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Data Primacy: Why Government's AI Ambitions Run Through Its Data Architecture
Bill Wright, Head of Government Affairs at Everpure, says federal agencies keep calling data their most valuable asset while their systems still treat it as a byproduct of individual applications.
Part 1: What Changes When AI Begins to Act?
Identity sprawl, unpredictable workload patterns, and autonomous systems that can outrun their own guardrails. At Everpure's Accelerate, financial-services infrastructure leaders laid out five areas where agentic AI is forcing a rethink.
Part 2: What Keeps a Promising Banking AI Demo From Making It Into Production?
An Everpure-hosted panel featuring Options Technology VP Andrea Moccia and financial services infrastructure leaders laid out the recurring failure patterns that trap AI pilots in permanent purgatory, and the architectural decisions that let a handful of firms break through.
Future of Data Management
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Restore Testing Gives Database Teams Proof Their Recovery Plan Works
Mariyan Clement, Database Architect at IBM, explains why monitoring a backup job proves nothing about recovery, and why he destroyed his own database to find out what a restore really involves.Software Efficiency Gives Infrastructure Teams Another Lever Beyond Adding Capacity
Janne Kalliola, Founder of Coonti, argues that software waste compounds across the technology stack and becomes more costly as AI scales.
As AI Uncouples Data From Applications, Storage Becomes The Foundation For Sovereign AI
Prakash Darji, GM of Digital Experience at Everpure, on why data primacy and corporate sovereignty are the new foundations of enterprise architecture.Small Modular Reactors Turn Data Center Operators Into Owners Of Their Own Generation
Husam Mohammed, MEP Design Director at Jacobs, on why the campuses being designed now are expected to produce their own power, water, and cooling on site.

Tokenized Finance Turns Data Quality Into A Real-Time Systemic Risk Control
Grace Wu, former Head of Product and Account Data Asset at HSBC, explains why stablecoins and tokenized assets are forcing financial institutions to redesign enterprise data architecture around real-time trust, governance, and accountability.
Why Capital Markets Firms Are Rethinking Data Infrastructure for Production AI
Everpure's Mike Russo and Richard Galvez on the orchestration, governance, and resilience gaps that separate AI experimentation from production in capital markets.Kubernetes Becomes The Enterprise Standard As Storage Catches Up To AI Demand
Greg Muscarella, GM of Portworx at Everpure, explains why storage, not compute or networking, is the constraint holding enterprises back as Kubernetes absorbs virtualized, containerized, and AI workloads onto a single platform.
Part 2: What Keeps a Promising Banking AI Demo From Making It Into Production?
An Everpure-hosted panel featuring Options Technology VP Andrea Moccia and financial services infrastructure leaders laid out the recurring failure patterns that trap AI pilots in permanent purgatory, and the architectural decisions that let a handful of firms break through.
AI Is Still Waiting for the Clean Data That BI Never Got
Avon's Head of Applied AI and Business Value has watched enterprises promise to replace people with tools once already. A decade on, the data underneath still isn't ready, and now the tools act on it without a human in the middle.
Mapping Data Movement Before Buying Hardware Keeps Inference Spend On The Real Constraint
Nagarjun Rajendran, Cloud and Technology Solutions Manager at Bosch Digital, shares why GPU utilization alone cannot tell a team what is limiting its production inference.
AI Agents Earn Autonomy Process By Process As Companies Verify The Data Behind Them
As more AI systems act without real-time oversight, leaders are weighing what a wrong decision would cost before handing over more work.
Part 1: What Changes When AI Begins to Act?
Identity sprawl, unpredictable workload patterns, and autonomous systems that can outrun their own guardrails. At Everpure's Accelerate, financial-services infrastructure leaders laid out five areas where agentic AI is forcing a rethink.Leadership
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When Models Disagree, Causal Testing Helps Data Leaders See Which Results Will Hold At Scale
Emily Nightingale, Director of Marketing Data Science at The Knot Worldwide, on how disagreement between sources becomes its own decision-grade signal.
