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Qdrant builds dataset to benchmark vector retrieval at scale
With AI tools requiring massive volumes of vectors to perform properly, the public dataset is designed to help organizations test their systems in real-world conditions.
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As AI evolves, Tableau talks direction ahead of Dreamforce
A knowledge layer and headless analytics capabilities show that the vendor is changing to meet the needs of its users as AI reduces the need for traditional BI.
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Root out hidden data debt to improve AI decision-making
AI-driven decisions can expose enterprise weaknesses, but organizations that address debt in several key areas could unlock $18 trillion in value.
Manage
Shift-left governance brings data controls upstream for AI
AI systems move too fast for data governance that waits until the end. Enterprises are replacing outdated review methods with earlier oversight and automated controls.
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Why anonymization alone no longer protects sensitive data
Anonymized data is still valuable, but organizations that rely on it alone can expose their business and customers to privacy, regulatory and reputational risk.
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Why more data will not deliver AI data readiness
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AI pressures expose outdated data pipeline architecture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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Problem Solve
Shift-left governance brings data controls upstream for AI
AI systems move too fast for data governance that waits until the end. Enterprises are replacing outdated review methods with earlier oversight and automated controls.
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AI value comes from continuous learning, not automation
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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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Data contracts gain urgency as AI tests data foundations
Unreliable data can undercut AI efforts before they deliver meaningful returns. Data contracts help clarify accountability and move quality controls upstream.
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How CDOs can show the business impact of data
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What AI-native analytics startups offer that BI vendors don't
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Modern IT threats demand continuous resilience
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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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Data Management
Qdrant builds dataset to benchmark vector retrieval at scale
With AI tools requiring massive volumes of vectors to perform properly, the public dataset is designed to help organizations test their systems in real-world conditions.
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Business Analytics
As AI evolves, Tableau talks direction ahead of Dreamforce
A knowledge layer and headless analytics capabilities show that the vendor is changing to meet the needs of its users as AI reduces the need for traditional BI.
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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.
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)?