Dave is the founder of AtScale and is the Chief Technology Officer. Prior to AtScale, he was VP of Engineering at Klout & at Yahoo! where he built the world’s largest multi-dimensional cube for BI on Hadoop. Mariani is a Big Data visionary & serial entrepreneur.
February 10, 2026
AI Governance Isn’t Theoretical Anymore: What Enterprise Leaders Are Actually AskingAI governance discussions used to center on frameworks and theoretical risk scenarios. This month’s TDWI webinar with Cal Al-Dhubaib, head of AI and data science at Further, revealed the many ways in which theory has moved to the practical. As…
February 3, 2026
Why Most Enterprise AI Projects Fail (and How to Make Them Work at Scale)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…
January 29, 2026
Why Context Became the Most Critical Layer in Enterprise AII recently sat down with Juan Sequeda, Principal Researcher at ServiceNow and co-author of Designing and Building Enterprise Knowledge Graphs, to discuss what's happening in enterprise data and AI. Listen to the full podcast episode here. Juan brings a unique…
January 27, 2026
Why AtScale Is Helping Shape the Open Semantic Interchange (OSI) Initiative2026 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…
January 21, 2026
The Hidden Cost of Semantic Drift in Enterprise AIThree teams ask the same question: "What was our gross margin last quarter?" Finance's dashboard shows it’s 30%. Sales’ margin reports show 32%. The AI copilot reports 31%. Which one's correct? Technically, all of them. Each system defines "gross margin"…
January 15, 2026
What Actually Changed in 2025 and Why It Redefined the Semantic Layer2025 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…
January 13, 2026
The Rise of LLM Agents in Data AnalyticsThe analytics world is changing. We have moved from static reports to self-service dashboards to conversational business intelligence (BI). Now, we're in the early days of LLM agents that don’t just answer questions but take action. They write SQL. They…
January 6, 2026
Data & Analytics Predictions for 2026As 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.…
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…
December 22, 2025
The Rise of AI Analysts and the Infrastructure They RequireI'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…
December 18, 2025
Why Snowflake’s Strategic Equity Investment in AtScale Validates the Open Semantic FutureI've spent more than two decades watching the data industry cycle through architectural shifts. Each wave promised to solve this fundamental challenge: delivering consistent, governed analytics that actually scale with the business. Most failed because they optimized for a single…
December 15, 2025
Universal Semantic Layer and AI Agents: What Enterprises Need to KnowHow can enterprises prepare for a world where AI agents, not just humans, are analyzing data and making recommendations? Every data and analytics leader is grappling with this question, and the answer lies in a universal semantic layer. In my…