New & Notable
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Why more data will not deliver AI data readiness
Volume-first data strategy served the BI era but falls short for agentic AI, where the constraint on readiness is context and governance rather than how much data exists.
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AI pressures expose outdated data pipeline architecture
Older pipelines may still move data, but AI workloads reveal whether they can withstand production demands, especially when agents act on data without human review.
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Five pressures that break AI programs after the pilot
Pilot-phase shortcuts leave enterprises with fragmented pipelines that cost too much to scale. Data foundations, rather than model capability, decide which programs reach production.
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7 expert recommended data observability tools
Commercial data observability tools can offer organizations pre-built components and plenty of vendor support for data use cases including monitoring, security and decision-making.
Find Solutions For Your Project
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Evaluate
Why more data will not deliver AI data readiness
Volume-first data strategy served the BI era but falls short for agentic AI, where the constraint on readiness is context and governance rather than how much data exists.
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AI pressures expose outdated data pipeline architecture
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Five pressures that break AI programs after the pilot
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7 expert recommended data observability tools
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Problem Solve
AI value comes from continuous learning, not automation
The companies pulling ahead in AI aren't doing it by deploying more tools. Rather, they're building the data foundations that make intelligence reusable, scalable and reliable.
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York's data governance program eyes capabilities, not control
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AI doesn't need an unstructured data dump -- move what matters
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8 proactive steps to build trusted data for analytics and AI
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Manage
What AI-native analytics startups offer that BI vendors don't
Traditional BI vendors face challenges as they try to transition into the AI era -- and now they're being targeted by AI-native startups unencumbered by legacy software.
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Modern IT threats demand continuous resilience
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CDO challenges that hinder data-driven initiatives
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Your AI isn't failing -- your metrics are
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Data Technologies Basics
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Integrate structured and unstructured data for better AI outputs
For AI agents to give the most accurate, informed responses, organizations must provide them with all the facts. Even those that are hidden in an old email or customer claim.
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Business resilience vs. business continuity: Key differences
Business continuity and resilience go hand in hand and play a role in an organization's disaster recovery plan. Essentially, business continuity is needed to achieve resilience.
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How to get reliable BI insights from AI-augmented analytics
Before adopting AI-powered BI applications, organizations need stronger business context, trusted workflows and users prepared to assess AI outputs.
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Enterprise backup as a service explained
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News
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Data Management
Data observability specialist Bigeye puts focus on AI spend
With agentic AI costing more than many organizations expected, the data observability specialist's latest alerts users when the expense of an agent suddenly changes.
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Data Management
Snowflake targets cost of AI with dynamic model routing
With spending on AI spiraling out of control for many organizations, the vendor's latest new feature automates model selection to balance cost and performance.
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Data Management
IPO inevitable for Databricks after adding $5B in funding
Experts note that the benefits of going public ultimately outweigh those of staying private, even with the vendor seemingly able to raise unlimited financing.
Data Technologies Definitions
- What is data visualization?
- What is customer data integration (CDI)?
- What is data restoration?
- What is data analytics (DA)?
- What is a backup storage device?
- What is off-site backup?
- What is an entity relationship diagram (ERD)?
- What is data as a service (DaaS)?