Olivia Xiaodan Meng’s Post

𝗪𝗵𝘆 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗔𝗿𝗲 𝗠𝗼𝘃𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 Traditional dashboards tell you what happened. Modern AI platforms help explain:  • Why it happened  • Which conversations caused it  • Which customers are at risk  • Which agents need coaching  • Where revenue opportunities exist Analytics are evolving from reporting tools into decision-support systems. The shift is from 𝗼𝗯𝘀𝗲𝗿𝘃𝗶𝗻𝗴 operations to 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 them. 𝗔𝗿𝗲 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝗲𝗻𝗼𝘂𝗴𝗵 𝗳𝗼𝗿 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝗼𝗿 𝗶𝘀 𝗶𝘁 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀? 𝗧𝗲𝗹𝗹 𝘂𝘀 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂 𝘀𝘁𝗮𝗻𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀. #artificialintelligence #enterpriseai #conversationintelligence #customerexperience #businessintelligence #realtimeanalytics #decisionintelligence #digitaltransformation #agenticai #vanie

📊 What if your survey data could do more than sit in spreadsheets? Most organizations don't have a data problem. They have an insight problem. When survey data lives across multiple tools, teams spend more time cleaning spreadsheets than uncovering what customers are actually saying. That's exactly what one market research firm was facing. So we helped them: 🔹 Centralize survey data into a single platform 🔹 Add an AI-powered intelligence layer 🔹 Turn raw responses into faster, actionable business insights The outcome? 📈 45% Revenue Growth ⚡ 25% Faster Insight Delivery The real value of AI isn't replacing analysts—it's giving them the ability to find patterns, trends, and opportunities in minutes instead of days. Swipe through the carousel to see how a modern data stack powered by AI transformed survey intelligence into business growth. 💬 What's the biggest challenge your team faces with survey or customer data today? #9series #MarketResearch #DataPlatform #AIAnalytics #Snowflake #BusinessIntelligence

The future of data visualisation is moving beyond dashboards; it’s about creating intelligent, accessible, and actionable insights. Power BI continues to evolve by combining advanced analytics, AI capabilities, stronger governance, and cloud-based collaboration to help organisations make faster decisions. As data volumes grow, the ability to transform complex information into clear business intelligence will become a critical competitive advantage. Read on for more: https://lnkd.in/dcmR9t9Q #PowerBI #DataAnalytics #ArtificialIntelligence #BusinessIntelligence

AI, analytics, and governance all depend on one thing: consistent business meaning. In this video, Jamie Knowles demonstrates how ER/Studio 21.1 turns Enterprise Logical Data Models into reusable semantic assets that can support governance platforms, knowledge graphs, semantic layers, analytics, and AI applications. 🔹 See the new ER/Studio Semantic Generator in action 🔹 Generate semantic layers for Power BI, OSI, and dbt 🔹 Preserve trusted business meaning across downstream platforms 🔹 Reduce semantic drift as your data ecosystem grows Watch the video 🔗 https://lnkd.in/gBEnTQ4J #ERStudio #DataModeling #DataGovernance #MetadataManagement #KnowledgeGraph

Dashboards tell you what happened. AI tells you why it happened—and what to do next.👌 Imagine a Commercial Performance Dashboard that doesn’t just display KPIs, but analyzes performance, identifies root causes, and provides actionable recommendations in real time. By integrating AI with commercial dashboards, decision-makers can move beyond reporting to intelligent, data-driven decisions. The future of business intelligence isn’t just visualization—it’s intelligent decision support. #ArtificialIntelligence #BusinessIntelligence #CommercialExcellence #SalesPerformance #PowerBI #DataAnalytics #DecisionSupport #DigitalTransformation

I use a simple score to pick AI use cases. The score has four parts. Each gets 1–5 points. 1) Impact — How much business value if it works. 2) Effort — Time and cost to build and maintain it. 3) Data readiness — Do we have clean, usable data now? 4) Adoption risk — Will people actually use it? I add the four scores. Higher total means higher priority. I prefer cases with high impact and data readiness even if effort is medium. That gives fast, reliable wins and builds trust. If you want the spreadsheet I use, send me a message and I’ll share it. #AIstrategy

Everyone wants AI agents, but almost nobody is talking about what happens after you put them in front of real users. The first time an agent confidently gives the wrong answer, people stop trusting it and suddenly every response has to be double-checked. The problem usually isn't the model. It's that the agent doesn't actually understand your business, your metrics, or how your data fits together. That's why we believe agentic BI needs more than an LLM on top of dashboards. Our Solutions Engineer, Zack Martin, wrote about what it takes to build analytics agents people can actually trust and why this is becoming one of the biggest challenges enterprises will face as AI moves into production. https://lnkd.in/gJ__eQne

🪷The Lotus-Eaters: When Dashboards Make Everyone Feel Busy AI type: Descriptive analytics, business intelligence, and passive anomaly detection The Lotus-Eaters represent comfortable inactivity. Everyone can see the dashboard. Nobody is certain what should happen next. Utilities already possess reports, scores, alerts, and analytics that show what happened. The operational gap appears after the insight: 🧭 Which issue matters most? 🏺 Why does it matter? 👤 Who owns it? ⚔️ What action should follow? ⚓ Was the action completed? 🏛️ Did the result improve? UTILITY AI TAKEAWAY ⚓ Visibility without decisioning can become a very attractive form of delay. Analytics creates more value when observations are contextualized, ranked, connected to next actions, routed to the right owner, and followed through to measurable outcomes. A Dashboard Is Not a Destination #UtilityAI #AnalyticsToAction #UtilityOperations #DecisionIntelligence #UtilityAnalytics

AI is moving proprietary data closer to the heart of customer workflows, creating new value and risks. Our latest article sets out how data, information, and analytics businesses can commercialize AI while protecting their core offer. Read more: skp.link/ayqs #MonetizingAI #Data #Information #Analytics #CommercialStrategy

If you sell data, information and analytics, - how should AI change (or not) your product roadmap? - how should it change how you think about data licensing? - and how should it change how you think about the notion of a 'product' itself? My colleague Oliver Peacock and I continue to talk to companies across all data/information markets contemplating the exact same questions and have written down some of our (current!) thoughts here...