Mantra wires your enterprise with AI-ready data — federated, contextualized, and intent-resolved — so any agent can deliver insights you can trust.
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Most AI investment in data and analytics is going to code generation — but Gartner's 2026 CDAO survey shows it's analytics use cases that deliver the highest ROI, with up to a 42% lift in business value.
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From building Mastercard's first data warehouse to betting early on Cloudera at Visa, serial CDO Andrew Clyne reflects on three decades of data leadership — and what AI changes next.
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Context engineering has become the defining discipline in enterprise AI. Here's a practical framework for the CDOs who have to build it — without rebuilding the data stack.
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Top 100 AI Leader Mano Mannoochahr shares lessons from John Deere, Travelers, and Verizon on why tractors prove automation creates more than it destroys, how petabytes of farm data revealed counterintuitive insights, and...
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Most of the context your AI needs already exists — it's just fragmented across systems, tools, and people's heads. Here's how to start connecting it without boiling the ocean.
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A knowledge graph maps what things are, a data catalog maps where things live, and a context graph maps how decisions work — and that difference changes everything AI can do with your data.
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Context graphs are emerging as the architectural layer that captures how your organization actually makes decisions — and they're quickly becoming the most important concept in enterprise AI.
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Former 3x CDO Kjersten Moody shares hard-won lessons from Unilever, State Farm, and Prudential on why thinking local unlocks global impact, how governance enables speed, and why AI is reshaping enterprise leadership....
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Why most “talk to your data” initiatives stall — and what it actually takes to break the metadata bottleneck and deliver production-grade, trustworthy AI analytics.
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AI accuracy doesn’t fail because models can’t write SQL — it fails because enterprises underestimate the cost and complexity of engineering business context at scale.
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AI initiatives aren't stalling because the technology isn't ready — they're stalling because most enterprise data architectures were designed for centralized warehouses and predictable questions, not distributed data...
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2026 is when AI pilots have to become production systems — here are the five infrastructure shifts that separate the companies who scale from those stuck explaining why their AI investments haven't delivered ROI.
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