column-Level Lineage gives us that certainty in BigQuery. It’s an empowering tool that frees us from manual audits and surprise outages. Go check it out—and enjoy the newfound confidence in your data pipelines! 🎉 What's the one column you are tracing first? Let me know in the comments! 👇 #DataEngineering #BigQuery #Dataplex #DataAnalytics #CloudData #Lineage

Enterprise data is undergoing a quiet shift from merely gathering everything into one massive repository to connecting, governing, and preparing data for impactful real-world AI applications. - Centralised architectures are being replaced by distributed, domain-led approaches, enhancing accessibility and manageability for teams while maintaining security. - The next critical step involves building trust and value through robust data governance and clear lineage, beyond just technology stacks. - AI-ready foundations demand more than just clean data; they require clear ownership, context, and compliance by design. As we approach 2026, successful organizations will prioritise how data flows, is governed, and contributes to business outcomes. There will be less focus on data in terms of simply where it resides. Curious about how to future-proof your data strategy for AI? Let's connect and explore the next steps for your enterprise data.

Why next-generation AI analytics may need a blockchain trust layer? AI analytics is moving from dashboards to decisions. As that happens, trust becomes more important than raw performance. Many organisations already struggle with questions like: Where did this data come from? Which model produced this result? Can we prove this decision was fair, unchanged, and compliant? Industry research increasingly points to trust, provenance, and auditability as the biggest blockers to scaling AI analytics, especially in regulated sectors like public services, finance, and healthcare. A blockchain trust layer can help by: 🔐 Providing immutable records of data lineage and model versions 🧾 Creating tamper-proof audit trails for analytical decisions 🤝 Enabling cross-organisation analytics without sharing raw data 📜 Supporting compliance and explainability by design This is not about running AI on-chain or crypto hype. The compute stays off-chain. Blockchain acts as a trust backbone for governance, accountability, and verification. As AI analytics becomes a system of record for decision-making, trust may be the defining feature of next-generation platforms.

The paradigm shift in analytics: How the analyst’s role is changing >>   We’re witnessing one of the biggest shifts in analytics in decades. Analysts are no longer defined by how fast they can write SQL or build dashboards, but by how well they can orchestrate analytical reasoning.   Their new craft isn’t about producing reports. It’s about guiding AI through context and validation, shaping a continuous process of exploration where human reasoning and machine intelligence learn from each other. Some call it augmented analytics. In truth, it’s the evolution of analytical thinking itself.   Instead of chasing anomalies or repeating manual analyses, analysts now guide AI through context, semantics, and reasoning, transforming raw computation into trustworthy insight. They’re becoming architects of how organizations think with data, not just report on it.   The change is fast and profound. By 2027, Gartner predicts that 75% of new analytics content will be powered by GenAI. That doesn’t replace the analyst. It elevates them from data executors to conductors of intelligence. https://lnkd.in/dHJPcfKe The future analyst blends human curiosity with machine precision, bringing meaning, accuracy, and reasoning back to the center of decision-making.

Last week, I had an insightful dinner discussion with industry peers and technology leaders. 𝗔 𝗸𝗲𝘆 𝘁𝗵𝗲𝗺𝗲 𝗲𝗺𝗲𝗿𝗴𝗲𝗱 𝗱𝘂𝗿𝗶𝗻𝗴 𝗼𝘂𝗿 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻: organizations are at a significant turning point in how they interact with data.  Traditionally, decision-making has relied on dashboards, reports, and business intelligence (BI) layers. There is now a belief that traditional dashboards may become outdated. The future of data interaction is simple in concept but challenging to implement: 𝘁𝗵𝗲 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗲 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗿𝗲𝗰𝗲𝗶𝘃𝗲 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲, 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲.  Here are five key takeaways from the discussion:  𝟭. 𝗜𝗻𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗶𝗻𝘁 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Static analytics, which focus solely on historical data, are no longer sufficient. Modern decision-making demands dynamic, contextual, and interactive insights beyond what traditional BI provides.  𝟮. 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗖𝗿𝘂𝗰𝗶𝗮𝗹: Although natural language interfaces provide quick responses, they are often hampered by fragmented data, inconsistent definitions, and a lack of context. This can result in confident yet inaccurate answers, highlighting that accuracy and trustworthiness are as important as speed.  𝟯. 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗚𝘂𝗶𝗱𝗲𝗱 𝗣𝗿𝗼𝗺𝗽𝘁𝘀: The traditional dashboard may soon be replaced by guided prompts. Unlike standard conversational AI, these prompts integrate business logic, maintain context, and help users formulate relevant questions.   𝟰. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀: The challenge lies not in the technology itself, but in the operating model. While having a visionary outlook is essential, executing that vision requires clear ownership of definitions, maintaining context, and achieving consistency across the enterprise. Otherwise, AI may merely amplify existing inefficiencies.  𝟱. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹: AI failures often result from a lack of context, not a lack of intelligence. This shift in understanding will reshape data architectures and roles. leading to the growth of governed, agent-based systems that operate across various domains. In summary, the discussion highlighted that the challenge extends beyond merely integrating AI with analytics. It involves fundamentally redefining how organizations formulate questions, access data, and make decisions. #AI #DataStrategy #Analytics #OperatingModel #GenerativeAI #DigitalTransformation

Everyone at Davos is talking about AI. Cool. But the bigger story in 2026 is uglier (and more useful): mis/disinformation and societal polarization are now top-tier global risks. WEF is basically saying this out loud: information disorder is now a board-level risk. Which means one thing for leaders: Trust is no longer a brand problem. Trust is an operating model. Here’s the uncomfortable translation: If you can’t answer these in 30 seconds, you don’t have “data.” You have vibes: • Where did this claim come from? • What changed since last week? • Who owns it? • Who can use it, and why? • Can we prove it on demand? This is why “better dashboards” won’t save you in 2026. The winners will be the companies that treat trust like reliability: • SLAs for critical metrics • versioned definitions (what is revenue today) • policy enforcement you can measure • lineage you can show, not narrate Hot take: The most important KPI for 2026 isn’t model accuracy. It’s trust latency. How fast your org can go from claim to source to proof. If you had to pick ONE Trust KPI for your org this year, what’s the most honest? A) Percent of critical metrics with end-to-end lineage B) Policy enforcement rate (access, masking, retention) C) Time-to-proof (claim to evidence) D) Data product SLA breach rate Drop your letter and why. #DataTrust #DataGovernance #DigitalTrust #ResponsibleAI #Davos