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If activation feels fuzzy in your company, you’re not alone. In fact, Rodrigo Fernandez has seen the same pattern across hundreds of SaaS businesses: growth teams get handed “increase activation,” but no one actually owns what activation means, how it’s defined, or how it’s measured. And when activation isn’t owned, it becomes a committee decision. It turns into noise. And your product data stops being useful.

The Model Context Protocol (MCP) is reshaping business intelligence. It provides the technical standard for a new class of generative BI tools that let you talk to your data. The engine behind this revolution is the MCP server—the essential component that connects AI models (like Claude or Cursor) to a company’s data. This article examines Tableau’s official MCP server vs. Databox MCP to help you decide between a traditional BI add-on and an AI-native headless platform.

2025 was one of our biggest years yet. We introduced powerful new capabilities that make it easier for anyone on your team to access the data they need, uncover insights, and share insights across the business —so decisions can happen faster and with more confidence. Here are this year’s top highlights.

AI answer engines are becoming a default step in B2B discovery. Instead of scanning ten blue links, buyers now ask one complex question in ChatGPT, Gemini, Perplexity, or Google AI Overviews — and get a single synthesized recommendation. If your brand isn’t part of those answers, you risk disappearing at the exact moment buyers decide. Databox’s latest research shows this shift is already happening.

Every business uses its own unique stack of tools to run marketing, sales, customer support, finance, operations, and more. And while each tool plays an important role, the data they generate stays spread across systems that don’t “talk” to each other well: While Databox already connects to 130+ of the most popular tools, some teams need access to a broader range of integrations and more flexibility to prepare their data for analysis.

It’s annual planning season! Across the company, leaders are setting targets, scoping new initiatives, and shaping the roadmap for the year ahead. But if you’ve been through this before, you know how quickly plans lose relevance. Goals that looked clear on paper become harder to measure in practice, teams lose sight of the bigger picture, and the assumptions you made about resources and impact rarely hold up.

Being effective is not just about output. It describes how a team works together to deliver results that actually matter. At Databox, we define effective teams as ones that are clear on expectations, aligned on goals, and confident in how their work connects to the bigger picture. Sounds good in theory, right? But how do we actually achieve it?

When we first introduced Teams in Databox, the goal was to give every team a “home base”, a shared space where they could collaborate around the metrics, Databoards, and Reports that mattered most. Executives could monitor company-wide performance at a glance. Team leaders could use it to organize dashboards around key initiatives and keep their teams aligned. And individual contributors could quickly access the data most relevant to their role. For many of you, that was the case.

OKRs were designed to help companies turn strategy into execution. In practice, though, that’s not always the case. When I first started working on the OKRs feature at Databox, I interviewed customers to understand how they apply OKRs and similar frameworks in their companies today.

Volatility is the new default. Uncertainty rivals pandemic-era chaos. Go-to-market leaders are stuck between long-term planning cycles and short-term fire drills. That’s a lot of bad news… but Kathleen Booth, SVP of Marketing & Growth at Pavilion, says it doesn’t have to be that way.