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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. Continue Reading

• 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. Continue Reading

• Modern IT threats demand continuous resilience

  Continuous resilience enables organizations to anticipate, withstand and recover from disruption. Modernize today to reduce operational risk and improve business agility. Continue Reading

• 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. Continue Reading

• 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. Continue Reading

• 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.Continue Reading

• 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.Continue Reading

• AI data fabric emerges as a governance layer for agents

  The latest take on data fabric architecture promises to help AI agents coexist with existing platforms, but there's some assembly required.Continue Reading

• How agentic AI amplifies data management challenges

  AI agents pose new data management challenges and magnify familiar ones. To build a solid agentic foundation, data leaders must plan for these critical challenges.Continue Reading

• How governance as code controls AI agent risk

  As more stringent EU AI Act requirements take effect, enterprises need runtime guardrails that control autonomous agents' actions, govern data access and preserve audit evidence.Continue Reading

• How to use AI to enforce data governance policies

  AI models and agents can automate data governance policy enforcement for proactive governance in dynamic data environments. But don't forget the human element.Continue Reading

• 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.Continue Reading

• Why Apache Iceberg is the center of attention in data platforms

  In a Q&A, consultant Donald Farmer explains why vendors are rushing to support Apache Iceberg and discusses its capabilities and deployment issues for data teams.Continue Reading

• 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.Continue Reading

• Treat HIPAA backup rules as infrastructure, not decorations

  Healthcare backup systems designed for recovery and retrofitted for HIPAA produce audit gaps. Encryption, access logging and retention belong in the architecture from the start.Continue Reading

• Why a dashboard audit matters before BI cleanup

  Managing large BI estates takes more than deleting stale dashboards. To keep order, leaders need to assess analytics usage and assign ownership to prevent clutter from returning.Continue Reading

• Why backup alone doesn't guarantee cloud recovery readiness

  Successful backup jobs can still leave dependency and configuration gaps, making orchestration and recovery testing critical for restoring cloud services and SaaS apps.Continue Reading

• Big data integration techniques and best practices to adopt

  Data integration in big data systems is even more complex now because of AI. To succeed, it requires a strategy built on new approaches and strong data management.Continue Reading

• Verify backup data integrity to reduce recovery risks

  Validation is an essential part of data backups. Test and confirm that your backup data is intact and usable now, rather than discovering problems during a recovery.Continue Reading

• 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.Continue Reading

• How dashboard sprawl challenges upend enterprise analytics

  The proliferation of dashboards, coupled with conflicting data definitions, exposes governance issues in organizations and reduces the ROI of analytics investments.Continue Reading

• How to build a business impact analysis checklist

  A business impact analysis is a critical part of disaster recovery planning. Avoid potential disruptions and smooth out the planning process with this BIA checklist.Continue Reading

• Data sovereignty expands beyond compliance boundaries

  Geopolitical conflict and outages can upend assumptions about where data is controlled and accessed, pushing leaders to plan for jurisdictional risk, resilience and recovery.Continue Reading

• How to plan business continuity activities, with a template

  Many activities comprise a business continuity plan. The better they are managed, the more successful the overall business continuity program will be.Continue Reading

• Data governance metrics: Measure success, identify issues

  Implementing a data governance program isn't enough. Data leaders also need to track and analyze various metrics to evaluate its effectiveness and address shortcomings.Continue Reading

• Govern citizen development to avoid data pipeline downtime

  Low-code/no-code and vibe coding require data leaders to shift from gatekeepers to architects of trust to maintain data integrity and keep pipelines running reliably.Continue Reading

• Resilience strategies for an AI-powered era

  Resilience is not an optional feature of an AI strategy, it should be the foundation. Incorporate risk mitigation early on to scale AI securely while keeping up with competition.Continue Reading

• Why leadership should be involved in data backup policies

  While IT administrators lead the day-to-day operations of data backups, business leaders have a key role to play in shaping their organization's backup policy.Continue Reading

• How agentic AI governance tackles data, security challenges

  AI agents promise real gains -- and pose real risks. Enterprises that move fast without first tightening governance controls might struggle to prevent rogue behavior.Continue Reading

• How to develop a data governance strategy: 7 key steps

  A strong data governance strategy enables more effective data use and helps prevent financial, legal and reputational problems. Follow these steps to develop one.Continue Reading

