How AI-powered data management solves BI challenges

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

Contributed to recently published ISG Software Research market perspective highlighting how enterprise success in the AI era hinges on an AI-ready data foundation, requiring not just new approaches to data management and governance but also a rethinking of how software providers align data and AI. It further explores Pentaho's pivot from legacy to AI-native in the data economy. Read the full perspective here: https://lnkd.in/gJd62DR6 #datagovernance #dataintelligence #Analytics #AI #GenAI #datamanagement #datastrategy #datafoundation #isg #isgsoftwareresearch #isgresearch

Delivering Agentic BI: How to Unify Infrastructure, Data and Semantics Whether you’re leading a data team or rewriting SQL queries and building dashboards, AI is fundamentally reshaping how organizations act on their data. Successful AI-powered business intelligence, or ”Agentic BI,” requires data intelligence, when AI understands the company’s data and its unique business concepts to truly unlock self-sufficiency and turbocharge productivity. - AI agents are fundamentally changing the way companies generate business intelligence. But without knowledge of each company’s semantics, AI agents are useless. - Instead, successful “Agentic BI” requires data intelligence, when the systems understand the company’s data and its unique business concepts. - Agentic AI requires three essential ingredients to be unified: infrastructure, data and semantics. Read More Here: https://lnkd.in/gk-Uv9PT #AgenticBI #DataIntelligence #LakehouseArchitecture #SemanticLayer #UnityCatalog #DataGovernance #AIDrivenBI #UnifiedInfrastructure #SmartAnalytics #NextGenAnalytics

Most companies still struggle to answer even basic business questions without waiting days or weeks for their data team. Dashboards and BI tools were supposed to solve this, but they only work if the right data already exists in the right format — and they break the moment a new question arises. That’s why it’s time to rethink what self-service really means. Self-service data isn’t about offloading work to business users. It’s about enabling data teams to deliver trusted, contextual answers instantly — across all your data — while maintaining control, governance, and trust. This blogs breaks down: • Why self-service analytics isn’t enough • The misconceptions that hold teams back • What true self-service data really looks like • Why this shift is critical for scaling AI 👉 Read the full story: https://lnkd.in/e9VZKCwb With self-service data, data teams don’t need to choose between speed and trust — they can deliver both, at scale. #SelfServiceData #AIReadyData #DataTeams #InstantDataAnswers

Why Are Companies Still Using Legacy BI Tools in 2025? Every leadership team says the same thing: “We’re data-driven. We’ve got a new data strategy. Our employees can use the platform. We’re democratizing insights.” But if we’re honest… are they really??? Most organisations are still running on legacy BI tools built for a world that no longer exists. 🚫 90% of employees can’t use them ❌ They can’t ask the next question ⚠️ And yet, they’re expected to make million-dollar decisions off a static dashboard that hasn’t evolved in years If your idea of being “data-driven” is waiting two weeks for an analyst to refresh a report, are you actually empowering decision-making, or just checking a box? The Talent Mismatch Here’s the irony: companies are paying data professionals $60,000 to $200,000 a year not to drive strategy, optimize pipelines, or experiment with AI… …but to build dashboards, chase ad-hoc requests, and serve as human report factories. 💡 Why hire world-class talent to do entry-level reporting work? 💡 Why not unleash them on data strategy, data optimization, and future-proofing your business? The Market in 2025: Moving at AI Speed Analytics today is no longer about static dashboards. It’s about conversation, prediction, and action. AI-driven platforms now allow every employee, not just analysts, to: 🗣️ Query data in plain language ⚡ Uncover insights in real time 🔮 Move from reporting the past to predicting the future This is the new normal. Decisions made at the speed of AI. And the gap is widening fast. Gartner forecasts that by 2026, over 80% of organizations will have used generative AI in some form of analytics, yet less than 25% will have moved beyond dashboards if they cling to legacy BI. IDC predicts AI-powered analytics will account for more than half of enterprise analytics spend by 2028. The message is clear: companies that embrace this shift are accelerating. The ones that don’t are falling behind fast. The Uncomfortable Truths -Legacy BI tools were great five years ago. -Today, they’re anchors. -Tomorrow, they’ll be your biggest competitive liability. Ask yourself: 🤔 Are you enabling data-driven decisions or just creating static reports? 🤔 Are your tools designed for speed and adaptability or for yesterday’s way of working? 🤔 Do you want your best people building dashboards or building the future of your business? In 2025, saying you’re “data-driven” while clinging to legacy BI is like claiming to be “digital-first” while still faxing contracts. The tools that got you here won’t get you there. The next generation of analytics is already here. Move now or 1000% be left behind.

7 days, 7 insights about data, meaning and the human choices behind our systems. 1. Data warehousing fundamentals by Remco Broekmans Remco argues against dismissing transformation and modeling in data pipelines, emphasizing that true business value requires integration around business functions rather than just storing raw data. I share his conviction that integration and modelling are not optional if we want data to serve business functions rather than just accumulate. A persistent staging area may have its value but it cannot replace a warehouse designed to reflect the business. 2. AI's cultural biases by Ross Denton Ross highlights how GPT responses show decreasing alignment with cultural values further from the US, warning against using AI tools to "cover" non-priority markets. When GPT aligns most with cultures closest to the US, it shows how these systems export not just computation but a (pre-setup) worldview. Efficiency pitches that use such models to 'cover' non-priority markets risk flattening nuance into silence. 3. Determinism in LLMs by Jaser Bratzadeh Jaser explores the implications of deterministic LLM inference for AI product managers, noting its impact on evaluations, QA, governance and new use cases. Determinism doesn't come for free. It reshapes cost curves, throughput and architecture choices. For PMs, the real skill will be not just to celebrate determinism but to decide where in the product journey you need it. 4. Global MDM by Nagim Ashufta, DRIVA Global uniformity is a mirage. Business models deviate, regulations differ, structures evolve. Shadow glossaries appear when systems force sameness. The point of Master Data isn't erasing difference, it's to make differences comprehensible and interoperable. Master data should serve the business model, not shape it. 5. Data storytelling by Dr. Joe Perez Joe draws parallels between the ancient art of creating Lapis Lazuli pigment and the careful, methodical work of data storytelling. Joe's analogy to data storytelling captures this perfectly. The artistry is in its care, the context and the origin. Whether pigment or insight, value comes from respecting the process that shaped it. 6. Transformation risks by Clare Kitching Clare warns that the biggest risk in transformation isn't failure but believing you're succeeding, highlighting the importance of asking outcome-driven questions. False success is often more dangerous than outright failure. Leaders need to move past shallow status questions and start asking outcome-driven ones. 7. Data Governance foundations by Sebastian Sbirna Sebastian makes the case for DG as the essential foundation for innovation in healthcare, distinguishing governance from privacy and emphasizing its role as a commercial enabler. Sebastian, I like how you have brought governance "down to earth" here. It shows why it is less about bureaucracy and more about ensuring clarity. Which post matches your current challenge?