🚀 The Future of Analytics: AI Agents + Dimensional Modeling = Game Changer 🚀 Just witnessed something fascinating - AI agents are now seamlessly working with traditional data warehouse dimensional models, and it's revolutionizing how we approach analytics! 🎯 Here's what's happening: ✅ Intelligent Schema Understanding: AI agents can instantly comprehend star schema structures, identify fact vs dimension tables, and understand grain relationships ✅ Automated Query Generation: From natural language to SQL - "Show me sales trends by region for Q3" becomes complex multi-table joins in milliseconds ✅ Dynamic Insight Discovery: Agents traverse dimension hierarchies automatically, uncovering patterns we might miss ✅ Real-time Optimization: They suggest schema improvements, identify performance bottlenecks, and recommend indexing strategies 🔥 The Magic Combination: - Kimball's proven methodology (star schema, conformed dimensions, slowly changing dimensions) - AI's pattern recognition and natural language processing - Modern cloud data warehouses (Snowflake, BigQuery, Redshift) Result? What used to take data analysts hours now happens in minutes. Business users can ask complex questions without knowing SQL. Data teams focus on strategy, not syntax. 💡 Real Example:  "AI Agent, analyze our customer churn by product category, geographic region, and seasonal trends over the past 3 years, then identify the top 5 actionable insights" The agent instantly: - Joins fact_sales with dim_customer, dim_product, dim_geography, dim_date - Applies proper time intelligence - Generates visualizations - Provides narrative insights 🎉 Why This Matters: This isn't replacing dimensional modeling - it's SUPERCHARGING it! The proven star schema design becomes the perfect structured foundation for AI to deliver unprecedented analytical velocity. The future of analytics is here: Human business intuition + AI computational power + Proven data architecture #DataWarehouse #ArtificialIntelligence #Analytics #BusinessIntelligence #DataEngineering #DimensionalModeling #StarSchema #DataScience #MachineLearning #CloudAnalytics What's your experience with AI-powered analytics? Have you seen AI agents working with your data warehouse? Share your thoughts! 👇

🚀 Top 10 AI in Data Analytics This Week! AI is transforming how we analyze, visualize & act on data. Here’s the freshest roundup of tools & trends you shouldn’t miss ⬇️ 1️⃣ Databricks 🤝 OpenAI Enterprise teams can now build AI agents directly on their data with Agent Bricks. Supercharging analytics with GPT-powered insights! 2️⃣ Google Cloud’s New AI Agents 🌐 Six powerful AI agents launched for data engineering, feature building & conversational analytics — making pipelines smarter & faster. 3️⃣ Snowflake Cortex AI ❄️ Natural language to SQL at enterprise scale. Security-first AI analytics for organizations that care about governance. 4️⃣ Microsoft Fabric + Copilot 💡 Copilot is now built into Fabric — write queries, automate BI, and document workflows with simple prompts. 5️⃣ Google BigQuery + Duet AI 📊 AI-assisted queries & dashboards in BigQuery Studio. Save time, cut costs, and optimize performance. 6️⃣ Tableau Pulse with Einstein Copilot ⚡ From dashboards to conversational insights — Tableau makes analytics feel more human & interactive. 7️⃣ ThoughtSpot Sage 🔍 Search-driven analytics with GPT-4. Ask a question in plain English, get instant insights from your enterprise data. 8️⃣ Powerdrill Bloom 🌸 Upload data → get guided insights, visualizations, and anomalies detected automatically. AI-first analytics at your fingertips. 9️⃣ AutoML & Augmented Analytics 🤖 Feature engineering, model selection & tuning now automated. Democratizing ML for analysts of every skill level. 🔟 Conversational Analytics 💬 Plain English to SQL, DAX & dashboards. Analytics is becoming as easy as asking a question. ✨ The future of data analytics isn’t code-heavy — it’s conversation-driven.

