Agentforce Sessions at TDX: Data 360, AI, and Enterprise Knowledge

POST 3/4  ·  THE DATA LOOP  Who owns? Data Platform: Strength or Fragmentation. Here's the loop that determines whether AI delivers value or just generates noise: Business Problem → Framework → Data Richness (or fragmentation) → AI Scope → Solution/ Refine to enrich: Business Problem. The hinge? Who owns the data platform. When IT, Business Units, the C-Suite, and Data Science all have a stake — with no clear ownership — your potential "data moat" becomes less, even a data swamp. Every AI initiative stalls at the same place: whose data is it, and who decides what to do with it? In small companies, is the data good enough? Here's the commercial angle that gets underestimated: customer data — CRM, market automation, loyalty systems — is often the richest proprietary data in the enterprise. And it lives in the commercial leader's domain. That's not an IT problem. That's a competitive advantage hiding in plain sight. Post 4 of 4. Friday: the ESR Ecosystem — and where my focus lands. Where does data ownership break down in your organization? What new data sources extend beyond traditional? Is it governance, culture, or something else?

Can you trace any number in your board deck back through every layer to its source? Most organisations can’t. The board sees AI, analytics, and automation. Clean dashboards. Confident decisions. That’s the top of the stack. But between that number and the raw data, there are six layers. And most of them are held together by hope. Here’s what the governed data stack actually looks like: → Source Systems. ERP, CRM, product database, event streams. And one person who “knows how it works.” This layer exists in every company whether you admit it or not. → Lineage & Transparency. Source system mapping, field documentation, pipeline traceability. Most orgs stop here and think they’re governed. → Data Quality. Validation rules, thresholds, alerts, incident handling, root cause analysis. This is what makes change safe and fast. → Definitions & Ownership. Business definitions, metric logic, named owners, escalation paths. Governance is an operating model, not a policy folder. → Trust at Scale. Confident decision-making, scalable self-service. This is the layer leadership thinks they’re buying. → AI, Analytics & Automation. Dashboards, advanced analytics, AI models, RAG, agents. Only possible when all layers below exist. Every company has this stack. The difference is whether someone owns each layer or just hopes it works. I wrote a free playbook on fixing the ownership layer in 30 days: https://lnkd.in/dGDjTkev ➤ Follow John for daily posts on what actually breaks in data teams and how to fix it. 🔔 Tap the bell on my profile to get notified when I post. ♻️ Repost if your stack has gaps nobody talks about.

zypl.ai and Treasure Data today announced a strategic partnership to enable enterprises to simulate, test, and optimize decisions before real-world deployment. The collaboration combines Treasure Data’s enterprise customer data platform (CDP) with zypl.ai’s synthetic data engine (zGAN) and Lucid decision intelligence platform, enabling companies to test and optimize marketing, product, and operational strategies while reducing reliance on costly real-world trials. The partnership supports enterprises globally, with initial deployments underway in Japan and broader engagement across Asia and other markets. Under the collaboration, enterprises can unify and activate customer data in Treasure Data’s CDP and generate privacy-safe synthetic datasets using zypl.ai’s zGAN technology, enabling teams to model complex scenarios, create customer “digital twins,” and evaluate multiple strategies before implementation. The joint solution addresses a key limitation faced by enterprises: despite significant investment in data platforms, many organizations remain constrained by data scarcity, strict privacy requirements, and limited ability to validate strategies before execution. By combining real-time data unification with synthetic data generation and scenario modeling, organizations can simulate rare or emerging conditions and compare strategies ahead of execution. Deployed in secure, controlled environments, the solution ensures data privacy, governed access, and full traceability across modeling workflows, while reducing experimentation costs, accelerating time-to-insight, and improving decision quality across key business function. The joint solution supports decision-making across multiple business functions: Marketing & Customer Intelligence * Customer simulation and digital twin modeling * Campaign and segmentation strategy testing * Personalization optimization under variable behavioral scenarios Product & Strategy Optimization * Pre-launch testing of product features and pricing models * Market entry and expansion scenario simulation * Decision validation under uncertain or data-limited conditions Operations & Demand Planning * Demand forecasting across variable market conditions * Supply chain and operational scenario modeling * Resource allocation optimization Risk & Uncertainty Modeling * Scenario testing in low-data or volatile environments * Behavior modeling under rare or emerging conditions * Decision support under incomplete or constrained datasets The partnership reflects growing demand for privacy-safe, simulation-driven decision-making across global markets, including Japan. Both companies are already engaging enterprises across industries, with pilot projects underway and broader deployment in progress.