• The data ownership blind spots putting organizations at risk

  Organizations can't claim to have good data governance when they still have unowned data. Assigning ownership to siloed and dark data is critical to enterprise success.Continue Reading

• Controlling data sprawl requires governance discipline

  Data sprawl drives higher infrastructure costs, expands security exposure and weakens compliance controls as data proliferates faster than governance can scale.Continue Reading

• What downtime and data loss really cost the business

  Downtime in the organization spotlights revenue and operational risk. Business leaders can use cost estimates to focus recovery spending where outages cause the greatest loss.Continue Reading

• How business leaders can make a data-literate culture stick

  Data literacy is an ongoing, interactive process. With executive support, a data-literate culture eases backlogs, improves AI outcomes and fosters better decision-making.Continue Reading

• 6 key components of a successful data strategy

  These six elements are essential parts of an enterprise data strategy that will help meet business needs for information when paired with a solid data architecture.Continue Reading

• Why maximum tolerable downtime is a key business metric

  Downtime cannot be eliminated, but it can be governed. By defining maximum tolerable downtime, data leaders align recovery planning with business priorities and risk tolerance.Continue Reading

• Build a data literacy program to fit company needs

  Want better decisions? Invest in a role-based data literacy program that aligns training to workflows, builds trust in metrics and improves execution on business goals.Continue Reading

• Data domain ownership, data mesh chart path to AI-ready data

  That AI initiative won't get off the ground without timely, reliable data. Bringing technology and team practices into sync reduces delays and boosts data readiness.Continue Reading

• The database is the new battleground for enterprise AI

  Agentic AI puts new pressure on enterprise databases, exposing gaps in the data access, security enforcement and tooling fragmentation that block production deployment.Continue Reading

• Q&A: The gap between AI ambitions and data readiness

  Ataccama CEO Mike McKee discusses why most organizations aren't ready for AI, why ROI failures are largely a people problem and what the data management industry keeps overlooking.Continue Reading

• Data lineage documentation matters for enterprise reliability

  Data lineage is critical to enterprise success. Tracing a data asset's journey through pipelines helps improve data quality, speeds up corrections and builds trust in analytics.Continue Reading

• The new standards for cyber-resilient backup

  Learn about the role of backup resilience as a frontline security control and how IT leaders can prepare their organizations to mitigate risk and meet compliance needs.Continue Reading

• Data lakehouse ransomware recovery strategies for AI

  Attackers target the metadata your data intelligence platform needs to function. Understand where your AI risks are and what it takes to restore service after an incident.Continue Reading

• Build trust on a federated governance model

  Follow this practical blueprint to adopt a modern data governance approach that aligns people, processes and platform to deliver measurable results from AI across the business.Continue Reading

• ITGC audit checklist: 6 controls you need to address

  Assess the risks to your IT operations and company infrastructure with an IT general controls audit. Explore six controls to audit and key steps with this free checklist.Continue Reading

• Why agentic AI demands both structured and unstructured data

  Agentic AI must access both structured and unstructured data to reason effectively. Converging these data types is the defining operational challenge for enterprise AI in 2026.Continue Reading

• 9 data analytics biases and how executives can address them

  Analytics can exhibit biases that affect the bottom line or cause reputational damage through discrimination. It's important to address those biases before problems arise.Continue Reading

• Key requirements for data and analytics governance platforms

  Successful governance platforms require business and IT alignment, user-friendly design and scalable architecture to tie governance to measurable outcomes.Continue Reading

• Data governance responsibilities now belong in the C-suite

  To improve business outcomes, leadership must move beyond IT controls and adopt a playbook that treats data as a shared enterprise asset with clear roles and policies.Continue Reading

• AI data governance guidance that gets you to the finish line

  As organizations dive into AI adoption, many realize the first real bottleneck is not the model but how to prepare their information so it can be used effectively in AI workflows.Continue Reading

• How executives can build a responsible AI framework

  Building a responsible AI framework requires governance policies, accountability structures, compliant infrastructure and clear metrics to ensure AI systems operate as intended.Continue Reading

• Build a business intelligence team to optimize data use

  Leaders who want to protect data investments must build a strategic business intelligence team with five core roles: the expert, architect, designer, analyst and data steward.Continue Reading

• Operational resilience is a benchmark for executive success

  Operational resilience is emerging as an executive benchmark as regulations, board scrutiny and compliance mandates drive the need for measurable KPIs and accountability.Continue Reading

