This blog post is Human-Centered Content: Written by humans for humans.
Happy Analytics and BI Platform Magic Quadrant release to all who celebrate. Usually when these reports drop, I feel like I’m reading last year’s news today. But this time, Gartner made genuinely astute observations about where this market is headed, even if their individual vendor ratings leave room for debate. If you’re interested in a deep-dive comparison of our three favorite tools on the ABI Magic Quadrant, stay tuned for Wesley’s comparison of Tableau, PBI and (new entrant) Sigma Analytics.
What Gartner Got Right
The Data Platform Invasion
Gartner’s most striking observation this year was their explicit acknowledgment that traditional BI vendors no longer control the analytics conversation. The repeated mentions of Databricks and Snowflake throughout the report reflect something we see often in our work: Organizations with significant investments in these platforms that want newer capabilities like conversational analysis are discovering that native analytics capabilities often serve their needs better than adding another vendor (or an expensive vendor add-on).
This shift represents more than vendor displacement. The boundaries separating data platforms, semantic layers and BI tools have become increasingly arbitrary. Consider how semantic layer providers like dbt and Cube have evolved. Our recent whitepaper with dbt Labs, “Semantic Layers in Action,” documents how metrics layers have become critical infrastructure for modern analytics.
Cube’s recent D3 announcement illustrates where this trend leads. Rather than serving as passive metadata repositories, semantic layers are becoming active participants in the analytics process. The promise here is compelling: Governance and explainability built into the foundation rather than retrofitted through compliance initiatives across multiple tools. Though these capabilities are nascent, the direction seems clear, and we’re excited for it.
Sigma Finally Gets Recognition
Sigma Analytics earned inclusion in the Magic Quadrant, though their positioning understates their importance. I’d argue they should be higher on both axes. The spreadsheet interface gets attention (finance and operational teams certainly love it), but Sigma’s real innovation lies in their conception of data applications as first-class citizens and the deep work they’ve been putting into making data feedback loops into enterprise warehouses a reality.
Consider the typical BI workflow: Users view dashboards, identify issues, then switch to other tools to take action. Sigma collapses this loop. Their platform combines accessible development tools with governed write-back capabilities to cloud data warehouses. When you add their measured approach to AI features through Ask Sigma, you get a platform that addresses full business workflows rather than theoretical use cases.
Where Gartner Misses the Mark
The AI Reality Gap
Gartner’s analysis accepts vendor AI claims with insufficient skepticism. Yes, every major vendor now features some form of conversational interface or “agent.” The problem is that there’s a huge difference between a conference announcement and a real feature that enterprises can trust. But having observed dozens of these systems in demos or production environments, I can report that the gap between marketing promises and operational reality remains vast. Even carefully orchestrated demonstrations fail regularly.
The problem runs deeper than implementation quality. BI vendors face a fundamental velocity mismatch. They’re attempting to integrate AI capabilities into architectures designed for a different era, while the underlying technology evolves faster than their development cycles can accommodate.
The Bigger AI Story
The real threat to traditional BI comes not from feature competition but from foundational shifts in how we create analytical tools. It’s the same trend that puts SaaS business models (as they exist now) at risk in general. As large language models make code generation increasingly accessible, the core value proposition of visual, “no-code” development environments decreases.
We’re observing teams skip traditional BI entirely, opting instead to describe their needs to an LLM and receive functional code within minutes. The “prototype in Streamlit, productionalize in React” pattern appears with increasing frequency. While this approach doesn’t suit every organization or use case, its growing prevalence suggests something important about the future of analytical tool development.
The question becomes, “Will BI vendors successfully integrate meaningful AI capabilities before AI labs like Anthropic make their entire category less relevant?” Based on current trajectories, I’m increasingly betting on Anthropic.
(For organizations seeking real conversational analytics today, Zenlytic merits examination. They lack Magic Quadrant presence but demonstrate both coherent vision and rapid improvement based on user feedback.)
Lock-in Complexity Goes Unexamined
While Gartner acknowledges vendor lock-in concerns, their analysis misses the deeper operational challenges organizations face. The real challenge isn’t just pricing complexity — we’re happy to explain the SKUs to you. The actual risk is architectural dependency. When you adopt Power BI within Microsoft Fabric, you’re committing to an entire data philosophy where compute, storage, governance and analytics become inseparable. (That’s not always bad, but it’s a choice that should be made intentionally.) Maybe the widespread adoption of universal data formats will alleviate this, but we’re not seeing it quite yet.
Similar dynamics affect Salesforce’s Tableau offerings. Gartner’s repeated references to “Tableau” obscure the reality that organizations must choose among Tableau Server, Tableau Cloud and Tableau Next, each carrying distinct capabilities, pricing models and AI features. Achieving Gartner’s described vision requires substantial investment across the Salesforce ecosystem, particularly in Data Cloud.
These aren’t merely commercial considerations. They shape how organizations can (or can’t) evolve their analytics capabilities over time. The integration forces data pipelines, security models and analytical patterns into vendor-specific molds. Meanwhile, newer platforms like Sigma demonstrate a different path: Broad connectivity without architectural commitment. But as the market rewards integration over modularity, truly flexible, best-of-breed approaches become increasingly rare.
What Actually Matters
The 2025 Magic Quadrant documents a market undergoing fundamental restructuring. But focusing on vendor positions obscures the more important question: how should organizations approach analytics tool selection in this environment?
Success requires moving beyond feature comparisons to understand how tools fit within existing workflows and enhance human capabilities. The winning platforms won’t necessarily be those with the most features or the most sophisticated AI. They’ll be those that reduce friction between question and answer, between insight and action.
This might mean a broad, sprawling platform for some organizations. For others, specialized tools like Sigma or carefully orchestrated open-source solutions will deliver better outcomes. The answer depends entirely on your specific context, existing investments and organizational capabilities.
We offer initial complimentary solution architecture sessions to help organizations navigate these decisions. Or if you have a complex environment, we have entire engagements built around solving these problems. Our approach draws on experience across hundreds of implementations, without vendor allegiance. Because ultimately, the most sophisticated analytics platform means nothing if it doesn’t serve the people who need it.
