Systems | Development | Analytics | API | Testing

A New Way to Run Pipeline Analysis #OnTheSpot

Jeffry Cape, our Senior GTM Enablement Manager, asked Spotter: "Look at our early stage pipeline generation performance from the dates of Shark Week in November and share what some of the best practices are". He wasn’t checking campaign stats—he was rewriting his daily workflow. A dashboard might tell you meetings are “up.” Spotter went further—running a hands-free deep dive on our global Shark Week program and surfacing what actually moves the needle on pipeline progression.

From Raw Data to Strategy #OnTheSpot in Under 3 Minutes

"I’ve worked in the analytics industry for 25 years... this thing makes me as powerful as any analyst I’ve ever met." When ThoughtSpot’s Tony Hammond handed a raw dataset to Spotter, our agentic AI analyst, he wasn't just running a demo—he was witnessing a with a complete transformation of the analytics workflow.

Data and AI Trends 2026: Predictions for Agentic AI Production

Agentic AI is moving quickly from experiments to real work. In 2026, it shows up inside the workflows that drive outcomes: decisions, operations, and accountability. In the season 7 premiere of the Data Chief podcast, host Cindi Howson sat down with three leaders who work at the intersection of AI ambition and enterprise execution: Paul Baier (GAI Insights), Jennifer Belissent (Snowflake), and Rory Blundell (Gravitee).

Build vs. Buy: Why Embedded Analytics is the Strategic Choice for Modern Data Leaders

For today’s CTOs and CIOs, the pressure to deliver actionable data insights within your products has never been higher. However, a critical dilemma often stalls your progress toward the business intelligence tools you need for the task: Should your engineering team build a bespoke analytics engine from scratch, or should you integrate a professional embedded solution?

The Future of AI in the Enterprise

As AI continues to rise in importance across all industries, the cost of implementation, readily available access to cloud computing, and practical business use cases make AI-powered offerings a competitive advantage for product managers, engineering, and data leaders. However, AI isn’t without its fair share of risks and challenges.

A Memory-centric Approach to System Strategy: 6 Takeaways from Supercomputing 2025

Artificial intelligence workloads are reshaping how memory is produced, priced, and prioritized. Not because the supply chain has fundamentally broken, but because manufacturers are making deliberate decisions about where to place capacity and capital. Wafer lines are being steered toward high-margin, long-term AI demand, not toward broad, undifferentiated expansion. HBM, advanced DRAM, and other AI-optimized memory now command the majority of investment and forward planning.

How to Use Databox MCP in Claude to Get Revenue Metrics

See the Databox Model Context Protocol (MCP) in action inside Claude. In this video, we demonstrate how to connect your business data to Claude AI to instantly audit your revenue metrics. Instead of navigating through multiple dashboards, we use the Databox MCP to: Stop guessing if your data is accurate. Start verifying it with Claude and Databox. About this series: This video is part of our "Chat with Your Data" series, where we explore the Databox MCP.

Supermetrics MCP vs. Databox MCP: Choosing Between Data Pipeline and Analytics Platform

If you’re evaluating MCP servers for your analytics stack, you’ve probably noticed that “MCP support” can mean very different things depending on the vendor. I’ve been working with both platforms, and the distinction matters more than most comparison articles let on. Supermetrics and Databox both offer MCP implementations, but they’re built for different jobs.