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Potential consequences are severe when AI agents lack context
When connected with relevant context, agents can increase organizational efficiency and reduce spending. When left to guess, they can cause significant harm.
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Data management vendors race to connect AI with context
With agents requiring situational awareness to be accurate, connecting AI with relevant data and business logic has been an almost singular focus throughout 2026.
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Context is king as agents evolve, but problems persist
Awareness enabled by appropriate data and business logic is one of the differences between agentic applications that properly perform and those that never reach production.
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How data mesh supports AI-ready data architectures
Data mesh can improve data access for AI initiatives by decentralizing ownership and governance, but it doesn't solve every data challenge.
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Data science and analytics News
Qlik launches data engineering tools to aid AI development
New capabilities, such as data quality agents and a feature that makes data products more reusable, support engineers to help organizations more easily achieve their AI goals.
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Database Management News
Couchbase evolution continues with new data layer for AI
New capabilities including Agent Memory and extension to edge devices help the vendor compete for market share as it grows beyond its database roots.
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Data Warehousing News
Latest AWS data management features target cost control
As the volume and complexity of enterprise data estates increase, and the size of data workloads grows due to AI development, the tech giant aims to help users reduce spending.
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Data Management Strategies News
Data management vendors race to connect AI with context
With agents requiring situational awareness to be accurate, connecting AI with relevant data and business logic has been an almost singular focus throughout 2026.
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Data Integration Manage
AI doesn't need an unstructured data dump -- move what matters
Bulk data migration pushes out AI timelines. Data leaders should invert the process: classify and catalog first, then move only relevant content to reduce delays and costs.
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Data Governance Evaluate
Data governance fails without CFO ownership
Without CFO ownership, data governance stays underfunded and unenforceable. Finance leadership ties governance to audit controls and budget accountability.
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How data mesh supports AI-ready data architectures
Data mesh can improve data access for AI initiatives by decentralizing ownership and governance, but it doesn't solve every data challenge.
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The 5 pillars of data observability
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Data governance fails without CFO ownership
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CDO challenges that hinder data-driven initiatives
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Problem Solve
AI doesn't need an unstructured data dump -- move what matters
Bulk data migration pushes out AI timelines. Data leaders should invert the process: classify and catalog first, then move only relevant content to reduce delays and costs.
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8 proactive steps to build trusted data for analytics and AI
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How governance as code controls AI agent risk
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AI agents push enterprises toward unified data governance
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Manage
How agentic AI amplifies data management challenges
AI agents pose new data management challenges and magnify familiar ones. To build a solid agentic foundation, data leaders must plan for these critical challenges.
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How to use AI to enforce data governance policies
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AI agent failures offer hidden value for enterprises
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Why Apache Iceberg is the center of attention in data platforms
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Data Management/Data Warehousing Basics
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Get Started
How agentic AI amplifies data management challenges
AI agents pose new data management challenges and magnify familiar ones. To build a solid agentic foundation, data leaders must plan for these critical challenges.
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Get Started
Data ontologies are foundational for usable AI outputs
Data ontologies strengthen AI outputs with governed definitions across platforms and departments, helping data leaders boost trust and improve decision-making.
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Get Started
How to use AI to enforce data governance policies
AI models and agents can automate data governance policy enforcement for proactive governance in dynamic data environments. But don't forget the human element.
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Data management strategies
Data management vendors race to connect AI with context
With agents requiring situational awareness to be accurate, connecting AI with relevant data and business logic has been an almost singular focus throughout 2026.
-
Data management strategies
Context is king as agents evolve, but problems persist
Awareness enabled by appropriate data and business logic is one of the differences between agentic applications that properly perform and those that never reach production.
-
Data management strategies
New Oracle AI database targets cost, data residency concerns
The tech giant's latest offering is a scaled-down version of its Exadata platform and helps differentiate its lineup of databases from those of competing providers.
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