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Data Quality Issues and Challenges | IBM

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Artificial IntelligenceAnalyticsAlice GomstynAlexandra Jonkerdatasetsdecision-makingdata silosbig databusiness intelligenceartificial intelligenceMachine learning algorithmsmodelsGartnerAI-ready data.data integrationdata observabilitydata governanceIBM Privacy Statementduplicate data entriesschemaData labelingData biasLearn more about data biasData silosGo to episodedata pipelineschemasdata transformationunstructured datadata synchronizationcyberattackdata poisoningLearn more about data poisoningdata quality managementdata quality monitoringcatalogsmetadataData cleansingdata deduplicationdata engineersData validationlifecycleroot cause analysisdata lineageservice level agreement (SLA)Alice GomstynAlexandra JonkerReport Increasing AI Adoption with AI-Ready Data Gain actionable insights on how to invest in AI technology for data and preparing data for AI. Read the reportWebinar | On demand AI agents run on data—is yours ready? Your data is your competitive edge. Learn how to unlock it securely and drive measurable ROI from AI in this short webinar. Watch nowTechsplainers Podcast Data management explained Techsplainers by IBM breaks down the essentials of data for AI, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast. Listen nowEbook Unify and access your data to help scale your AI Learn why the path to AI-ready data often starts with effective access to both structured and unstructured data and the challenges that can impede data leaders. Read the ebookCase study Legal overhead turned into strategic insight Learn how an AI-powered legal agent helps accelerate decision-making, reduce manual work and improve compliance. Read the case studyVideo AI Academy: Building a data strategy for enterprise AI In this episode, Cathy Reese explains how organizations today need a data strategy that’s ready for advanced AI, which will require them to harness their highest quality data assets. Watch the episodeEbook The hybrid, open data lakehouse for AI Simplify data access and automate data governance. Discover the power of integrating a data lakehouse strategy into your data architecture, including cost-optimizing your workloads and scaling AI and analytics, with all your data, anywhere. Read the ebookReport Cost of a Data Breach Report 2025 Data breach costs have hit a new high. Get up-to-date insights into cybersecurity threats and their financial impacts on organizations. Read the reportGuide The data leader’s guide to AI-ready data Understand the actionable steps data leaders can take to overcome data challenges, establish the groundwork for a trusted data foundation and help get your organization’s data ready for AI. Read the guideReport How the C-suite is turning information into impact Explore insights from 1,700 CDOs in this cross-industry report for data leaders. Read the reportExplore watsonx.governance®Explore data governance solutionsExplore AI governance consultingExplore watsonx.governanceExplore AI governance solutionsBad Data, Bad Results: When AI Struggles to Create Staff Schedules.”AI at the core: From AI projects to profits.”Dealing with Missing or Incomplete Data: Debunking the Myth of Emptiness.”“JPMorgan to pay about $350 million penalty over trade reporting gaps.”“Data disconnect: Over 80 percent of companies rely on stale data for decision-making.”“Label-Noise Learning with Intrinsically Long-Tailed Data.”“How to Understand and Fix Bias in Artificial Intelligence-Enabled Health Tools.”“Florence Nightingale: 200 Years Since Her Birth and We Still Making the Same Errors With Data.”“Label Noise in Context.”“Comparing the accuracy and speed of four data-checking methods.”“Vero finance staff back in state’s dog house after data-entry goof.”“EPD apologises for ‘data entry error.’”“Big Data, Transmission Errors, and the Internet.”

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