Loved this brilliant insight from Animesh Kumar that highlights a critical link which is at the heart of any successful AI driven business transformation. The Rise of #ContextArchitecture: Where Metadata Is No Longer a Byproduct- It’s "The Product" In the AI era, context is the hyperfuel transforming datafills into goldmines. While data tells us what happened, context tells us why, and this shift is foundational. Traditional data stacks optimized for storage and processing are falling short in enabling true understanding. For years, organisations have collected data without context, optimised pipelines without meaning, and catalogued metadata without comprehension. A well-engineered context architecture changes that by reconstructing context across disconnected layers: not through documentation, but through deduction. What is #ContextArchitecture? This new paradigm prioritizes metadata, profiling, and usage analysis to transform passive catalogs into active reasoning systems. At its core is the 1. #DeductionStack — a 3-layer model that infers structure (Meta), content (Profile), and intent (Usage). This enables systems to deduce, not just describe. The deduction stack builds the semantic spine and feeds forward the deduction mappings and behavioural insights to the 2. #ProductiseStack: Turning understanding into intelligent data products.It maps your realised intelligence: the data products, services, and models that embody business outcomes The Deduction Stack reveals your current potential, while the Productise Stack manifests it into living products. 3. #ActivationStack: Powering real-time, cross-domain decisioning through composable intelligence.This stack abstracts complexity, allowing domain-specific apps, be it in marketing, supply chain, or finance, to plug directly into the intelligence network of data products and act on them instantly. This is where data finally behaves like software with composable and responsive features. Together, these form a closed-loop of contextual intelligence, paving the way for AI-native, self-evolving data ecosystems paving the path to AI maturity. Key takeaway? Catalogs are extremely useful, if only there is a solid context architecture behind them that is architecturally consolidating the data ecosystem. And this is genuinely not the job of catalog companies or siloed catalog tools. The change needs architectural intervention from within your organisation. #ContextArchitecture #Metadata #AI #DataProducts #DigitalTransformation #EnterpriseAI

Do your reports explain *why* something is happening and what to do next? Most don't. This new article by Whitney M. Marks shows you how conversational analytics can provide not just an answer, but a recommended next step.

The Future of Insight Isn’t Just Data — It’s Vibe MIT Sloan’s recent article, “Vibe Analytics: Vibe Coding’s New Cousin Unlocks Insights,” highlights a powerful shift in analytics: the ability to measure what was once considered unmeasurable — tone, emotion, and narrative context across human interactions. At Vibe Analytic, we’re building the infrastructure to make that real. Our AI-driven modelling stack translates unstructured feedback, conversations, and behavioral data into explainable, actionable insight. What we deliver: • Vibe Fit Scores that quantify sentiment, intent, alignment, and friction across channels. • Automated insight pipelines built on embeddings, clustering, and narrative analysis. • Power BI dashboards for real-time mapping of customer experience and operational bottlenecks. • Prefect + FastAPI orchestration for continuous model refresh, explainability, and audit readiness. • CX, Growth, and Retention playbooks that cut churn, lift engagement, and improve activation within days. The goal is simple: help organizations see what customers feel — and act on it faster. Whether it’s optimizing a SaaS onboarding journey or monitoring sponsor contagion risk in banking, our models expose the hidden relationships driving behavior. Vibe Analytics is more than a trend. It’s the next evolution of decision intelligence — where human narrative meets machine precision. Read the MIT Sloan article: https://lnkd.in/dz8jz5Wr Learn more: www.vibeanalytic.com #AI #Analytics #CustomerExperience #VibeModelling #DataScience #VibeAnalytic

Thursday Three Bullets for 23OCT25 – curated reads sparking smarter data conversations 1️⃣ The term “Data” is dividing us. Background and perspective Too many organizations still separate “business” and “data”, as if they operate in different worlds. Adam Keresztes' article suggests this divide is blocking growth, particularly in the era of AI. Today, AI can spot patterns often confirming what we already know, but only humans can provide the “why”. True progress happens when machine-derived business logic, human judgement, and a common data language converge for AI to successfully create new insights. 💡 AI may understand our data, but does it understand us? Answer: without semantics it does not! 🔗 Original article: https://lnkd.in/gZ9bwr2Y 2️⃣ The Human Fabric of Knowledge Architecture So, we’ve allegedly mastered data management, but that’s no longer enough. François Rosselet suggests the new competitive edge lies in speed of understanding. Consequently, knowledge architecture is replacing traditional architecture, where the focus is on building data ecosystems with enhanced semantics, reasoning, and human-AI collaboration. These systems allow the business to make decisions faster than the competition. 💡 Are you still just storing data, or building data ecosystems where AI understands our business? 🔗 Original article: https://lnkd.in/gSCMrMCv 3️⃣ Deploying AI agents at scale Snowflake’s recent white paper reminds us that AI agents are only as powerful as the data ecosystem they sit on. Without a modern, governed foundation that unites structured and unstructured data, AI cannot act, it can only observe. With data agents, conversational agents, and multi-agent systems now shaping enterprise AI, success is dependant on a solid foundation of integrated data. 💡 With Agentic AI evolving so fast, is your data ready to empower AI? 🔗 Original article (registration required): https://lnkd.in/gvz6vuPZ #ThursdayThreeBullets #TTB #DataArchitecture

