Features

Features

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• SLAs for disaster recovery: Free template and guide

  Download our free template to create a service-level agreement with the performance and response time requirements that disaster recovery plans demand.  Continue Reading

• 8 data integration challenges and how to overcome them

  Data integration in modern architectures faces eight challenges, from preserving lineage at scale to serving AI and analytics workloads, each explored with practical strategies.  Continue Reading

• Unstructured data needed, but often untapped, for agentic AI

  AI development initiatives hinge on the quality and completeness of the underlying data, but research from BARC shows that many organizations struggle to operationalize key data.  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

• 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

• 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

• 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

• Evolving Alteryx focusing on AI-ready data, logic for agents

  As the vendor grows to meet changing customer needs, CEO Andy MacMillan says its goals have expanded beyond its data prep roots to include connecting agents with proper context.  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

• 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

• Power-constrained data architecture curbing AI ambitions

  Rising power demands and grid interconnection delays are hampering enterprise AI efforts and altering data strategies, workload placement and resilience planning.  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

• 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

• Tableau in transition as AI forces BI vendors to evolve

  With its new context layer for AI, the vendor is attempting a needed evolution as agents and other cutting-edge tools reduce enterprise reliance on traditional analytics.  Continue Reading

• New Tableau leader talks vendor's evolution in era of AI

  With longtime data and analytics providers pivoting to find their role within AI workflows, Mark Recher takes over during a time of transition for the vendor.  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

• 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

• 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

• Data quality, fast failures and quick wins key to AI success

  Despite many organizations struggling to realize value from their development initiatives, best practices can help, according to industry experts at Domo's annual user conference.  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

• 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

• Domo drives customer excitement with latest AI capabilities

  New features including an MCP server keep the vendor current with industry trends and have users imagining what's possible with agentic AI.  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

• 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

• As AI advances, Domo is evolving to meet customer needs

  Once viewed as an analytics specialist, the vendor's product development plans are now focused on helping users build agents and other applications that can be trusted and deployed.  Continue Reading

• How big data collection works: Process, methods, challenges

  Before big data can be used in analytics and AI applications, data teams must collect it from various sources. Here's how to create and manage an effective collection process.  Continue Reading

• 7 data backup strategies and best practices you need to know

  A strong data backup strategy is not an option; it is a requirement. Here are seven best practices organizations can implement to better protect and manage backups.  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

• How to build an effective big data strategy

  Big data initiatives won't deliver business benefits without a comprehensive strategy to guide data management and analytics work. Here's how to build one.  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

• Good governance key to reducing high AI project failure rate

  Better governance of cutting-edge applications and the data that feeds them are key to overcoming development obstacles, Databricks exec Craig Wiley said in a recent interview.  Continue Reading

• SLM vs. LLM: Rightsize data architecture to optimize AI use

  It doesn't need to be a binary choice. Enterprises are warming up to smaller AI models to meet compliance and cost needs while reserving large models for complex jobs.  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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Big data analytics and business intelligence: A comparison

  BI and big data analytics support different types of analytics applications. Using them in complementary ways enables a comprehensive data analytics strategy.  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

• 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

• 4 trends that will shape data management and AI in 2026

  The tendencies that influence the next 12 months build on what began last year, including rising mergers and acquisitions and the need to simplify developing and deploying agents.  Continue Reading

• 4 trends that shaped data management, analytics in 2025

  With each relating to agentic AI development, tendencies included nearly universal support for MCP and rising emphasis on semantic modeling, among others.  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

• 9 data backup trends to watch

  A new year brings challenges, especially in the evolving data backup and protection landscape. Avoid falling behind the times by preparing for these key trends.  Continue Reading

• One year of MCP: Support a must for data management vendors

  With most AI development initiatives focused on agentic AI, failure to provide the framework for easily connecting agents with relevant data is now a competitive disadvantage.  Continue Reading

• Microsoft helps Premier League fuel fan experience with AI

  A new app built by England's top soccer division using the tech giant's data management and development capabilities draws on decades of data to deliver personalized insights.  Continue Reading

• Ng: Biggest benefit of AI may be unlocking unstructured data

  Tech entrepreneur Andrew Ng says that in addition to autonomous action, one of the most beneficial applications of AI is enabling easy access to valuable unstructured data.  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

• Snowflake's AI capabilities fueling music data specialist

  Luminate, which provides the data for Billboard's music charts and informs other entertainment industry clients, is developing agents using the data platform vendor's tools.  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

• 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

• AI in sports: Agents and the future of analytics

  Pragmatic advances such as improved data integration and streamlined information delivery will aid teams, as will transformative gains including agent-driven game strategies.  Continue Reading

• AI in sports: How modern tools are changing analytics

  With recent technological advances making it easier to access and operationalize data, advanced analysis is now the norm for teams when evaluating players and plotting strategy.  Continue Reading

• AI in sports: A look back at the analytics pioneers

  Where once baseball's Oakland A's gained a strategic edge by discovering undervalued statistics, teams across all sports are now using emerging technologies to inform decisions.  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

• 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

• 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

• 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