Data Management: Page

How to control agentic AI access to enterprise data

AI agents stand in for users and reach multiple systems. Governing their access requires distinct identities, scoped authority and records that show who authorized each action.

By Sean Kerner • Sept. 21, 2026

Successful AI deployment rests on strong data foundation

Just as BI was built on a bedrock of high-quality, relevant data, AI tools are only as trustworthy as the data that informs them.

By Eric Avidon • Sept. 21, 2026

Data storytelling techniques can help sell governance efforts

In addition to explaining analytics results, data storytelling can help sell business executives on the need for strong data governance and management programs.

By Craig Stedman • Sept. 18, 2026

Does setting data guardrails slow innovation?

Governance and speed are treated as opposites, but the real variable is design. Four control domains determine whether data guardrails help or hinder.

By Mostafa Ibrahim • Sept. 17, 2026

New features position Alation’s AIOS as AI management layer

Lineage tracing in AI Governance and governed access to unstructured data advance the platform and keep the vendor in step with what competitors are offering.

By Eric Avidon • Sept. 17, 2026

Agentic data management starts taking shape as a tech option

Data management agents could help data teams boost efficiency and AI readiness, but leaders must address governance and find low-risk starting points to build expertise.

By John Moore • Sept. 16, 2026

Neo4j intros graph-based suite for fighting financial crime

The vendor is adding tooling from its GraphAware acquisition to go beyond fraud detection and differentiate itself from those providing only part of the crime-stopping ecosystem.

By Eric Avidon • Sept. 16, 2026

Why AI should build on, not copy, your data stack

The model might be the face of AI, but the data architecture performs the heavy lifting. Its strength determines the consistency and reliability of AI-generated results at scale.

By Bill Schmarzo • Sept. 15, 2026

Lakebase: Another vendor buzzword or a useful new technology?

Two vendors now offer lakebase platforms that blend data lakes and databases. Like the data lakehouse, it's a buzzy-sounding mashup with mainstream potential.

By Donald Farmer • Sept. 11, 2026

Alteryx evolving to become connective tissue for AI

New features such as an MCP server and a studio for transforming datasets into agents aid the vendor's transformation and help distinguish itself from competing providers.

By Eric Avidon • Sept. 11, 2026

Cloudera partners with Mistral to bring AI to users’ data

The integration enables Cloudera customers to keep data secure when building and deploying agents and helps differentiate the vendor from some of its competitors.

By Eric Avidon • Sept. 10, 2026

Databricks launches model to boost data retrieval for AI

Adding sequential search to improve the speed and accuracy of responses to complex queries evolves existing capabilities and keeps Databricks in step with competitors.

By Eric Avidon • Sept. 10, 2026

Data mesh success hinges on hybrid implementation

Data mesh is settling into a hybrid model as enterprises balance domain ownership with shared controls, cross-domain analytics and AI requirements at scale.

By George Lawton • Sept. 4, 2026

PayPal exec maps a path to adopting shift-left data governance

Moving data governance upstream can help build trusted data for users and AI. PayPal governance leader Brandy O'Shields explains how to adopt a shift-left approach.

By Craig Stedman • Sept. 3, 2026

Boomi intros layer for controlling AI costs and connections

The Agent Control Plane combines previously disparate capabilities in one environment to help users better observe and command their fleets of agents.

By Eric Avidon • Sept. 2, 2026

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.

By Eric Avidon • Sept. 1, 2026

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.

By John Moore • Aug. 28, 2026

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.

By Damon Garn • Aug. 26, 2026

AI doesn’t have a trust problem. Your data does.

The AI auditability gap isn't an AI problem. It's decades of data management debt, finally coming due, exposed by autonomous decision-making.

By Bill Schmarzo • Aug. 25, 2026

AWS emphasizing database security as AI powers new threats

While the tech giant has always focused intently on protecting proprietary data, its database security capabilities are taking on new importance as agents increase vulnerabilities.

By Eric Avidon • Aug. 24, 2026

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.

By John Moore • Aug. 21, 2026

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.

By Eric Avidon • Aug. 19, 2026

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.

By Scott Thompson • Aug. 19, 2026

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.

By Sean Kerner • Aug. 18, 2026

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.

By Stephen Catanzano • Aug. 18, 2026