Anyone else spend January canceling subscriptions you forgot you had?
This time of year, many of us take stock of what’s working, what we’re holding onto out of habit, and what needs to go. It’s a familiar reset: fewer things, but ones that hold up.
That’s the mindset behind this month’s content: moving away from one-off fixes toward systems and approaches that last. From data strategy roadmaps that stand the test of time to AI-assisted solutions that ease day-to-day work, each piece focuses on reducing rework and helping teams move faster on a solid foundation.
If you’re tired of starting over every time priorities shift, this one’s for you.
Below you'll find:
Hot Topic: Closing the AI readiness gap: from research to live panel discussion
Our Recommended Read: Get your data strategy roadmap adopted
Emerging Tech Insights: Two platform shifts shaping AI and analytics workflows
From the field: What a 7-week internal migration taught us
Taking AI Readiness from Research to Live Panel Discussion
AI shows up on nearly every data roadmap, yet most organizations still struggle to see meaningful ROI. The issue isn’t funding. It’s whether the underlying data stack can support AI in production, at scale, with trust.
We’re teaming up with ThoughtSpot and dbt Labs to break down how leading organizations design their data stacks to move from AI experimentation to measurable impact.
You’re invited:
How AI-Leading Orgs Build Their Data Stack Differently Tuesday, Feb. 10 at 11 AM CT
The discussion will focus on:
What separates AI ambition from AI readiness
The core components of an AI-ready data stack, including architecture, integration, governance, and enablement
Where AI-leading teams focus technology investment and where they intentionally do not
This session is designed to move beyond experimentation and into decisions that support real business impact.
📖 Get Your Data Strategy Roadmap Adopted Across the Org
Most data roadmaps fail for a simple reason. They look good in slides but don’t translate to practical application. This piece shows you how to build a roadmap leaders will fund, teams will follow, and the organization will keep using as priorities shift.
What we break down:
How to frame your roadmap so executives fund your outcomes
Ways to give departments visibility into what's coming, when, and what you need from them
How to account for capacity and skill gaps in the roadmap
How to keep your roadmap relevant, not a one-time artifact
Bonus: You also get access to the Data Strategy Roadmap Review Template, a quarterly check-in tool that helps you assess impact, adoption, readiness, and alignment before things drift.
Why it matters: This removes friction from common data and operational workflows that don’t translate well to text alone. Instead of re-prompting or switching tools, users can explore data, review documents, and monitor systems in place, making AI-assisted workflows more practical, usable, and production-ready.
Databricks Makes ADBC the Default for New Power BI Connections
Why it matters: This shifts Power BI data access onto a columnar, Arrow-native path, reducing serialization overhead and improving reliability when moving data into semantic models. For teams hitting performance limits or refresh instability with ODBC, this is a structural improvement, not just a speed boost.
From the Field
What a 7-Week Internal Migration Taught Us
We recently completed an internal data warehouse migration in record time – a project that encompassed five major migrations in under seven weeks.
Scope of the migration:
Ingestion: GCP → Databricks native
Orchestration: Google Workflows → Databricks Jobs
Transformation: Google Dataform → dbt Labs (dbt Fusion)
Warehouse: BigQuery → Databricks
User-managed data: Google Sheets → Sigma Data Apps
Two things made this possible:
1) Our AI-assisted MCP approach handled large portions of the migration safely and repeatably. That freed the data team to spend time validating results, handling edge cases, and ensuring outcomes were correct, not just fast.
2) We committed to a data stack we stand behind – Sigma, dbt, and Databricks – and applied it consistently.
What to learn from this: If a migration depends on heroics, it won’t scale. Speed comes from reducing manual work and ambiguity, not from pushing teams harder. AI-powered solutions paired with a strong infrastructure is how you get there.
