Modern BI wasn’t built for AI. Fragmented tools, duplicated logic and inconsistent metrics make analytics harder to trust. A better approach brings data, semantics, dashboards and AI together on a single governed foundation. This guide explores how modern analytics platforms can: • Simplify analytics architecture • Improve metric consistency across tools • Accelerate real-time insights • Let teams ask questions in plain language and get analytical answers https://lnkd.in/g-7xMBd9
The fragmentation problem is real. Most teams are stitching together 4-5 tools just to go from raw data to a decision, and every handoff introduces inconsistency. The shift toward a single governed foundation is exactly where things need to go - analytics that people can actually trust without chasing down which dashboard has the right number.
AI doesn’t fix fragmented analytics, it exposes it. Without a consistent semantic layer, “answers” become opinions. The real value is in creating a shared foundation that both humans and AI can rely on.
What s third class perspective filled with flaws from a vendor desperately trying to push LLMs everywhere. No the world problems of bad data and solutions architecture cannot and should not be fixed bynover engineering LLMs. Its like the horrible obsevability sokution from you where there was fundamentally flawed designa and solutions in the way you store your logs and then threw LLM into it showing a fabricated win.
So true: fragmented tools and duplicated logic hold back modern analytics. This guide breaks down how to simplify architecture, unify metrics, and speed up real-time AI-powered insights.
AI can’t give you the right answers if your data tools are all speaking different languages and can't work together.
Scott Paul, MBA, CCISO, CISSP, CISM, CRISC, CEH, CDPSE thought this might be relevant for you!
A solid framework, and the unified semantic layer is probably the most underrated part. One of the few genuine attempts to resolve metric inconsistency at the platform level rather than patching it downstream. Two comments I do have ; 1) On Iceberg interoperability: the direction is genuinely encouraging. From what w've observed, there's still a feature gap with Delta in areas like CDC and streaming, and several native tools still seem to assume Delta under the hood. Curious to hear from people running this in production: how are you experiencing that gap today ? or is that an erroneous observation? 2) On Lakeflow Designer: it's a recent addition ? The real question is whether some business analyst can actually take it to production autonomously, or whether an engineer still needs to be nearby. That gap between "no-code" and "actually no-code" is where it tools struggles I find. ... Ah last one :) There is one thing these pillars do not address, by their nature, how the platform is understood and used. Tools do not create the organizational consensus. Beyond the concepts Data Mesh, Data Products, Federated Gov, Data Contracts or products ...itremains the hardest part. Still waiting to discover a SIMPLE framework for that :)