Evaluate

Evaluate

Weigh the pros and cons of technologies, products and projects you are considering.

• 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. Continue Reading

• 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. Continue Reading

• How to get reliable BI insights from AI-augmented analytics

  Before adopting AI-powered BI applications, organizations need stronger business context, trusted workflows and users prepared to assess AI outputs. Continue Reading

• 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. Continue Reading

• 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. 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

• 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

• How AI is changing data protection

  There's no doubt that recent AI developments have affected data protection. IT leaders must not only stay on top of these changes but also prepare for what's next.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

• Data ontologies are foundational for usable AI outputs

  Data ontologies strengthen AI outputs with governed definitions across platforms and departments, helping data leaders boost trust and improve decision-making.Continue Reading

• What a leadership transition reveals about your data strategy

  The failure pattern of a leadership transition reveals what data strategy institutionalization requires. Without it, a data strategy framework is a dependency, not a strategy.Continue Reading

• Unstructured data is the bottleneck for agentic AI success

  Ungoverned, unstructured data blocks enterprises from scaling agentic AI. Contracts, transcripts and internal documents hold the context agents need but can't yet reach.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

• 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

• Geopolitics reshape data protection plans

  Business and technology leaders are revising their data protection plans as global conflicts challenge current resilience and risk management plans.Continue Reading

• Tableau, Qlik in flux: What it means for BI users amid AI shift

  Ongoing transitions at Tableau and Qlik highlight how the rise of AI-driven analytics is changing the roles and technology needs of data analysts -- and the BI market.Continue Reading

• Upskilling is key to building AI-driven data teams

  External hires and consultants can help, but strengthening the current data team for AI work often leads to quicker progress, lower costs and better retention.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

• Which data management costs should survive budget cuts?

  Tighter budgets force hard choices about data governance. This breakdown of essential data management costs shows where to cut spending and where cuts create risk.Continue Reading

• 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.Continue Reading

• Why enterprise AI depends on the semantic layer

  Semantic layers are moving from BI tools into the core analytics stack as AI agents query enterprise data, requiring governed definitions for consistent interpretation.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

• Choosing the right AWS disaster recovery strategy in 2026

  Organizations unprepared for outages often have a disaster recovery plan that does not match their business needs. These metrics can help leaders choose the best AWS strategy.Continue Reading

• Compare AI risk management frameworks from NIST and beyond

  While NIST is the most widely used, it's just one of the frameworks available today that can help manage the risks associated with AI.Continue Reading

• Why AI forces security-first governance

  AI systems fail quietly through drift, biased outputs and degraded judgment. A security-first governance approach gives leaders the visibility and continuous control to scale AI safely.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

• AI orchestration modernizes approaches to disaster recovery

  As ransomware gets more dangerous and infrastructure grows more complex, AI-assisted DR can shorten recovery time by improving readiness and decision-making under pressure.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

• 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

• Build a data governance team that delivers results

  Regulations, AI and jumbled implementation oversight weaken decision-making. A dedicated team can help bring structure, accountability and consistent outcomes.Continue Reading

• How do risk assessment costs vary and why?

  Risk assessments help identify and, more importantly, prioritize activities an organization needs to address its most serious threats and vulnerabilities. However, costs may vary.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

• Reconsider the AI readiness gap in data and analytics

  The AI readiness gap is widely framed as temporary, but examining it across three distinct enterprise layers suggests it might be more permanent than the market suggests.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

• Hybrid search demands reshape retrieval frameworks for AI

  As AI workloads mature, enterprises face multiple data platform choices to improve search and retrieval capabilities while meeting governance and operational demands.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

• AI in business intelligence: How to manage it effectively

  AI tools are becoming a key part of BI systems, both to streamline tasks and add new analytics capabilities. Here's how to successfully integrate AI into BI processes.Continue Reading

• Cloud vs. local backup: Which is right for your organization?

  Cloud vs. local backup is an important discussion for IT leaders today. The cloud backup market is soaring, but traditional local backups also have much to offer.Continue Reading

• Data governance for AI requires a cross-functional approach

  AI systems create risks that span data, security and model integrity. A cross-functional governance model distributes ownership without creating silos.Continue Reading

• AI, analytics push data-in-use protection up priority list

  As sensitive data moves into AI pipelines, organizations must evaluate how to protect it during processing and what safeguards IT platforms provide for data in use.Continue Reading

• 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.Continue Reading

• Why enterprise AI initiatives fail without governance

  Enterprise AI pilots succeed in controlled conditions but collapse when scaling exposes the accountability, ownership and explainability gaps that governance was meant to prevent.Continue Reading

• Top data preparation challenges and how to overcome them

  Data preparation is a crucial but complex part of analytics and AI applications. Don't let these seven common challenges send your data prep processes off track.Continue Reading

• Strengthen cyber resilience by shifting to a modern DR model

  RTO, RPO and uh-oh. Rushed restores can reinfect systems. Update outdated DR practices to avoid these issues and focus resources on the systems that matter most to the business.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

• 5 backup monitoring tools for better data protection

  Do you know where your backups are? Check out this list of cross-platform backup monitoring tools that provide insight into backup performance and health.Continue Reading

• 12 top open source databases for enterprise use

  Open source databases offer viable alternatives to proprietary systems. This guide offers 12 popular options across relational, NoSQL and source-available technologies.Continue Reading

• 8 benefits of using big data for businesses

  Big data is a valuable resource for improving business processes and driving innovation. Here are eight ways big data applications benefit companies.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