🔍 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: 𝗧𝗵𝗲 𝗙𝗶𝗿𝘀𝘁 𝗮𝗻𝗱 𝗠𝗼𝘀𝘁 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗦𝘁𝗲𝗽 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 Before organizations talk about AI, analytics, or advanced insights, there is one fundamental question: 👉 𝗖𝗮𝗻 𝘆𝗼𝘂 𝘁𝗿𝘂𝘀𝘁 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮? Data Quality is not just a component of data governance, it is the starting point and backbone of the entire framework. Without it, even the most sophisticated data platforms will produce unreliable outcomes. 🔄 𝗔 𝗦𝗶𝗺𝗽𝗹𝗲 𝗬𝗲𝘁 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝟲-𝗦𝘁𝗲𝗽 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝘆𝗰𝗹𝗲\ 1️⃣ Define – Align data quality goals with business objectives 2️⃣ Assess – Evaluate current data across key quality dimensions 3️⃣ Analyze – Identify gaps and root causes 4️⃣ Improve – Design targeted improvement initiatives 5️⃣ Implement – Execute solutions and fixes 6️⃣ Control – Continuously monitor and sustain data quality This is not a one-time effort — it’s a continuous cycle that evolves with the business. ⚠️ Why Data Quality Matters So Much Poor data quality leads to: ❌ Incorrect business decisions ❌ Ineffective marketing & customer engagement ❌ Operational inefficiencies ❌ Compliance and risk exposure On the other hand, high-quality data enables: ✅ Trusted insights and reporting ✅ Stronger customer understanding ✅ Efficient operations ✅ Scalable AI and analytics adoption 🏛️ The Role in Data Governance Data governance defines the rules, but data quality ensures those rules deliver real value. It acts as the bridge between:  • Strategy and execution  • Data availability and data usability  • Information and actionable insight ✨ Key Takeways:   • No data quality, no data trust. No data trust, no business value.  • Start with data quality, because everything else depends on it. #DataQuality #DataGovernance #DataStrategy #Analytics #DigitalTransformation #DataDriven #Insights

“AI” doesn’t fix “Data entropy”. It amplifies it… Most enterprise systems don’t fail at data capture. They fail at data usability, for real decisions. The stack is familiar: a. ERP holds transactions b. Power BI builds reports c. Teams expect insights But the gap shows up in the same places, every time: Consistency. Relationships. Ownership. Consider what each function actually sees: a. Sales tracks “Orders Booked” b. Planning looks at “Capacity” c. Procurement monitors “Lead Times” d. Warehouse sees “Physical Stock” All valid. All real. But not always the same picture. Now layer AI on top. We talk about agentic systems, autonomous decisions, predictive models, but one question doesn’t go away: What data are these systems actually aligned on? If “Customer” exists in multiple forms and “Inventory” means different things across functions, AI doesn’t resolve that ambiguity. It actually scales it. Platforms like "Microsoft Fabric" are a genuine step forward, not just as tools, but as unification layers ERP, data, analytics and AI.. With OneLake plus semantic models, there’s an opportunity to build a shared operational understanding. But the fundamentals still hold: a. Technology supports data quality, it doesn’t replace it. b. Reporting tools don’t define business meaning. c. Ownership still matters. The sequence hasn’t changed: 1. Process clarity 2. Data clarity 3. Intelligent systems When these align, transformation accelerates. When they don’t, complexity grows faster than clarity. What’s your experience? Is the data foundation solid before AI enters the picture?

𝐖𝐡𝐲 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 𝐟𝐚𝐢𝐥 — 𝐭𝐡𝐞 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐥𝐚𝐲𝐞𝐫 𝐧𝐨𝐛𝐨𝐝𝐲 𝐭𝐚𝐥𝐤𝐬 𝐚𝐛𝐨𝐮𝐭 Most AI agent deployments don't fail because of the model. They fail before the model is even called. Here's the layer that kills agent projects in production and almost nobody talks about it: 𝐓𝐡𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. When teams build agents, they obsess over the prompt. What to say to the model. How to structure the instruction. What they skip is designing what the agent actually knows when it runs: → What data does it have access to at inference time? → How fresh is that data real-time or stale? → What happens when the context window fills up mid-task? → How does the agent know when it's operating outside its reliable zone? An agent built on a brilliant prompt but fed incomplete, outdated, or unstructured context will hallucinate confidently every single time. We've seen this pattern repeatedly: A team deploys an outbound agent. The prompt is clean. The logic is sound. But the CRM data feeding it is inconsistent missing fields, duplicate entries, outdated company info. The agent starts generating personalised outreach that references the wrong role, wrong company size, or a product the prospect already cancelled. The agent didn't fail. The data layer failed. The agent just made it visible. 𝐓𝐡𝐞 𝐟𝐢𝐱 𝐢𝐬𝐧'𝐭 𝐚 𝐛𝐞𝐭𝐭𝐞𝐫 𝐦𝐨𝐝𝐞𝐥. 𝐈𝐭'𝐬 𝐚 𝐛𝐞𝐭𝐭𝐞𝐫 𝐝𝐚𝐭𝐚 𝐜𝐨𝐧𝐭𝐫𝐚𝐜𝐭. Before any agent goes into production, define: → Exactly what data it consumes and from where → How that data is validated before the agent touches it → What the fallback behavior is when data quality drops below threshold → Who owns the data pipeline not just the agent Agents amplify whatever they're given. Give them clean context they perform. Give them noise they scale the noise. The architecture question isn't "which agent framework should we use?" It's "what does our agent know, and how reliable is that knowledge?" #AIAgents #SaaSArchitecture #LLMEngineering #EnterpriseAI #CodingTheBrains