• Experts share practices to overcome AI data readiness

  Enterprise AI ambitions can stumble without strong data fundamentals. Here's how to pick, organize and maintain data so systems behave reliably and projects move to production.Continue Reading

• Data and AI governance must team up for AI to succeed

  AI applications won't produce reliable results -- and could create compliance and business ethics risks -- without strong data governance processes underpinning them.Continue Reading

• 6 ways to use AI in IT disaster recovery

  AI is everywhere these days, and disaster recovery is no different. IT teams can use AI to mitigate, prevent and recover from disruptions faster than traditional methods.Continue Reading

• Improving business forecasting with synthetic data and simulation modeling

  Synthetic data and simulation forecasting help executives overcome data constraints, test scenarios and strengthen strategic decision-making under uncertainty.Continue Reading

• How to secure sensitive data when offboarding employees

  Establish a multi-phased approach that turns a risky situation into a managed process with several departments working in coordination to ensure a smooth exit.Continue Reading

• The future of AI depends on better data, not bigger models

  AI's competitive advantage is shifting from model scale to data quality. Organizations that invest in governance and infrastructure build more reliable, defensible systems.Continue Reading

• How to cut data loss risks when employees leave

  How an employee leaves influences what data they might take. Organizations can implement several practical methods to protect code, client information, and other assets.Continue Reading

• What executives look for in a data quality platform

  Data quality strategy now functions as a governance and risk discipline, with executives weighing metrics, ROI accountability and data trust as indicators of enterprise reliability.Continue Reading

• Residual access failures put data at risk

  Stop breaches that start with orphaned accounts. This guide identifies offboarding weak spots and gives leaders a practical checklist to close gaps fast and prove compliance.Continue Reading

• Ways to protect data platforms from turnover risk

  Departing data employees can take valuable Institutional knowledge. Protect it with consistent documentation, a central repository and tested backups to keep systems stable.Continue Reading

• Building a strong data analytics platform architecture

  Data analytics platforms are crucial for information-driven enterprises. With the right architecture, organizations can gain meaningful insights and gain a competitive edge.Continue Reading

• How to prevent data loss: 4 strategies for better data protection

  Preventing data loss doesn't start and end with better data backups. An organization's culture, IT strategy and choice of tools all play important roles.Continue Reading

• Modernized big data architecture a must for AI to deliver

  Many enterprises put AI into production in 2025 but found their legacy data stacks stalled progress. See what it takes to modernize big data systems for better AI results.Continue Reading

• The trust-at-speed paradox: Most data governance wasn't built for AI

  Agentic AI operates autonomously, exposing gaps in governance, data quality and accountability. Executives must address these limits to manage risk and move AI into production.Continue Reading

• 8 ways to use AI for data backup

  Recognize AI-based data backups as a strategic imperative that propels your IT team forward by protecting critical business assets while providing compliance and cost optimization.Continue Reading

• Modern data architectures as a risk management strategy

  As organizations modernize their data systems, architecture choices will determine how risk is governed, disruptions are absorbed, and regulatory obligations are managed over time.Continue Reading

• 12 enterprise cloud backup services to consider in 2026

  These cloud-based backup products offer a variety of features, such as AI and enhanced security measures, to help enterprises reduce risk and meet their compliance needs.Continue Reading

• How to avoid data loss in a natural disaster

  Protection from environmental disruptions requires more than simple backups. This guide shows how fortified infrastructure, robust cloud strategy and risk analysis ensure uptime.Continue Reading

• How data lineage became a boardroom metric

  Data lineage has moved beyond a technical function, becoming a board-level signal of how well organizations govern, audit and explain their data across complex environments.Continue Reading

• The top 2026 data conferences to plan enterprise strategy

  This guide lists events that can help data leaders assess how AI fits into their current architecture and identify improvements needed to meet future data demands.Continue Reading

• Top trends in big data for enterprises in 2026

  As AI systems mature, organizations must evaluate models, infrastructure and governance frameworks that balance cost, compliance and performance this year and beyond.Continue Reading

• 2026 will be the year data becomes truly intelligent

  As AI moves into production, enterprises are redefining data management around shared meaning, operational trust and system coherence rather than standalone capabilities.Continue Reading

• How AI changes data governance roles and responsibilities

  Data governance is a team effort, though recent AI developments have caused data and AI teams to converge. As a result, organizations have created new roles in these teams.Continue Reading

