Saurin Pandya

Outcome-Anchored Agent Strategy If your agent initiative doesn’t have a line item on the executive scorecard, it’s already on the chopping block. MIT's systems thinking labs drilled into me that measurable business outcomes come first. In the enterprise data science teams I've led, agent projects only moved the needle when we defined each agent as an owner of a KPI, not as a proof of concept. - Translate revenue, risk, or customer metrics into agent-level objectives before writing prompts. - Surface the data signals and decision levers the agent can realistically influence. - Instrument evaluation gates so leadership can see the agent's contribution on the same dashboards as human teams. This is the first pillar of my Agentic AI course because it keeps innovation aligned with executive scorecards and makes it easier to secure ongoing sponsorship. #AgenticAI #EnterpriseAI #DataScienceLeadership #MIT #IISc

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If the plumbing of your data isn’t right, even the smartest analytics stack won’t deliver. In our client conversations at DataQuark.AI, nearly 7 out of 10 brands admit unmanaged tags have polluted their dashboards, CDPs, and campaign reporting. Often, the problem is discovered only after revenue has already been lost. The cost shows up in ways businesses relate: • Marketing spends wasted on wrong signals • Compliance risks from misfiring consent banners • Slow, inefficient teams debating ownership instead of fixing issues • Customers lost because insights weren’t trusted in time • Technical documentation beyond business teams' understanding The solution isn’t adding more tools, it’s fixing the foundation. We suggested clients to setup a Tag & Data Dictionary that brings order: monitored tags, defined events, junk cleared, and consent tracked properly - all in near real time. When this methodical approach is prioritzed, the business impact is clear: - Marketing efficiency restored - Compliance risks minimized - Faster decisions, less firefighting - ROI lifts touching double digits Because data you can’t trust doesn’t just waste budgets, it erodes confidence. Trust in data isn’t just a metric, it’s the multiplier of every other outcome. LS Digital Group #DataTrust #MarketingAnalytics #TagManagement #CleanerData

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First Post in a while, but have been jumping on the AI automation train for Data and Analytics. Created several application including text sentiment analysis and efficiency process. What are some of the ideas out there behind AI and Data and Analytics you been wanting to try?

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AI is good at things analytics is bad at, and vice versa? I recanted a recent AI finding to Matt Regan today over lunch today. I passed Claude Code a delimited file and asked it to "tell me how column H differs when column J has a value or not." Column H was an extended email exchange between two parties. Column J was more or less a success flag. I more specifically asked it to tokenize the emails, n-gram the results, and find good positive prediction indicators. I was at least smart enough to tell it to exclude reply text (repetitive). The results were wholly uninteresting. I asked it to try some more filtering methods (TF-IDF, etc.). Still, the results were lame. It seemed to be too focused on success indication and the top 100 results were largely unusable (names, generic business jargon, etc.). I decided to change tack ... "Tell me what you find in the email exchanges where the success flag was on." Now, here we go. I got some clear and interesting findings, with stats to back them up. I'd actually studied this data myself beforehand and could verify (for the most part). I kept mulling it over the weekend, and come this morning I decided to push a bit further. "Is there anything particular about the first few emails in the chain that indicates a higher possibility of the success flag and vice versa?" All of a sudden I had a half dozen good points on each side. What led here? Getting out of the way. I'd probably still be sparring with Claude, trying to get decent lists of positively and negatively correlated n-grams, if I hadn't stepped back. I'm super happy I rolled with the AI tide rather than swimming against it.

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How do you transition GenAI from a cool chatbot novelty into a reliable, enterprise grade platform? It really comes down to two critical mandates: strict semantic governance and rigorous context grounding. Anthropic recently published an excellent write-up on how they automated 95% of their internal business queries with Claude. Their core finding is a massive validation for enterprise data engineering: accurate analytics isn't a SQL generation problem, it is a context, governance, and verification problem. Reflecting on my own experience at Meta, these principles reinforce exactly what it takes to drive production grade AI value and prevent what Anthropic calls a "false sense of precision." A few of my main takeaways from the article: 1) Eliminating data ambiguity with entity resolution. The hardest part of data analytics is navigating the messy reality of data itself. When an AI handles a question like "how many active users do we have," it needs a standardized corporate ontology to know exactly which table definitions and look-back windows to use. Mapping questions to precise, up-to-date data entities is what gives the model an accurate anchor point for reasoning. 2) Fighting skill drift through active metadata maintenance. Anthropic noted that without well-maintained context, internal query accuracy drops from 95% to 65% in just a single month as table structures and definitions evolve. Treating your metrics and business rules as versioned, governed software assets is the only way to prevent your AI's knowledge base from quietly rusting. 3) Validating continuously via adversarial review. To trust automated outputs, you need a live verification layer at answer time. Implementing continuous offline evaluations, provenance checks, and adversarial sub-agent loops ensures data quality and structural correctness before a response ever hits an executive's screen. True AI transformation is not about the model you choose. It is about the structural governance of the knowledge you feed it. Read the full article here: https://lnkd.in/enQGdS53

