Techsplainers by IBM

Explore Think 2026: https://www.ibm.biz/think2026event  This episode of Techsplainers introduces AIOps observability, explaining how artificial intelligence transforms traditional IT monitoring into intelligent, automated systems. We explore how AIOps observability works by collecting logs, traces, and metrics, then applying AI capabilities like anomaly detection, root cause analysis, and predictive analytics to make sense of this data. The episode details how this powerful combination automates troubleshooting, reduces recovery time, and improves operational efficiency. We examine practical use cases including alert fatigue reduction, capacity planning, and performance degradation prevention. Finally, we look at how generative AI is enhancing observability platforms with natural language interfaces that allow IT teams to quickly diagnose issues without manual dashboard navigation. Techsplainers shows how these technologies are reshaping IT operations, with adoption growing significantly year over year. Find more information at https://www.ibm.com/think/topics/aiops-observability Find more episodes here https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio

Explore Think 2026: https://www.ibm.biz/think2026event  This episode of Techsplainers explores edge computing, a distributed framework that processes data closer to its source rather than sending everything to centralized cloud data centers. Matt explains how edge computing works through components like edge devices, gateways, and network infrastructure, highlighting how it complements rather than replaces cloud computing. Listeners will discover the key benefits of edge computing, including improved performance through reduced latency, optimized real-time decision making with local analytics, stronger security through localized data processing, enhanced scalability, increased operational efficiency, and reduced costs. The episode also covers the emerging field of edge AI, which processes machine learning workloads directly on connected devices, and examines real-world applications across healthcare, transportation, telecommunications, financial services, and entertainment industries. Find more information at https://www.ibm.com/think/topics/edge-computing Find more episodes here https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio

Explore Think 2026: https://www.ibm.biz/think2026event  This episode of Techsplainers explores hybrid cloud architecture—the approach that combines on-premises, private cloud, and public cloud environments into a unified IT infrastructure. We examine how hybrid cloud has evolved from traditional data centers to become a cornerstone of digital transformation, enabling organizations to balance security, cost, and innovation. The discussion covers the building blocks that make hybrid cloud work, including network connectivity through VPNs and APIs, virtualization technologies, containerization for application portability, and unified management platforms. We also clarify the distinction between hybrid cloud and multicloud approaches, while highlighting how most enterprises today leverage both in ""hybrid multicloud"" environments. The episode concludes by examining key benefits including agility, business continuity, cost savings, application modernization, and support for emerging technologies like generative AI. Find more information at https://www.ibm.com/think/topics/hybrid-cloud-architecture Find more episodes here https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio

Explore Think 2026: https://www.ibm.biz/think2026event  This episode of Techsplainers explores agentic coding in depth, building on our previous introduction. Matt explains how coding agents work across development stacks, distinguishing between vibe coding, agentic coding, and agentic engineering on the AI assistance spectrum. The episode highlights practical applications, from code reviews to feature development, while acknowledging the substantial benefits of handling mechanical coding tasks. Matt also addresses important challenges like subtle bugs and over-reliance, concluding with five best practices: defining guardrails, reviewing all AI-generated code, maintaining observability, providing proper context, and offering feedback to these AI collaborators. As with any powerful tool, thoughtful implementation is key to maximizing the value of agentic coding. Find more information at https://www.ibm.com/think/topics/agentic-coding Find more episodes here https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio

Explore Think 2026: https://www.ibm.biz/think2026event This episode of Techsplainers explores the concept of AI sovereignty, the ability of organizations and nations to control their artificial intelligence ecosystem. We examine why AI sovereignty has evolved beyond traditional data residency concerns into a holistic strategy covering infrastructure, data, models, and operations. The discussion highlights the four core components of AI sovereignty: data sovereignty, operational sovereignty, digital sovereignty, and AI infrastructure. We also distinguish between AI sovereignty and sovereign AI, explore implementation approaches using public cloud, hybrid cloud, and on-premises solutions, and outline key benefits including enhanced security, regulatory compliance, operational resilience, and competitive advantage. Finally, we provide best practices for organizations looking to implement an effective AI sovereignty strategy in an increasingly AI-driven business landscape. Find more information at https://www.ibm.com/think/topics/ai-sovereignty Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio

Explore Think 2026: https://www.ibm.biz/think2026event This episode of Techsplainers delves into the intricate workings of quantum-centric supercomputing hardware and architecture. Host Ian Smalley explains the quantum processing unit (QPU) at the heart of these systems, describing how superconducting qubits function through Josephson junctions and require temperatures colder than space to maintain their quantum states. The episode outlines the three-phase evolution of quantum-centric supercomputing: from specialized compute engines within existing systems, to tightly coupled resources through advanced middleware, to fully co-designed quantum-HPC systems. Listeners will learn about key challenges facing this technology, including error correction approaches, scaling quantum processors through next-generation interconnects, and algorithm discovery. The discussion also covers IBM's ambitious roadmap toward systems with 2,000 logical qubits by 2033, providing insight into how this revolutionary computing paradigm will mature in the coming years. Find more information at www.ibm.com/think/topics/quantum-centric-supercomputing Find more episodes at https://www.ibm.biz/techsplainers-podcast  Narrated by Ian Smalley

Explore Think 2026: https://www.ibm.biz/think2026event This episode of Techsplainers introduces quantum-centric supercomputing, a revolutionary approach that combines quantum computing with traditional high-performance computing to create powerful integrated systems. Host Ian Smalley explains how these systems leverage the unique properties of qubits—including superposition, entanglement, interference, and decoherence—to potentially solve complex problems exponentially faster than classical computers alone. The episode covers the fundamental differences between classical and quantum computing, explores potential applications in pharmaceuticals, chemistry, and machine learning, and clarifies that quantum computing will complement rather than replace classical computing. Listeners will gain insight into this cutting-edge technology that IBM predicts will enable major breakthroughs in simulation, optimization, and solving challenging mathematical equations across multiple industries. Find more information at www.ibm.com/think/topics/quantum-centric-supercomputing Find more episodes at https://www.ibm.biz/techsplainers-podcast  Narrated by Ian Smalley

Explore Think 2026: https://www.ibm.biz/think2026event This episode of Techsplainers introduces OpenRAG, IBM's open-source framework that connects large language models to enterprise data sources. We explore how OpenRAG builds bridges between powerful AI and organizational knowledge through Retrieval-Augmented Generation (RAG), enabling AI systems to ground their responses in actual company information rather than relying solely on training data. The discussion covers OpenRAG's flexible deployment options—from fully self-hosted architectures to hybrid cloud implementations—and highlights its modular design that allows organizations to customize components based on their specific needs. We examine real-world applications including enterprise knowledge assistants, customer support automation, regulatory compliance tools, research document analysis, data exploration interfaces, and collaborative knowledge systems. The episode concludes with practical guidance on getting started with OpenRAG, emphasizing its accessibility for both experimentation and enterprise-scale deployment. Find more information at https://www.ibm.com/think/topics/openrag Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio

Explore Think 2026: https://www.ibm.biz/think2026event This episode of Techsplainers explores data retrieval, the essential process of accessing information from various data sources. We examine how this field has evolved beyond simple database queries to encompass complex AI-driven techniques. The discussion covers traditional approaches like SQL and indexing alongside modern methods including vector search, natural language processing, and retrieval augmented generation (RAG). We highlight how agentic RAG elevates retrieval capabilities through intelligent decision-making components like semantic caching, routing agents, and query planning. Real-world examples demonstrate impressive efficiency gains across healthcare, financial services, and e-commerce, while we also address challenges including data quality, security concerns, and vendor lock-in. As organizations manage ever-expanding data volumes and AI workloads, sophisticated data retrieval becomes increasingly critical to business success. Find more information at https://www.ibm.com/think/topics/data-retrieval Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio

Explore Think 2026: https://www.ibm.biz/think2026event This episode of *Techsplainers* explores multi-agent collaboration, where multiple AI agents work together as a coordinated team to accomplish complex tasks. We explain how these systems have evolved beyond traditional LLMs to create autonomous workflows for research, support, analysis, and operations. The discussion covers key collaboration models including rule-based, role-based, and model-based approaches, and examines leading frameworks like IBM's Bee Agent, LangChain, and OpenAI's Swarm. We also highlight Watsonx Orchestrate as an enterprise solution for orchestrating AI-enabled workflows through interconnected components. Throughout the episode, we use the analogy of drone teams searching disaster sites to illustrate how independent agents can coordinate effectively without centralized control to tackle complex challenges that would overwhelm a single agent. Find more information at https://www.ibm.com/think/topics/multi-agent-collaboration Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio

This episode of Techsplainers explores data governance, the essential framework that ensures organizational data is properly managed, protected, and utilized. Amanda explains how data governance serves as an "air traffic control system" for information, defining policies and procedures for data collection, storage, and usage throughout its lifecycle. The discussion covers the four key components of governance frameworks: program goals and roles, data standards and policies, auditing procedures, and supporting tools. We examine how effective governance delivers tangible benefits including enhanced data value, balanced access, compliance with regulations like GDPR and HIPAA, and responsible AI development. The episode also addresses common implementation challenges such as lack of sponsorship, inconsistent architecture, and evolving AI requirements, before concluding with best practices including automation, creating a comprehensive data catalog, and continuous improvement. As the final installment in our data for AI series, this episode demonstrates how governance provides the structure that enables everything from AI-ready data to synthetic data creation. Find more information at https://www.ibm.com/think/topics/unstructured-data Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Amanda Downie

This episode of Techsplainers explores synthetic data - artificially generated information designed to mimic real-world data while preserving statistical properties and patterns. Amanda explains how synthetic data has become critical for AI development by addressing issues of data scarcity, privacy concerns, and training needs. The discussion covers the three types of synthetic data (fully synthetic, partially synthetic, and hybrid) and various generation techniques including statistical methods, GANs, transformer models, VAEs, and agent-based modeling. We examine the significant benefits of synthetic data - customization flexibility, improved efficiency, enhanced privacy protection, and data enrichment - while also addressing challenges like bias propagation, model collapse, accuracy-privacy tradeoffs, and verification needs. The episode concludes with real-world applications across automotive, finance, healthcare, and manufacturing industries, demonstrating how synthetic data is becoming essential for AI development. Find more information at https://www.ibm.com/think/topics/unstructured-data Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Amanda Downie

This episode of Techsplainers explores unstructured data - information without predefined formats that makes up 90% of enterprise data. Amanda explains how unstructured data differs from structured and semi-structured data, covering its diverse sources from emails to social media posts to sensor data. The discussion highlights why unstructured data has transformed from "dark data" into a strategic asset, particularly for AI applications. We explore key use cases including generative AI training, retrieval augmented generation (RAG), sentiment analysis, and predictive analytics. The episode also covers storage solutions like object storage and data lakes, plus processing tools that help organizations extract value from their unstructured information. With proper governance and management, unstructured data has become the fuel powering today's AI revolution. Find more information at https://www.ibm.com/think/topics/unstructured-data Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Amanda Downie

This episode of Techsplainers explores the concept of "bad data" - information that compromises decision-making because it's inaccurate, incomplete, inconsistent, outdated, duplicate, invalid, or biased. We examine why bad data is particularly dangerous due to its stealthy nature, often going undetected until significant damage occurs. Through real-world examples like Unity Technologies' $110 million loss from bad data in their AI algorithms, we illustrate the severe consequences across industries from healthcare to finance. The discussion covers the diverse causes of data quality problems - from system failures and data decay to human error and integration challenges - and provides a comprehensive approach to prevention through governance, monitoring, cleansing, and data literacy. As organizations increasingly rely on AI systems, understanding that "garbage in, garbage out" applies more than ever becomes crucial for success in data-driven initiatives. Find more information at https://www.ibm.com/think/topics/bad-data Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Amanda Downie

