Opinion

Opinion

• 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.  Continue Reading

• 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

• 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

• 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.  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

• 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.  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

• 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.  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

• 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.  Continue Reading

• 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.  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

• 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.  Continue Reading

• 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.  Continue Reading

• 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.  Continue Reading

• 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.  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

• 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

• Real-time data streaming for AI: invest where it matters

  Don't let batch processing lead to missed opportunities. Build AI systems for continuous data flows that deliver instant decisions, change outcomes and justify the cost.  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

• 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

• 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

• 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

• 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

• Data intelligence isn't just a buzzword

  Data intelligence is more than a trend. It represents the future of how organizations will operate by connecting structured and unstructured data for smarter decisions.  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

• 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

• Is Apache Iceberg worth a full migration?

  Apache Iceberg delivers modern data lake features, but adoption depends on existing architecture, team resources and tolerance for migration complexity.  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

• Why Apache Iceberg is essential for modern data lakehouses

  Organizations adopt Apache Iceberg to build open data lakehouses that support high-performance analytics, multi-cloud strategies and warehouse-grade reliability.  Continue Reading

• Why data platforms matter for AI agents and MCP success

  AI agents and Model Context Protocol systems depend on fast, scalable access to diverse data. Modern data platforms deliver that access across fragmented enterprise environments.  Continue Reading

• Hybrid data management strategy for enterprise AI success

  Hybrid data management gives organizations the access, control and agility needed to support reliable and scalable enterprise AI across various environments.  Continue Reading

• Unified databases: A powerhouse behind generative AI success

  Unified databases support generative AI (GenAI) by integrating all data types into a single platform, streamlining infrastructure and improving scalability, speed and trust.  Continue Reading

• The importance of data products

  Treating data as a product enables organizations to turn raw information into actionable insights through intentional design, cultural alignment and AI-ready architecture.  Continue Reading

• AI's appetite for data is changing data requirements

  AI relies on high-quality real-time data. Companies that fail to modernize their data strategies risk falling behind, while those that adapt unlock AI's competitive advantage.  Continue Reading

• Turning data into a strategic advantage

  To transform data from a burden into an advantage, improve data management strategies, break down data silos, invest in analytics capabilities and build a data-driven culture.  Continue Reading

• Human oversight enables automated data governance

  Automating data governance makes tedious, time-consuming tasks more efficient. The human element remains critical to keeping automated systems in compliance with regulations.  Continue Reading

• Data management 2025 predictions: Bringing generative AI to enterprise data

  Organizations pursuing generative AI tools in 2025 will focus on making sure their data is AI-ready. The tools must work internally before consumers might see options next year.  Continue Reading

• Can Cohesity and Veritas define data protection's future?

  Moving forward, the combined firm's leadership will be under pressure to make good on its long-term vision.  Continue Reading

• Vectorized data uses see resurgence with generative AI

  Advancements in generative AI are driving renewed interest in vector databases. Organizations have also found new uses for the established technology.  Continue Reading

• You need analytics governance

  Analytics governance might not seem exciting, but it can improve innovation and mitigate risks. It's also critical to responsible data management and analytics practices.  Continue Reading

• What Veeam's backup support of Proxmox means for SMBs

  Proxmox has officially entered the virtualization platform conversation. The addition of Veeam backup support provides smaller organizations an alternative to pricier VMware tools.  Continue Reading

• Generative AI shines spotlight on data governance and trust

  Generative AI creates new opportunities for how organizations use data. Strong data governance is necessary to build trust in the data AI models use.  Continue Reading

• Data readiness unlocks the potential of AI

  AI models rely on data to function. Before implementing AI, make sure your data can support initiatives by evaluating its quality, accessibility, integration and governance.  Continue Reading

• Trusted data is the foundation of data-driven decisions, GenAI

  Any organization that wants to drive decision-making with data or use generative AI won't succeed without understanding how to cultivate trusted data.  Continue Reading

• Data management and governance key to successful AI use

  AI's effectiveness is limited by data quality. Building strong data management and governance programs are crucial to handling the challenges that AI presents to organizations.  Continue Reading

• Real-time analytics, vector search accelerate time to insights

  Real-time analytics and vector search capabilities reduce time to insights, enabling data-driven organizations to make faster decisions and generate value from their data.  Continue Reading

• Unlock the value of data through AI, modern data platforms

  Organizations looking to harness the power of generative AI to unlock data value should combine it with the modern data platform to maximize performance and efficiency.  Continue Reading

• Explore the benefits of AI for DataOps

  Enterprise Strategy Group research shows most organizations feel they need AI to unlock the full potential of DataOps and improve data pipeline performance.  Continue Reading

• Organizations identify DataOps as driver to improve data use

  An overwhelming majority of organizations report plans to increase DataOps spending. Continued investments target real-time, quality data-driven decision-making and insights.  Continue Reading

• Data protection and security: A marriage of necessity?