🧼 If AI is the “brain” of RevOps in 2026, data quality tools are the hygiene routine that keeps it useful. Instead of a giant tool list, think in 4 categories: 1️⃣ Enrichment & B2B data providers Tools like Amplemarket, ZoomInfo, Apollo, and Clay give you fresher firmographic and contact data, multi‑provider enrichment, and better match rates so AI has accurate inputs. 2️⃣ Data quality & observability platforms Platforms like Monte Carlo, Bigeye, Collibra, and Soda/Great Expectations monitor freshness, anomalies, schema changes, and pipeline health so you catch data issues before models and dashboards break. 3️⃣ RevOps‑oriented CRM data hygiene RevOps stacks that emphasize dedupe, normalization, and field governance (inside or around CRM) make sure records stay clean through routing, territory changes, and AI workflows. 4️⃣ Governance & cataloging Data catalogs and governance layers such as Collibra or OvalEdge help define owners, dictionaries, and policies so everyone (including AI) uses data the same way. The “best” tool is the one that fits your RevOps architecture: Your CRM and warehouse Your enrichment sources Your AI and orchestration layer But the non‑negotiable is this: in 2026, no serious AI RevOps strategy exists without an explicit data quality stack under it. #RevOps #AIinRevOps #DataQuality #RevenueOperations #MarTech #SalesOps #BWAHDigital

Still relying on legacy analytics tools? You might be slowing down more than you think. Modern enterprises need more than static reports—they need real-time, AI-driven insights to stay competitive. That’s where a future-ready analytics platform powered by Power BI migration makes the difference. With the right approach, businesses can unlock: Ø AI-powered business intelligence for enterprises Ø Advanced AI analytics for enterprises Ø Scalable AI Enterprise Data Analytics Ø Faster, data-driven decision-making But successful transformation isn’t just about tools—it’s about strategy, integration, and execution. That’s where Skybridge Infotech comes in. We help organizations modernize their data ecosystem through: ·      business intelligence services ·      Power BI migration services ·      end-to-end enterprise analytics solutions The result? A smarter, more agile enterprise powered by data. Ready to transform your analytics into a competitive advantage? More: www.skybridgeinfotech.com #PowerBI #EnterpriseAnalytics #BusinessIntelligence #AIAnalytics #DigitalTransformation #DataDriven #SkybridgeInfotech

“Mostly right” data in your analytics pipeline can be just as dangerous as completely wrong data. The real challenge isn’t extraction — it’s the last mile. Turning raw outputs into something your systems can actually trust. I’ve been working on a project that extracts structured data from documents into queryable systems. The biggest issue? Hallucinations slipping through. One bad value can quietly corrupt everything. Example: Actual: john.smith@acmecorp.com Extracted: john.smith@amecorp.com Looks valid. Passes format checks. Lands in your CRM. Now every follow-up fails silently. No alerts, no bounce — just lost opportunities. That’s not “mostly right.” That’s wrong with consequences. A few things I’ve learned: • Model choice matters — same prompt, different outcomes • Prompting matters more — clarity reduces hallucination risk • Extraction is easy — validation is the hard part • Confidence scoring changes everything Attaching a confidence score to important fields that matter has been a game changer. High confidence gets written. Low confidence gets flagged for review before it reaches production. If your downstream systems can’t trust the data, the pipeline has no value. Sometimes the most important feature isn’t better AI — it’s a well-designed human checkpoint. A human-in-the-loop approach. Curious how others are handling this. What does your validation layer look like in production?

The $100K+/year modern data stack was built for enterprises with dedicated data engineering teams. SMBs never had those teams, and now they never will need them. AI agents connected directly to your CRM, email, and project tools can deliver 80% of the analytical value at a fraction of the cost. But here's the real unlock: they don't just *analyze* your data. They *act* on it. While competitors like Definite and Julius AI are building AI-native analytics platforms that replace the data warehouse's query layer, Outermind is building the operations platform that replaces the entire data-driven decision-making workflow. Your agents execute directly on insights. They send emails, update records, manage workflows, drive results. No human interpretation layer. No unused dashboards. No data engineer required. The era of copying all your data into one expensive place is ending. For SMBs, it was never the right approach to begin with. Read the full analysis: https://lnkd.in/gxx38fF2 #AI #DataEngineering #SMB #Agents