With the final stretch of the year underway, we’ve got a few data and AI insights worth your attention: what Gartner is forecasting for 2026, how MCP can finally make AI integral to your business, and tech updates from Databricks and Microsoft.
Plus, we’ve got a data-governance thriller for you — the kind of plot twist that rivals the cringe of Thanksgiving dinner debates!
Below you'll find:
Our Recommended Read: Gartner's outlook on the tech trends shaping 2026
Hot Topic: Why MCP is emerging as a real solution in operationalizing AI
Emerging Tech Insights: What's new in Databricks' IDE and SQL Server 2025
Data Myths Busted: The truth behind "we just need better AI tools"
LOL Moment: A governance-themed holiday 'thriller' that hits a little too close
Gartner’s latest rundown shows just how quickly the ground is shifting under data and tech teams. This list shows the themes data leaders are preparing for and how they’ll shape planning and resource decisions.
💡 What caught our attention:
AI is splintering into specialized components (supercomputing, multiagent systems, domain-specific models) and each one puts new demands on your data
Governance isn’t a side note anymore. Digital provenance, AI security, and preemptive cybersecurity are shaping how organizations architect their entire stack
Confidential computing and the shift toward local, controlled cloud environments signal tougher rules on where data lives and who can access it.
As we read through this piece, we recognized some of the same themes we found in our own research: the ambition to scale AI is high, but the underlying data foundation is where things either take off or stall out.
🔥 MCP is rapidly redefining how AI connects to the enterprise
Most teams trying to operationalize AI run into the same friction points: fragmented integrations, missing context, and models that can advise but can’t act. It slows everything down and forces you to maintain a stack of brittle connectors across tools that change every quarter.
MCP (Model Context Protocol) is Anthropic’s answer. It is an open standard that gives AI a consistent way to interact with enterprise systems. It’s not another vendor wrapper or proprietary add-on. It’s a protocol, and the design solves problems every data team knows well.
Here’s the value in plain terms:
One MCP server can expose your data, logic, and permitted actions in a controlled, transparent way
Any MCP-compatible AI tool can work through that layer without one-off integrations
The model securely gains context about your environment instead of starting from zero
And it can take real actions, not just generate instructions you still have to implement
For teams working in large, complex environments, this shift is meaningful. MCP reduces the integration burden that keeps AI stuck in prototype mode and makes operational use cases far more achievable, without forcing you to rebuild your entire architecture to get there.
Want to see what this looks like inside a real enterprise?
We’re hosting a session with EMC Insurance to walk through how they’re applying the Analytics8 MCP framework to operationalize AI and accelerate high-impact data initiatives.
And while not in the product yet, this setup clearly sets the stage for agentic data engineering, where an AI agent can assemble or extend pipelines from high-level business requests. A shift to watch.
SQL Server 2025 is generally Available
Microsoft announced the GA of SQL Server 2025, positioning it as an AI-ready enterprise database with major upgrades across performance, developer experience, and security.
What’s worth your attention:
AI built in with native vector support, semantic search, and model management directly in T-SQL
Developer-first improvements like native JSON, REST APIs, RegEx, fuzzy matching, and richer event streaming
Performance + reliability gains from optimized locking, improved tempdb governance, and enhanced failover
Cloud alignment through Fabric mirroring, Arc integration, and a unified SQL experience across on-prem and cloud
The throughline: Microsoft is turning SQL Server into a more flexible, AI-capable engine that can run consistently across environments, without forcing teams to overhaul their operational patterns.
Data Myths Busted
🛑 Myth: “We just need better AI tools!”
Teams assume the next model upgrade or platform shift will finally unlock AI value. But if your metrics don’t line up across systems, if no one can explain why two dashboards disagree, or if you’re still reconciling definitions by hand, a better model won’t fix that. It only makes the gaps more obvious.
✅ Reality Check: The real unlock is consistency. When lineage is clear, logic is documented, and data behaves the same way across your ecosystem, any AI tool performs better. You stop fighting foundational issues and start scaling work that moves the business. Tools accelerate impact, but only when the environment is ready for them.
LOL Moment
Coming to a theater hopefully nowhere near you.
Parental advisory required. Governance meetings contain strong language and unresolved conflicts.
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