  The data protection market is fundamentally changing. An increased focus on cybersecurity and resilience efforts should shift how an organization views backup.  Continue Reading

• GenAI, Vector AI Search, multi-cloud top Oracle CloudWorld

  Oracle CEO paints a bright picture of the future with generative AI, shares integration plans and expands Microsoft partnership.  Continue Reading

• Security a top challenge in building a modern data platform

  Modern data platforms help manage large volumes of data and empower real-time decision-making. It starts with overcoming the top challenges organizations identify.  Continue Reading

• Data quality fuels analytics, AI

  Data quality is tied to organizations' ability to gain actionable data from analytics and AI processes, and orgs that implement data quality tools can make faster decisions.  Continue Reading

• Data management best practices key to generative AI success

  Generative AI can create data, empower decision-makers and innovate competitive advantages. Organizations need strong data management practices to use it effectively.  Continue Reading

• Grow data trust to avoid customer and corporate consequences

  A lack of data trust can undermine customer loyalty and corporate success. To avoid the consequences, understand the effects of poor data trust and learn key steps to build it.  Continue Reading

• 15 ways AI influences the data management landscape

  AI, NLP and machine learning advancements have become core to data management processes. Ask tool vendors how they use -- or fail to use -- AI in these 15 areas.  Continue Reading

• Cohesity's catalysts: AI, cyber resilience and partnerships

  Cohesity adds new AI capabilities to its data platform to improve cyber resilience and data management, while touting partnerships and responsible AI.  Continue Reading

• 6 ways Amazon Security Lake could boost security analytics

  Amazon's new security-focused data lake holds promise -- including possibly changing the economics around secure data storage.  Continue Reading

• How AI might change the data protection space

  AI is making waves throughout IT, including in backup and disaster recovery. Learn where AI is being used for data protection and where it might be headed.  Continue Reading

• Veeam aims to expand reach, bolster data security

  Veeam's user conference centered on cyber resilience and ransomware preparedness and prevention, with new partnerships and integrations that expand its platforms.  Continue Reading

• Caution: There are many ways to lose SaaS data

  As the number of organizations using SaaS apps grows, data loss becomes a larger concern. Unfortunately, there appears to be a lot of confusion about where that loss stems from.  Continue Reading

• Empower decision-making with real-time insights

  Using the right strategies is a crucial key to unlocking the benefits of real-time analytics and empowering organizations with agile data-driven decision-making.  Continue Reading

• Informatica World 2023: Cloud, data and AI together

  Informatica launched the generative AI Claire GPT product and plans to offer Intelligent Data Management Cloud as a Microsoft Azure Native ISV Service.  Continue Reading

• Modernizing a data warehouse for real-time decisions

  Updating a data warehouse to improve scalability, flexibility, security and speed is necessary to keep pace with real-time analytics demands.  Continue Reading

• QlikWorld 2023 recap: The future is bright for Qlik

  Qlik celebrated analytics advancements at QlikWorld 2023, highlighting its acquisition of Talend, the evolution of Qlik Cloud, efforts to build data trust and more.  Continue Reading

• No, magic backup people aren't protecting your SaaS data

  Are your SaaS-based applications protected? Organizations that rely on the SaaS vendor for backup and recovery face a tough road ahead. Don't skimp on internal backup measures.  Continue Reading

• Technology decisions and technical debt in data protection

  In this video, two expert analysts discuss digital transformation, IT spending and technical debt, as well as how these topics fit into a data protection strategy.  Continue Reading

• Data lakes: Key to the modern data management platform

  Data lakes influence the modern data management platform at all levels. Organizations can gain faster insights, save costs, improve governance and boost self-service data access.  Continue Reading

• What's all this talk about data mesh?

  Data mesh brings a variety of benefits to data management, but it also presents challenges if organizations don't have the right culture and infrastructure in place.  Continue Reading

• Data protection for SaaS-based apps is a work in progress

  Organizations with SaaS-based applications are still relying on the providers for data protection, even though the vendors are rarely responsible for SaaS data backups.  Continue Reading

• Avoid data sprawl with a single source of truth

  Enterprise Strategy Group research shows organizations are struggling with real-time data insights. A single source of truth can make operations more efficient and more accurate.  Continue Reading

• 3 data protection and governance predictions for 2023

  How should backup teams prepare for a new year? Data protection pros can adjust to a changing IT landscape by keeping an eye on ransomware, data governance and compliance.  Continue Reading

• ESG predicts 2023 shifts for DataOps, data management

  Organizations are using cloud technologies and DataOps to access real-time data insights and decision-making in 2023, according to Enterprise Strategy Group research.  Continue Reading