This episode of Techsplainers explores the concept of AI-ready data - high-quality, accessible, and trusted information that organizations need for successful artificial intelligence initiatives. We examine why only 29% of technology leaders believe their data meets AI readiness standards and break down the four essential characteristics that make data truly AI-ready: being unified and accessible, properly governed, secure, and supported by the right skills and infrastructure. The discussion highlights common barriers to AI readiness including data fragmentation, quality issues, skills gaps, and security risks, while explaining how organizations are failing to utilize their valuable unstructured data - with less than 1% of enterprise data currently leveraged in traditional large language models. Through practical examples and industry insights, this episode provides a roadmap for transforming raw data into a strategic asset that can power trusted, reliable AI applications across the enterprise. Find more information at https://www.ibm.com/think/topics/ai-ready-data Find more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Amanda Downie

This episode of Techsplainers explores cloud architecture—the foundational blueprint for cloud computing environments. We break down the four essential components: the front-end (user interfaces and dashboards), the back-end (servers, databases, and infrastructure), the network (connections between components), and cloud delivery models (IaaS, PaaS, and SaaS). The discussion covers various deployment approaches, from public and private clouds to hybrid and multicloud environments, and explains how organizations strategically combine these models to optimize performance and security. We also highlight the role of cloud architects in orchestrating these complex environments and implementing best practices for automation, data management, and workload placement. Finally, we examine the business benefits of well-designed cloud architecture, including accelerated modernization, faster innovation, enhanced resilience, and improved security and compliance across environments. Find more information at https://www.ibm.com/think/topics/cloud-architectureFind more episodes at https://www.ibm.biz/techsplainers-podcastNarrated by Douglas Lambert

This episode of Techsplainers explores private cloud computing—how it provides cloud benefits with enhanced security and control for organizations with sensitive data or specific regulatory requirements. Find more information at https://www.ibm.com/think/topics/private-cloudFind more episodes at https://www.ibm.biz/techsplainers-podcastNarrated by Douglas Lambert

"This episode of Techsplainers explores public cloud computing, where third-party providers deliver computing resources over the internet on a pay-as-you-go basis. Douglas explains how public cloud works as a multi-tenant environment where users share virtualized resources while maintaining data isolation. The discussion covers the three primary service models—Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). The episode also compares public cloud with private and hybrid approaches, highlighting how organizations typically combine these models for optimal flexibility. Finally, we address security considerations in public cloud environments, noting how provider security has evolved to often surpass on-premises solutions despite requiring different management approaches. Find more information at https://www.ibm.com/think/topics/public-cloudFind more episodes at https://www.ibm.biz/techsplainers-podcast Narrated by Douglas Lambert

This episode of Techsplainers explores the inner workings of cloud computing by examining its core components and service delivery models. Host Douglas Lambert explains the three fundamental building blocks of cloud computing: data centers (the physical infrastructure), networking capabilities (enabling high-speed connections), and virtualization (the technology that maximizes hardware efficiency). The episode then details the spectrum of cloud service models, from Infrastructure as a Service (IaaS), which provides basic computing resources, to Platform as a Service (PaaS), which offers development environments, to Software as a Service (SaaS), which delivers ready-to-use applications. The discussion concludes with an exploration of serverless computing, where providers automatically handle all infrastructure management and scale resources instantly based on demand. Through relatable analogies comparing these models to housing options—from unfurnished apartments to fully-serviced hotels—the episode demystifies the technical aspects of how cloud services are structured and delivered. Find more information at https://www.ibm.com/think/topics/cloud-computingFind more episodes at https://www.ibm.biz/techsplainers-podcastNarrated by Douglas Lambert