Utilities Can Meet Much Of AI's Power Demand By Upgrading Infrastructure They Already Have
Brendan Andrews, Vice President of Energy and Infrastructure at Bureau Veritas, on why upgrading existing infrastructure beats waiting a decade for new capacity.
Smart Building Growth Pushes Tech Leaders To Separate Sensor Traffic From Business-Critical Systems
Peter Wilson, Chief Technology Architect at Fujitsu, shares why sensor data pays off only when it answers a cost, space, or productivity decision the business already faces.
The Smartest AI Business Cases Put Freed Capacity Back to Work
IBM AI strategy leader Prashant Parida joins the Data Wire podcast to explore what companies can create with the capacity AI releases.
America Won’t Win the AI Race Without Data Center Efficiency
Bill Wright, Head of Government Affairs at Everpure, makes the case for AI-ready data infrastructure that supports national innovation without burdening taxpayers.
Green Dashboards, Red Outcomes: How XLAs Fix The Gap Between SLA Compliance and Business Success
Paul Neuman, Founder and Head of GTM Services at IT Services Demystified, explains why the industry's most common performance metric is also its most misleading, and what it takes to close the gap between technical delivery and business outcomes.
Enterprises Are Deploying Agents Like Software. They Should Be Onboarding Them Like Employees.
Daniel Davenport and William duPont of Applied Identities explain why treating agents like software instead of employees leaves companies with generic tools that fail to reflect their strategy, values, or competitive differentiation.Cyber Resilience
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Why AI Integrated Through Vendor APIs and Cloud Ecosystems Amplifies Every Form of Enterprise Risk
Kay Kimberg Fonguh, a technology risk and control specialist, argues that AI embedded through third-party vendors, APIs, and foundation model providers materially amplifies model risk, cyber risk, concentration risk, and decision-making opacity.
Everpure CTO Says Disaster Recovery Alone Leaves Cyber Resilience Exposed
Andrew Stone, CTO for the Americas at Everpure, outlines why treating cyber recovery like disaster recovery leaves organizations dangerously exposed and how to build a truly resilient strategy for modern threats.How AI Attacks are Driving CISOs to Swap Old Prevention Tactics for Resilient Data and Provable Recovery
Rick Orloff, VP and CISO at Everpure, shares how embedding security in core data infrastructure gives enterprises control when attacks inevitably break through.
Zero Trust Becomes a Modernization Strategy As Leaders Move Beyond Compliance Thinking
Gerald Caron, VP of Cybersecurity at RIVA Solutions, on how his federal experience shaped a view of Zero Trust as a modernization and operating model challenge.
Inside the Shift Toward Internal Data Governance As Global AI Regulation Fragments
As global AI rules diverge, DataRep Non-Executive Director Onur Korucu explains why effective governance now depends on understanding data flows and infrastructure.
How Leaders Are Re-Mapping Security Boundaries As Companies Trade Proprietary Data For AI Gains
Sohail Iqbal, Chief Information Security Officer at Veracode, explains how intentional data sharing for AI value is dismantling perimeter security and redefining the human role as governance over trust.
A CEO & CISO's Joint Strategy for Managing Tension Between Innovation and Security
Rayson Technologies CISO Andrew Wylie and CEO Jason Pugh explain how to manage AI risk with a hybrid approach that pairs human intuition with intelligent, innovative technology.Most Popular
AI
Data Primacy: Why Government's AI Ambitions Run Through Its Data Architecture
AI
Part 1: What Changes When AI Begins to Act?
AI
Part 2: What Keeps a Promising Banking AI Demo From Making It Into Production?
LEADERSHIP
When Models Disagree, Causal Testing Helps Data Leaders See Which Results Will Hold At Scale
AI
AI Is Still Waiting for the Clean Data That BI Never Got
AI
Mapping Data Movement Before Buying Hardware Keeps Inference Spend On The Real Constraint
LEADERSHIP