• Organizations capitalize on intelligent data management

  Intelligent data management concepts are opening new avenues for organizations to make better data-centric decisions and extract more value from their data.  Continue Reading

• AWS DataZone headlines AWS re:Invent 2022

  AWS DataZone will enable the sharing, search and discovery of data at scale with less risk. It is one of many data advancements announced at AWS re:Invent 2022.  Continue Reading

• Ransomware preparedness: The long road ahead

  Is your organization ready for ransomware? A recent survey shows that businesses in a variety of industries are all struggling with ransomware prevention and recovery.  Continue Reading

• Big Data London focuses on future of data-driven strategies

  At Big Data London, data quality and intelligence took center stage as companies strive for fast and efficient delivery of quality information -- and the vendors to make it happen.  Continue Reading

• The modern data platform drives data-centric organizations

  IT leads the modern data platform, functioning as the conduit, efficiently delivering the correct data to the right users, to empower data-driven decision-making in organizations.  Continue Reading

• SaaS data protection is a challenging but critical task

  Don't fall victim to a SaaS backup gap. Ensure critical data in cloud-based SaaS applications has proper protection, especially with increased cloud use.  Continue Reading

• Successful digital transformation strategies start with data

  Data has become a natural resource essential to business ops. Companies need to consider four digital transformation elements when developing strategies to harness data.  Continue Reading

• Data backup strategies in 2020 vs. 2025: What's going to change?

  Let's look to the future -- all the way to the year 2025. How will data backup evolve? How will sharp increases in data volume affect backup strategies?  Continue Reading

• 2020 data backup trends aim for faster, cheaper

  These impending data backup trends will provide better peace of mind for the reliability of recovery and help your organization use backups more efficiently.  Continue Reading

• Important questions about DRaaS you're not asking your vendor

  Not all DRaaS vendors are created equal. Expose any weaknesses and shortcomings in your current or prospective providers by asking the following questions.  Continue Reading

• Addressing the rising legacy-to-cloud migration need

  Despite a clear DR need for methods of migrating data from legacy systems into the cloud, the complexities that come along with it can be tough to handle without the right help.  Continue Reading

• Governance, compliance, ethics in data mining: Separate but equal

  In the ethical mining and analysis of data, governance, compliance and ethics are mistakenly taken as one in the same. Data managers need to be aware of the critical differences.  Continue Reading

• Hypervisor vendors upset virtual disaster recovery market

  Zerto and Veeam have conquered the DR market, but hypervisor vendors such as VMware and Nutanix have emerged, offering services that look to lower costs and provide better performance.  Continue Reading

• GDPR privacy concerns still brewing on law's first birthday

  The first year of the much-debated EU data protection rule was subdued. High-profile fines for privacy breaches have yet to come, but regulators are starting to take action.  Continue Reading

• Does multi-cloud backup have a future, or is it hype?

  Backing up to multiple clouds can benefit your backup and recovery in different ways. However, from other important data protection perspectives, it may not be as beneficial.  Continue Reading

• Data protection systems must address GDPR, ransomware concerns

  Backup products don't have a granular enough understanding of data to scan or analyze it effectively and fully meet today's privacy and security requirements.  Continue Reading

• Data backup security measures to take ahead of a hurricane

  In this firsthand look, IT and storage expert Brien Posey details how he secured data backup and other important system elements as Hurricane Florence approached.  Continue Reading

• What-if business planning simulation at its predictive best

  Simulating timely and accurate business scenarios can be an essential competitive weapon for predicting the performance, pitfalls and benefits of strategic initiatives.  Continue Reading

• Augmented analytics reveals the hidden side of things

  The rise of augmented analytics empowers businesses to find correlations in data that might very well be the critical difference in operational efficiency and cost savings.  Continue Reading

• 5 trends driving the big data evolution

  The speedy evolution of big data technologies is connected to five trends, including practical applications of machine learning and cheap, abundantly available compute resources.  Continue Reading

• Preventing ransomware attacks is a top storage vendor claim

  Storage vendors advance silly and not-so-silly claims about how data backup and protection technologies are the answer to ransomware prevention and growing ransomware fatalism.  Continue Reading

• A data replication strategy for all your disaster recovery needs

  Know your IT environment inside and out to choose the right data replication product for your organization's current and future disaster recovery requirements.  Continue Reading

• Disaster avoidance requirements ensure business continuity

  Include disaster recovery and avoidance requirements as design goals when creating storage and production environments to ensure business continuity during weather events.  Continue Reading

• What's new with data protection systems? Everything

  The next backup hardware you purchase will likely come with integrated backup software. That changes the buying dynamic and complicates the decision-making process.  Continue Reading