Data Management: Page

AI pressures expose outdated data pipeline architecture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

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.

By George Lawton • Aug. 18, 2026

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.

By Eric Avidon • Aug. 18, 2026

Where data teams fall short on governance for the EU AI Act

Most enterprises govern AI in siloed, employee-tied pockets, but the EU AI Act assumes formalized enterprise-wide governance. That gap drives the risk data teams now face.

By Jelani Harper • Aug. 17, 2026

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.

By Eric Avidon • Aug. 14, 2026

Databricks’ Electric acquisition adds embeddable PostgreSQL

Flush with funding, the data management and AI vendor continues to make purchases that add capabilities aimed at aiding users building and deploying agents.

By Eric Avidon • Aug. 13, 2026

MongoDB intros latest features to fuel AI development

Unifying frequently disparate capabilities such as vector embedding, data retrieval and agent workflows helps distinguish the vendor amid a crowded field of data and AI providers.

By Eric Avidon • Aug. 13, 2026

Data mesh success depends on more than architecture

The technological aspect is only part of the move toward decentralized data management. Adoption can stall when teams resist new responsibilities and cross-domain ways of working.

By Mary Pratt • Aug. 12, 2026

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.

By Bill Schmarzo • July 31, 2026

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.

By Sean Kerner • July 31, 2026

York’s data governance program eyes capabilities, not control

York University has focused its data governance program more on solving data quality, access and trust issues for end users than on controlling their data use.

By Craig Stedman • July 30, 2026

Is your data infrastructure ready for the zettabyte era?

AI initiatives are driving demand for curated, controlled data. Leaders must move beyond capacity planning and build the governance and quality practices that make data usable.

By Brien Posey • July 29, 2026

The CDO’s new role is curating context for data governance

Agentic AI demands that CDOs move beyond cataloging to curating the context AI systems retrieve, creating a new accountability layer for data governance.

By Donald Farmer • July 29, 2026

Nimble launches agents to cull relevant web data for AI

The vendor's Web Search Agents autonomously learn domain-specific use cases to help enterprises collect contextually relevant information for AI and analytics.

By Eric Avidon • July 29, 2026

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.

By Eric Avidon • July 29, 2026

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.

By Eric Avidon • July 28, 2026

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.

By Eric Avidon • July 27, 2026

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.

By Chris Tozzi • July 24, 2026

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.

By Stephen Catanzano • July 24, 2026

The 5 pillars of data observability

Data observability provides complete oversight of an organization's data pipeline. Use the 5 pillars of data observability to ensure efficient, accurate data operations.

By Erin Sullivan, Peter Spotts • July 24, 2026

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.

By Eric Avidon • July 23, 2026

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.

By Zac Amos • July 23, 2026

8 proactive steps to build trusted data for analytics and AI

Trusted data is even more critical as AI use increases. These eight steps will help data leaders build a strong foundation for effective analytics and AI applications.

By Anne Marie Smith, Ph.D. • July 23, 2026

CDO challenges that hinder data-driven initiatives

Chief data officers must turn AI ambition into measurable value. These eight issues show where data strategies break down -- and how leaders can respond.

By Mary Pratt • July 22, 2026