Observing in the Data Science World: The Screenshot Problem AI is helping data scientists to write code faster, generate compelling slide decks and building advanced data visualizations. But when it comes to sharing quick insights with stakeholders, the fastest way is still.. screenshots. These screenshots of exciting tables or graphs spread like wildfire—across PMs, leadership, and entire orgs. Sometimes screenshots are out of our control—they can be taken by anyone in the audience and circulated without context. Weeks later, the questions start rolling in: "Who did this analysis?", "What’s the underlying data?", "Can we recreate this graph?" And suddenly, data scientists and PMs are spending hours reverse-engineering their own work just to make it consumable again in slides, wikis, or databases. That's why I built a quick agentic toolkit - Screenshot to Data. - Upload any screenshot of a table or graph - Get structured outputs: CSVs, SQL queries, and editable slides - Even embed the table/graph into Wiki/Confluence pages Even if extracting data from screenshots might take just 10 minutes manually, scale that across ~40 DS/PMs in a large org, and the time savings become massive. More importantly, it gives back valuable hours for insight generation, not screenshot reconstruction. Let me know your thoughts! Built with Google AI Studio and edited with Adobe Express. #generativeai #agents #datascience #productivity

Food for thought for all Data and AI Product managers... Dashboards are great, but conversations are better I’ve worked with Business Intelligence tools to build dashboards, track KPIs, and keep reporting consistent. They’re great for answering questions we already know to ask. But when I want to dig deeper, explore patterns, test a hypothesis, or just ask “why” the traditional BI feels slow and rigid. That’s why I’m excited about what’s happening with LLM-powered Exploratory Data Analysis (EDA). Instead of navigating dashboards or writing SQL, you can simply ask: “What drove revenue growth last quarter?” …and get a clear, context-aware (not so straightforward and needs work though) answer in seconds. This isn’t science fiction. It’s already here with tools like: Snowflake Copilot for conversational analytics on your data cloud ThoughtSpot for natural language search-driven BI Or even a custom solution built on top of your data warehouse or lake using an MCP server Here’s how I see the difference: 📊 Traditional BI Predefined dashboards and rigid schemas Great for standardized reporting Governance through strict modeling 🗣️ LLM-Powered EDA Natural language queries and dynamic exploration Combines multiple sources without heavy prep Governance embedded in metadata and access controls The big shift? BI is structured and visual-first. LLM-powered EDA is adaptive and conversational, making it easier to explore data and get answers fast. I believe these approaches will coexist... BI for compliance and repeatable reporting, LLM-powered EDA for discovery and speed. What do you think? Will conversational analytics become the default for ad-hoc analysis? #DataAnalytics #AI #BusinessIntelligence

Over the past few years, I’ve watched Data Science evolve at lightning speed. But nothing compares to the transformation we’ve experienced in the last three. Generative AI has completely reshaped how we work. What once took weeks of manual data prep, pipeline building, and model iteration can now be achieved in hours or even minutes. The role of a data scientist is shifting from repetitive coding to designing intelligent, AI-driven workflows that create real business impact. Recently, I came across Google’s new Practical Guide to Data Science, and here are a few key insights that really stood out to me 👇 ➝ The Agentic Shift Most of a data scientist’s day used to go into cleaning data, tuning models, and writing the same pipelines again and again. Now, AI agents automate those parts. Our value is moving toward analysis, interpretation, and driving business outcomes. ➝ Multimodal Data For years, we were limited to structured tables. But most enterprise data is actually unstructured images, PDFs, audio, and text. With BigQuery, you can now analyze all of this directly with SQL. That means combining structured sales data with call transcripts is no longer a challenge it’s a reality. ➝ Blending External Intelligence with Enterprise Data Foundation models bring real-world knowledge into enterprise stacks. Instead of writing hundreds of rules, you can now ask nuanced questions like: “Which of our products show high satisfaction based on quality?” This kind of reasoning used to take months of manual analysis. ➝ AI as a Feature Engineering Engine Instead of just running basic sentiment analysis, you can extract structured insights at scale — for example, analyzing sentiment specifically around “battery life” or “user interface” and joining that with sales data. Raw text turns into powerful, high-value features for your models. ➝ In-Place Model Development Moving data between systems used to be a major bottleneck. With BigQuery ML, models are trained and deployed right where the data lives. Teams are seeing deployment times cut by 10x, letting them focus more on insights and less on infrastructure. ➝ Vector Embeddings & Semantic Search Vector search used to mean adding another system. Now, it’s built into BigQuery itself. That means semantic product discovery, document retrieval, and multimodal analysis all within your data warehouse. ✨ Follow Reshma S || #reshmawithai for more insights on how AI is transforming the data landscape.