{"id":19486,"date":"2026-05-18T11:32:43","date_gmt":"2026-05-18T11:32:43","guid":{"rendered":"https:\/\/www.f5tci.com\/?p=19486"},"modified":"2026-05-18T11:32:43","modified_gmt":"2026-05-18T11:32:43","slug":"o-centro-do-bi-e-dos-dados-esta-a-mudar","status":"publish","type":"post","link":"https:\/\/www.f5tci.com\/en\/2026-05-18_o-centro-do-bi-e-dos-dados-esta-a-mudar\/","title":{"rendered":"Is the center of BI and data shifting?"},"content":{"rendered":"
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For years, the dashboard was the central metaphor of BI and data.<\/h5>\n

Building a good dashboard used to be a sign of analytical maturity. The platform was the product, and knowing how to navigate it was a valued skill.<\/p>\n

\u201cThe center of gravity in BI may now be starting to shift<\/strong> and probably not in the way most organizations expected. With the rise of the MCP<\/strong> (Model Context Protocol), the AI agents<\/strong> , and conversational interfaces, the most important question is no longer how we present data. The real question becomes: do we truly understand what our data means?<\/strong><\/p>\n

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The latest evolutions in platforms like Qlik Cloud<\/a><\/span> and Microsoft Fabric<\/a><\/span> reinforce the same trajectory. Qlik\u2019s MCP Server, Qlik Answers, its agentic experiences, and Microsoft\u2019s accelerating investment in Copilot, semantic models, and OneLake\u2011native agents signal a decisive shift in how users engage with enterprise data: away from manual navigation and toward contextual, natural\u2011language interactions built on governed semantic models.<\/p>\n

Something in this relationship is indeed changing.<\/strong>Not abruptly, but in a way that is structural enough to deserve the attention of any organization that invests seriously in analytics.<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div>

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What exactly are we talking about when we talk about MCP?<\/strong><\/h5>\n

MCP is not just another conversational interface on top of dashboards. It is an open protocol, adopted across virtually the entire relevant data ecosystem in 2025. It is not the bet of a single vendor. It is shared infrastructure, and that is precisely why the shift it introduces is structural, not incremental.<\/p>\n

The most significant shift may be something else entirely: the ability to consume analytical capabilities outside the BI platform itself. For years, access to enterprise data depended on dashboards, filters, and manual navigation. With MCP, AI agents can now query semantic models, business context, and governed data directly, without the user ever opening a traditional analytics tool.<\/p>\n

A manager can simply ask an assistant: \"What were last quarter\u2019s margins by region?\"<\/span><\/p>\n

And receive an answer built from semantic models, <\/span>certified metrics, <\/span>access permissions, <\/span>business context and <\/span>governance.\u00a0<\/span>Without opening dashboards or navigating interfaces.<\/p>\n

The user no longer needs to adapt their thinking to the structure of the tool, the system now interprets the context of the decision.<\/strong><\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div>

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What changes and what remains:<\/strong><\/h5>\n

Dashboards are not going away. But the most vulnerable segment appears to be what we might call \"administrative BI\": reports built to answer repetitive questions and pages filled with dozens of KPIs that are rarely consulted.<\/p>\n

When an agent can answer directly from governed data, part of that layer becomes redundant. Yet there are contexts where visualization remains extremely relevant. Operations, logistics, and retail teams still depend on immediate visual reading:<\/p>\n