If by the end of this year you can explain what ‘six seven’ means, you’re doing better than most of us.

2025 threw a lot our way. Some trends earned their momentum (like Model Context Protocol, unified semantic layers, and embracing open data formats). Others… remain unclear (see: skibidi).

Here’s the good news: building, delivering, and executing a data strategy is still easier than understanding Gen Alpha slang.

And on that note … may your 2026 be filled with data-driven opportunities and less explaining what ‘rizz’ means. 🍾

Below you'll find:

Our Recommended Read: This year's most read resources 

Hot Topic: dbt Fusion is reshaping orchestration  

Emerging Tech Insights:  MCP moves to the Linux Foundation, Snowflake backs an open semantic layer, & Databricks raises $4B+ to push deeper into AI

LOL Moment: It may hurt a little, but where's the lie?  

Let's get to it!

Tracey Doyle

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Our Recommended Read 

📖 This Year’s Most-Read Resources

We looked back at the most-read resources we shared in 2025. The common thread? You're being pushed to scale AI, modernize governance, and prove data strategy value.

These were the most viewed resources by your data peers this year:

1. Who owns governance? Data governance only works when ownership is clear. This blog breaks down who needs to own what and shows how to define roles that fit your org without overbuilding governance for governance’s sake.
2. Is your data AI-ready? Clean and cloud-based isn’t enough. This resource breaks down what AI-ready data looks like, where teams get stuck, and how to assess your readiness before AI-initiatives stall or quietly fail.
3. Where your peers are at with AI-readiness. Our exclusive research puts hard numbers behind the gap between AI ambition and reality, showing how your peers are (and aren’t) preparing their data for AI.
4. Data strategy, without the fluff. This blog outlines the five elements that turn strategy into action, from aligning to business goals to building a roadmap teams can execute and evolve as AI and analytics demands grow.
5. Do you have the right data team in place? This resource breaks down how to structure and scale a data analytics team, with clear guidance for data leaders on how to align talent to real business priorities. 

Hot Topic 

dbt Fusion Is Reshaping How Teams Think About Orchestration   

Our CTO Patrick Vinton joined dbt Labs to talk about what the new Fusion engine changes for data teams. The discussion focused on one area everyone feels, but few talk about openly: orchestration.

Patrick didn’t sugarcoat it:

👉 In most organizations, orchestration is the most brittle part of the data journey. It’s packed with institutional knowledge, difficult to maintain, and it forces engineers to spend as much time managing pipeline logic as they do building the actual data products.

Fusion’s state-aware orchestration flips that dynamic.

“Fusion lets us focus on meaningful business problems, not stitching code together,” Patrick notes. “The downstream impact is simple: fresher analytics for the people who need them.”

That shift frees data teams to spend more time modeling, testing, and delivering value, instead of babysitting pipelines.

For the full conversation, including where Patrick sees this heading next, watch the panel recording. 

Emerging Tech Insights 

A few of the tech updates on our radar: 

Model Context Protocol Moves to the Linux Foundation

Anthropic is donating the MCP to the newly formed Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation backed by Anthropic, OpenAI, Block, and major cloud and platform providers.

Why it matters: MCP shifting to neutral, community-led governance signals that agentic AI is maturing fast. Standards like MCP are becoming core infrastructure, not experimental glue code. For data and analytics teams, this reduces vendor lock-in risk and accelerates real-world adoption of AI agents that can safely interact with systems, tools, and data at scale.

Snowflake Backs an Open Semantic Standard for AI and BI

Snowflake has joined a broad group of data, analytics, and AI vendors to launch the Open Semantic Interchange (OSI) initiative. The goal is straightforward: define a shared, vendor-agnostic semantic standard so metrics, definitions, and business logic stay consistent across AI agents, BI tools, and analytics platforms.

Why it matters: As AI-driven analytics scales, inconsistent metrics and fragmented semantic layers become a hard blocker. OSI signals that vendors see semantics as shared infrastructure, not a competitive moat. For data teams, this could reduce rework, improve trust in AI outputs, and make true cross-tool analytics far more practical.

Databricks Raises $4B+ to Push Deeper into AI Applications

Databricks announced a $4B+ Series L investment, valuing the company at $134B, alongside continued strong growth and a sharper focus on building AI-driven applications on enterprise data.

Why it matters:
Databricks is making a clear bet that the next phase of analytics is application-led. By combining transactional data, user-facing apps, and agent orchestration inside a single platform, they’re reducing the friction between insight and action, and setting the stage for AI systems that can operate, not just report.

LOL Moment 

Where's the lie? 

We'll be back next year with more laughs! 😊 

Have a great week!

Tracey

Transform your business with data.

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