• How to build a data catalog: 10 key steps

  A data catalog helps business and analytics users explore data assets, find relevant data and understand what it means. Here are 10 important steps for building one.Continue Reading

• 10 dashboard design principles and best practices

  Dashboards are a key tool for delivering analytics data to business users. Here's how BI teams can design effective dashboards to help drive informed decision-making.Continue Reading

• Why data semantics matters for context-aware systems

  Data semantics organizes context, relationships and logic across enterprise data to create systems that understand how information connects and informs decision-making.Continue Reading

• How AI governance manages risk at scale for enterprises

  Effective oversight of AI systems requires more than technology. It relies on defined roles, coordinated risk protocols and tight collaboration across data and model teams.Continue Reading

• 6 essential data engineer skills for modern data environments

  As AI automates more tasks handled by data engineers, the role is shifting from pipeline building to strategic skills that keep modern architectures adaptable.Continue Reading

• Data contracts help build trustworthy data products for AI

  Data contracts establish clear expectations between data producers and consumers, turning governance into a continuous, automated process that builds trust for AI.Continue Reading

• How to deploy Data Duplication on Windows Server

  Data deduplication is necessary to stay ahead of storage demands and maintain available space. Windows Server Data Deduplication is one tool that can help.Continue Reading

• Best practices for using simulation models in business

  Simulation models provide businesses with a framework for forecasting and strategy through tested practices in finance, healthcare and logistics.Continue Reading

• 5 knowledge graph use cases in data fabric architecture

  Knowledge graphs in data fabrics enable semantic layers, cross-domain modeling, operational intelligence, enterprise search and intelligent metadata management.Continue Reading

• Cisco and Splunk are teaching AI to anticipate system failures

  Cisco and Splunk use machine data to train a new time-series foundation model that surfaces hidden issues and creates a durable competitive edge.Continue Reading

• The race to build the ultimate data platform

  Unified data platforms are driven by AI demands and efficiency goals to replace fragmented tools and enhance governance across enterprises.Continue Reading

• 5 steps for sustainable data management

  Data management practices that embrace sustainability principles can streamline data processes, improve data protection and reduce energy consumption.Continue Reading

• Inline deduplication vs. post-processing deduplication

  Will inline or post-processing deduplication work better for your data protection strategy? Learn more about each method with this tip.Continue Reading

• Real-time edge analytics use cases for business

  Real-time edge analytics use cases in manufacturing, logistics, healthcare and retail show how localized processing balances latency, compliance and integration.Continue Reading

• Using simulation forecasting in business analytics

  Simulation forecasting allows organizations to explore future scenarios, strengthen planning efforts and improve outcomes through advanced modeling techniques.Continue Reading

• Enterprise data governance: Frameworks and best practices

  Data backup and recovery depend on a solid governance framework that includes procedures for data management, stewardship, quality monitoring, protection, security and compliance.Continue Reading

• How AI-powered governance enables scalable AI deployment

  AI-powered governance tools help organizations move AI from trials to production by automating compliance, mitigating risks and safeguarding brand reputation.Continue Reading

• Image-based vs. file-based backup: Key comparisons

  Image-based backups protect entire systems with single files, while file-based backups offer granular protection. Most organizations benefit from implementing both approaches.Continue Reading

• Building a power outage business continuity plan: Step by step

  Loss of electric power presents a major risk to business continuity, and no organization is immune. Take these steps to create a solid business continuity plan for power outages.Continue Reading

• 12 enterprise data backup challenges and how to overcome them

  The virtues of backing up and securing data are well founded, but getting there is no easy feat. Storage capacity, floods of data and infrastructure costs are among the pitfalls.Continue Reading

• 7 predictive analytics skills to improve simulation modeling

  Predictive analytics skills such as statistical analysis, data preprocessing and model evaluation can help data professionals build more accurate, dynamic simulation models.Continue Reading

• How data for AI is changing the modern data platform

  Data platforms must evolve to meet AI requirements, with a greater focus on real-time integration, unified governance, infrastructure flexibility and data quality.Continue Reading

• Enterprise data platforms adapt for GenAI and agentic AI

  Generative and agentic AI are redefining enterprise data strategy as platforms evolve to support new demands for quality, access and governance.Continue Reading

• Implement a business continuity plan for remote workers

  Business continuity efforts don't stop when employees go remote. Learn how to create and manage a reliable business continuity plan for remote workers.Continue Reading