• 15 top data catalog software tools to consider using in 2026

  Organizations can use numerous tools to build and manage data catalogs. Here are 15 prominent ones that data leaders should consider for their data management needs.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

• 10 predictive analytics platforms for enterprises in 2026

  The leading products have evolved into autonomous ecosystems that use AI agents and natural language to accommodate users at every skill level and accelerate decision-making.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

• 18 top big data tools and technologies to know about in 2026

  Numerous tools are available to use in big data applications. Here are 18 popular open source big data technologies, with details on their key features and use cases.Continue Reading

• Why you should be using backup monitoring software

  To protect data, creating backups is not enough anymore. Backup monitoring software can help make sure those backups are ready to use, compliant with regulations and meeting SLAs.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

• 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

• How cyber insurance requirements reshape backup architecture

  From immutable backups to air-gapped copies, these requirements reflect the growing need for organizations to prove they can recover from an incident -- or risk losing coverage.Continue Reading

• Should you use data backups to train AI models?

  AI models must be trained on high quality, complete data. A lot of it. Backups are a treasure trove of this type of data, but are they ready to train AI?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

• 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

• 18 data science tools to consider using in 2026

  Numerous tools are available for data science applications. Read about 18, including their features, capabilities and uses, to see if they fit your analytics needs.Continue Reading

• SaaS shared responsibility model: What vendors don't cover

  Don't let your organization lose cloud data to misplaced trust and thin vendor protections. Learn what providers don't cover and how to build backups that pass audits.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

• Why ethical use of data is so important to enterprises

  Enterprises that don't use data ethically have a lot to lose. To maintain their businesses' trustworthiness and value, executives must craft a comprehensive, transparent strategy.Continue Reading

• Backup environment evaluation: Verify before you buy

  As backups move toward integrated data protection, this guide explains why recovery speed, environment‑wide visibility and maturing AI features should guide platform choices.Continue Reading

• Choose an enterprise backup architecture that fits risk

  Weigh the trade‑offs among on‑premises, backup as a service and hybrid backup, and use a clear framework to choose the approach that fits your organization.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

• Data Technologies outlook on 2026: The year of data is here

  Data will be the backbone of success in 2026. As AI reshapes data management, organizations must balance innovation with ethics, governance and high-quality data to thrive.Continue Reading

• The future of business intelligence: 10 top trends in 2026

  Here are 10 key trends affecting the current state and future direction of BI initiatives that analytics leaders should be aware of. No surprise: AI use is among them.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

• 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

• Data science applications across industries in 2026

  Industries like healthcare, retail and finance use data science applications to improve diagnostics, optimize operations, forecast trends and prevent fraud.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

• Data architecture vs. information architecture: How they differ

  Data architecture and information architecture are distinct but related disciplines that work in tandem to support an enterprise's data and business strategies.Continue Reading

• Top data protection software vendors for business in 2026

  Data compromises carry real financial risk to organizations. It's imperative to invest in the right multifunctional data protection software that ensures security.Continue Reading

• Different types of database management systems explained

  The various types of database software come with advantages, limitations and optimal uses that prospective buyers should be aware of before choosing a DBMS.Continue Reading

• 9 backup as a service (BaaS) providers in 2026

  BaaS is available in public, private and hybrid varieties and from numerous vendors. Here's how to evaluate the options to find a service that meets your organization's needs.Continue Reading

• 9 examples of business intelligence use cases for companies

  BI tools and applications can help improve decision-making, strategic planning and other business functions. Here's a look at nine top BI use cases for organizations.Continue Reading

• 15 common data science techniques to know and use

  Data scientists use statistical and analytical techniques to analyze data sets. Here are 15 popular classification, regression and clustering methods.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

• 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

• 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

• How AI is transforming data recovery for the modern era

  Today's data recovery tools integrate AI models to analyze file systems, data structures, historical patterns and emerging threats to improve storage, protection and restoration.Continue Reading

• 12 leading courses in data backup training for IT teams

  Data backup training covers key aspects of data protection that are essential for compliance and risk mitigation. Here are 12 programs that build enterprise competencies.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

• The future of quantum data centers: Resilience and risk

  The quantum outlook calls for close ties with classical computing, even as it tops standard IT in some use cases. Businesses can benefit but must address post-quantum security.Continue Reading

• Top data quality management tools in 2025

  Data quality management tools provide profiling, cleansing and monitoring features that keep enterprise data accurate and consistent across analytics and governance efforts.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

• The cost of downtime and how businesses can avoid it

  Disrupted operations cost businesses billions of dollars annually. Disaster planning, cyber resilience and monitoring system dependencies can help limit the damage.Continue Reading

• Hadoop vs. Spark for modern data pipelines

  Hadoop and Spark differ in architecture, performance, scalability, cost and deployment. They offer distinct strengths for modern cloud-native data pipelines.Continue Reading

• What makes an effective data science team structure?

  Data science team structures vary in strength, and their success depends on how roles and leadership align with business goals to meet analytics needs.Continue Reading

• Business continuity in the cloud: Benefits, issues and tips

  Using the cloud for business continuity helps reduce downtime, increase redundancy and simplify disaster recovery plans. Learn how to implement it with this tip.Continue Reading

• Build IT resilience to avoid paying ransomware demands

  No one wants to pay the ransom after a cyberattack, but many organizations feel like they have no choice. Explore the benefits of investing in resilience over making payments.Continue Reading