Opinion
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The hidden data costs missing from AI budgets
Operational work to shore up a weak data foundation can distort the economics of AI projects. Bringing these costs to light helps clarify ROI and what to prioritize.
Bill Schmarzo • Oct. 2, 2026 -
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Why entry-level data jobs are still critical in the AI age
AI shouldn't eliminate entry-level data jobs. Shifting junior roles to AI auditing and management helps organizations develop the next generation of data leaders.
Donald Farmer • Sept. 25, 2026 -
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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.
Mostafa Ibrahim • Sept. 17, 2026 -
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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.
Bill Schmarzo • Sept. 15, 2026 -
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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.
Damon Garn • Aug. 26, 2026 -
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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.
Bill Schmarzo • Aug. 25, 2026 -
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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.
Stephen Catanzano • Aug. 18, 2026 -
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Why AI for BI works for reports, but not platform building
AI makes generating BI reports simple, which makes platform-building look easier than it is. BI leaders should weigh the long-term commitment before deciding to build.
Sireesha Pulipati • Aug. 14, 2026 -
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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.
Bill Schmarzo • July 31, 2026 -
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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.
Stephen Catanzano • July 24, 2026 -
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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.
Zac Amos • July 23, 2026 -
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Your AI isn’t failing -- your metrics are
AI doesn't know what matters to your organization. It only knows what the business measures -- and weak metrics become problems when AI treats them as the goal.
Bill Schmarzo • July 21, 2026 -
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Will AI replace data analysts: A year and a half later
Organizations should foster and fund -- not terminate -- entry-level positions in favor of AI. Junior talent is key to the long-term success of AI agents.
Donald Farmer • July 17, 2026 -
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AI agent failures offer hidden value for enterprises
Most agent pilots will never reach production, but these failures can prove useful to data operations. Organizations should look to the 2010s mobile app boom for guidance.
Stephen Catanzano • July 1, 2026 -
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Securing data against AI attacks can’t be a side project
The same AI capabilities that power enterprise innovation also give attackers machine-speed access to database vulnerabilities, demanding a shift in how organizations protect data.
Stephen Catanzano • June 30, 2026 -
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AI agents push enterprises toward unified data governance
Enterprises facing data sprawl and growing risks from autonomous agents need unified governance and runtime controls to manage agent access and actions they can take.
Stephen Catanzano • June 30, 2026 -
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Data modeling is having a moment, and AI is the reason
Data modeling defines how enterprise data is structured and governed, and it has become the foundation for trustworthy AI, cloud migration and compliance.
Stephen Catanzano • June 23, 2026 -
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Why the rush to replace dashboards with AI is a mistake
AI-generated answers are getting better fast, but dashboards still do something AI can't, even as the line between them blurs more every day.
Sireesha Pulipati • June 9, 2026 -
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Context is the make-or-break layer for AI in production
Your AI model might not be the reason it fails in production. Missing or unclear context throws off the model, leading to untrustworthy outputs that the business can't act on.
Stephen Catanzano • June 4, 2026 -
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Data observability for AI helps curb poor model performance
Data quality issues get amplified in AI applications. Models can produce confident -- but misleading -- forecasts and conclusions without observability safeguards.
Stephen Catanzano • May 26, 2026 -
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Tracing data lineage in AI systems
Data lineage records how data moves through AI pipelines, turning model debugging, impact analysis and audits into queries rather than manual investigations.
Stephen Catanzano • May 18, 2026 -
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Understanding data contracts for AI projects
AI models fail silently when upstream data shifts. Data contracts prevent this by making schema, semantics and quality a binding agreement between producers and consumers.
Stephen Catanzano • May 13, 2026 -
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Why enterprise AI stalls between pilot and production
Most enterprise AI programs stall between pilot and production. Closing the operationalization gap requires trusted data, built-in governance and open interoperability.
Stephen Catanzano • April 23, 2026 -
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Converged architecture is enterprise AI’s missing foundation
Fragmented AI infrastructure fails in production. Converged data architecture with unified governance offers the path to reliable, scalable enterprise AI.
Stephen Catanzano • April 13, 2026 -
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Will agentic AI transform enterprise disaster recovery?
Guest contributor Catalin Voicu argues that the hesitation around agentic AI in disaster recovery isn’t about capability, it’s about trust and accountability.
Catalin Voicu • April 7, 2026