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🚀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗕𝗜 𝗮𝗿𝗲 𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗮 𝗻𝗲𝘄 𝗲𝗿𝗮 — 𝗮𝗻𝗱 𝗚𝗮𝗿𝘁𝗻𝗲𝗿’𝘀 𝗹𝗮𝘁𝗲𝘀𝘁 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝗰𝗮𝗽𝘁𝘂𝗿𝗲𝘀 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝗮𝘁 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗽𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿. Gartner’s future-state themes for Analytics & Business Intelligence (ABI) aren’t “nice to know” anymore — they’re becoming design principles for the next generation of intelligence platforms. A few standout shifts: 🔹 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 AI agents that don’t just analyse — they monitor, detect, recommend, and act within defined guardrails. This is where BI stops being passive. 🔹 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 The convergence of BI, AI, rules engines, simulation, and workflow into a unified decision stack. This is the beginning of “decision automation at scale.” 🔹 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗕𝗜 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 Analytics co-pilots embedded into every role, not just every tool. The interface becomes conversation-first, insight-second. 🔹 𝗣𝗿𝗼𝗱𝘂𝗰𝘁-𝗹𝗲𝗱 𝗗𝗮𝘁𝗮 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 Metrics, insights and analytical capabilities managed as products with clear ownership, SLAs, and value tracking. 🔹 𝗖𝗼𝗺𝗽𝗼𝘀𝗮𝗯𝗹𝗲, 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰-𝗱𝗿𝗶𝘃𝗲𝗻 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Reusable components, semantic layers, and interoperability replacing monolithic BI stacks. 👉 𝗚𝗮𝗿𝘁𝗻𝗲𝗿 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝗹𝗶𝗻𝗸: https://lnkd.in/gAxB23N8 My take? Organisations should be refreshing their Analytics and Business Intelligence (ABI) roadmaps 𝘯𝘰𝘸, not in 12–24 months. The companies already executing on Agentic Analytics and Decision Intelligence Platforms are building a competitive gap that’s going to widen fast. If you want a point of view on how these themes translate into practical next steps — architecture, operating model, value cases — happy to compare notes. #Analytics #BusinessIntelligence #AI #DecisionIntelligence #AgenticAI #IntelligencePlatform #DataStrategy #Gartner #FutureOfAnalytics 🚀

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Semantic layers are emerging as core infrastructure for agentic analytics. Why? Because agents need context for accurate analytics, and that context is the semantic layer: it tells agents how business metrics like revenue or MAU should be calculated. Without this, they fumble badly. The big vendor debate is: where should this layer live: in the BI tool (Looker’s LookML, PowerBI’s DAX), in middleware (dbt, cube), or in the database? I strongly believe in “semantic pushdown”: stick metadata and metric expressions in the database alongside table definitions. Make it easy to manage and access via SQL and let it inform optimizations like materializations. After all, what is a database if not a place to store and query data?

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Dr. Ramla Jarrar, an MMM expert, shares an important perspective every marketer using MMM should read. The customer journey is complex, and while MMM is a powerful tool for understanding how spend impacts that journey, its limitations are often overlooked and nuances brushed over once results are packaged in polished decks. The tension I often see: marketers need one strong KPI to track large marketing investments, but the complexity of measurement means no single framework provides that holy grail metric. Measurement methodologies are sophisticated and technically robust, yet each has constraints. Triangulating across multiple frameworks—MMM, incrementality testing, attribution, brand tracking—provides better insights, though interpretation remains as much art as science. This requires partnership. Marketers need to understand what each methodology can and cannot deliver. Analytics partners, whether internal teams or external consultancies, have the responsibility to explain assumptions, acknowledge limitations, and help interpret results responsibly. When both sides engage with this complexity honestly, we make better decisions. A question for marketers: where do you find the biggest gaps between marketing performance measurement and the questions you need answered? #MarketingMixModeling #MMM #MarketingAnalytics #MarketingMeasurement #MarketingROI #DataDrivenMarketing #MarketingStrategy

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