Blog:
Thought Leadership

Updated March 12, 2026

Gartner opened the Data & Analytics Summit 2026 with a memorable metaphor. The keynote framed the current moment as a raft ride through a roaring, treacherous river. The river represented AI. Fast-moving. Powerful. Difficult to control. The message was clear.…

Updated March 5, 2026

I've been in the BI and data space long enough to recognize when something fundamental shifts. Denny Lee and I first worked together when he was at Microsoft on SQL Server Analysis Services (SSAS), and I was a customer building…

Updated February 25, 2026

Most enterprise AI conversations still center on prompts, whether through chat interfaces, copilots, or question-answer workflows. But the highest-value AI agents don’t wait for someone to ask a question. They monitor signals, detect thresholds, and trigger actions. These are headless…

Updated February 20, 2026

Many teams assume they’re doing agentic analytics because they support text-to-SQL or chat-based BI. And while conversational BI does change how questions are asked, it doesn’t go so far as to change how decisions are made. It’s like transitioning from…

Updated February 17, 2026

Conversational BI pilots succeed because they operate in constrained environments. A dozen users, curated datasets, predictable query patterns. When AI agents enter the picture, everything breaks. Gartner forecasts that more than 40% of agentic AI projects will be abandoned by…

Updated February 3, 2026

Most enterprise AI projects follow a familiar pattern. Successful demos lead to promising pilots. Executives commit budgets to scale the initiative across departments. Then the wheels come off. Despite $30-40B in enterprise investment in GenAI, MIT’s Project NANDA found roughly…

Updated January 27, 2026

2026 is shaping up to be the year enterprises finally accept that semantic layers are no longer optional. As organizations scale AI initiatives and rationalize their analytics stacks, a familiar problem keeps resurfacing. The same business question produces different answers…

Updated January 15, 2026

2025 wasn't just another year of AI hype. It was the year enterprise data shortcomings became visible at scale. Multiple industry studies show that most AI initiatives stall before production or fail to scale. MIT’s Project NANDA found that roughly…

Updated January 6, 2026

As 2025 comes to a close, one thing is clear: the experimentation phase of GenAI in data and analytics is ending. In its place, enterprises are entering a more pragmatic era focused on trust, governance, economics, and tangible business outcomes.…

Updated January 6, 2026

Last year, I made a set of predictions for data and analytics trends for 2025: "The Future of Data & Analytics: Trends to Look For." Now that the year has wrapped, it’s time to put those forecasts to the test…

Updated December 22, 2025

I've been building analytics software for more than two decades. I've watched waves of innovation promise to democratize analytics, including self-service BI, cloud warehouses, and natural language query systems. Each wave delivered value, but also exposed new challenges. We're now…

Updated December 10, 2025

How do you move from experimental AI pilots to production systems you can trust? That was the central question Kyle Morton and I discussed on last month’s EDM Association webinar.  The data management community is keenly aware of what’s at…