This episode of Techsplainers introduces the fundamentals of cloud computing, explaining it as on-demand access to computing resources over the internet with pay-per-use pricing. Host Douglas Lambert breaks down how cloud computing powers everything from consumer applications like email and streaming services to critical business operations across organizations of all sizes. The discussion covers the evolution of cloud computing from its conceptual origins in the 1960s to its emergence as a business necessity in the early 2000s with pioneers like Amazon Web Services, Google, and Microsoft. Listeners will discover four key benefits that make cloud computing revolutionary: cost-effectiveness through pay-as-you-go models, enhanced speed and agility in deployment, unlimited scalability to match demand, and access to cutting-edge technologies without implementation hurdles. Through relatable analogies comparing cloud services to streaming platforms, this episode demystifies the concept of "the cloud" for both technical and non-technical audiences. Find more information at https://www.ibm.com/think/topics/cloud-computingFind more episodes at https://www.ibm.biz/techsplainers-podcastNarrated by Douglas Lambert

This episode of Techsplainers explores AI agent security - the critical frameworks, tools, and practices needed to ensure autonomous AI systems operate safely and responsibly. We examine the unique security challenges of AI agents compared to traditional cybersecurity, focusing on three key risk categories: threats targeting the agents themselves (like prompt injection and training data poisoning), risks in agent interactions with external systems (such as unauthorized data access and privilege escalation), and dangers from emergent agent behaviors that may have unintended consequences. The discussion covers essential security practices including least privilege access, authentication mechanisms, continuous monitoring, and circuit breakers to halt problematic actions. We also highlight the importance of sandboxing agents in controlled environments and conducting red team exercises to proactively identify vulnerabilities. As AI agents become more powerful and autonomous, implementing robust security measures becomes increasingly critical for responsible deployment across organizations. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker

This episode of Techsplainers explores AI agent evaluation - the systematic approaches used to assess the performance, capabilities, and limitations of autonomous AI systems. Unlike simpler AI models, agents require multidimensional evaluation frameworks that examine task performance, reasoning quality, safety, adaptability, efficiency, and user experience. We discuss various evaluation methodologies including benchmark testing, simulation-based evaluation, and human assessment, along with specific metrics organizations use to measure agent effectiveness. The episode also addresses the unique challenges of evaluating multi-agent systems, open-ended tasks, and ethical dimensions of agent behavior. Listeners will learn about emerging trends in agent evaluation, including automated assessment tools and sophisticated observability mechanisms that provide insight into agent decision-making processes. As AI agents become more capable and widely deployed, robust evaluation practices become increasingly essential for ensuring these systems perform reliably, safely, and effectively across diverse contexts. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker

This episode of Techsplainers explores AI agent governance - the essential frameworks and practices that ensure autonomous AI systems operate safely, ethically, and effectively. We examine how governance needs to span the entire agent lifecycle, from initial design decisions to ongoing operational oversight. The discussion covers key governance dimensions including access controls, tool usage permissions, decision authority, monitoring systems, feedback mechanisms, and accountability structures. We highlight core principles like transparency, human oversight, and continuous evaluation that underpin effective governance approaches. The episode also addresses emerging trends like adaptive controls and risk-based governance that calibrate oversight based on an agent's capabilities and potential impacts. As AI agents become more powerful and widespread, implementing robust governance becomes increasingly critical for organizations seeking to harness these technologies while managing their unique risks and ensuring alignment with human values and organizational goals. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker

This episode of Techsplainers explores AI agent orchestration - the sophisticated process of coordinating multiple specialized AI agents to collaborate effectively on complex tasks. We explain how orchestration systems manage the assignment, communication, and coordination between different AI agents, each with their own capabilities. The episode breaks down the key components of successful orchestration, including the central orchestrator, communication protocols, task decomposition, result aggregation, and conflict resolution mechanisms. We examine different orchestration patterns like sequential, parallel, hierarchical, and collaborative approaches, with special attention to emergent collaboration models where agents dynamically form teams based on evolving needs. The discussion also covers significant challenges in agent orchestration, including communication overhead, dependency management, quality consistency, security concerns, and the risk of cascading failures. Through real-world examples in customer service, software development, research, and content creation, listeners gain insight into how orchestrated AI agents can tackle problems that would be impossible for any single agent. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker

This episode of Techsplainers explores AgentOps, the emerging operational framework for managing and optimizing AI agents throughout their lifecycle. Similar to how DevOps manages software development and MLOps handles machine learning models, AgentOps provides the essential practices, tools, and methodologies for deploying, monitoring, evaluating, governing, and continuously improving autonomous AI systems. We examine the five key areas of AgentOps: deployment, monitoring and observability, evaluation and testing, governance and safety, and continuous improvement. The episode also addresses the unique challenges of managing AI agents, including their unpredictability, complexity, tool integration requirements, performance drift, and multi-agent coordination needs. Listeners will gain insight into how organizations can implement effective AgentOps through clear metrics, specialized monitoring tools, robust testing frameworks, and comprehensive governance systems to maximize the reliability and performance of their AI agent investments. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker

This episode of Techsplainers explores financial management—the framework guiding resource allocation and investment decisions—and how AI and automation are transforming how organizations plan, budget, and optimize financial operations.

This episode of Techsplainers explores scenario planning—how organizations create multiple possible futures to manage uncertainty, mitigate risks, and make better strategic decisions across finance, supply chain, and beyond.

This episode of Techsplainers explores AI in Financial Planning & Analysis—how machine learning and generative AI are transforming finance from number-crunchers into strategic advisors.

This episode of Techsplainers explores Extended Planning & Analysis (xP&A)—how organizations can evolve beyond traditional financial planning to create a unified, AI-powered approach across all departments.

This episode of Techsplainers explores Enterprise Performance Management (EPM) and how it helps finance teams transform from number-crunchers into strategic advisors through AI-powered planning and analytics.

This episode of Techsplainers explores how modern ETL has evolved into an agile, cloud-native approach that processes data in real-time to support today's fast-paced business needs.

This episode of Techsplainers explores Change Data Capture (CDC)—the technique that keeps systems synchronized by tracking and transferring only data changes rather than entire databases.

This episode of Techsplainers explores real-time data integration—how organizations capture and process data in milliseconds to power instant decision-making and AI applications.

This episode of Techsplainers breaks down the key differences between ETL and ELT data integration approaches, explaining when to use each method for optimal data processing.

This episode of Techsplainers explores data integration—the process of combining scattered information into usable insights, from ETL to real-time approaches and their business benefits.

This episode of Techsplainers explores how identity threat detection and response (ITDR) tools protect the enterprise identity fabric by monitoring for suspicious behavior and automatically stopping attacks.

This episode of Techsplainers explores identity fabric: the integration layer that unifies disparate identity systems across hybrid environments to eliminate security gaps and enable consistent control.

This episode of Techsplainers explores nonhuman identities—the digital IDs attached to apps, bots and devices that outnumber human users in most organizations and present unique security challenges.

This episode of Techsplainers explains digital identity—the unique profiles that authenticate users and machines in IT systems—and why they're the foundation of effective identity security strategies.

This episode of Techsplainers explores identity security. Learn why protecting digital identities has become the cornerstone of modern cybersecurity and how organizations implement this crucial approach.

This episode of Techsplainers explores asset tracking technologies and systems that help organizations monitor and manage their physical assets for better efficiency and reduced losses.

This episode of Techsplainers explores CMMS software—how these systems automate maintenance operations, reduce downtime, and leverage AI to predict equipment failures before they happen.

This episode of Techsplainers examines the five types of preventive maintenance strategies and how modern technologies like IoT and AI are revolutionizing equipment maintenance practices.

This episode of Techsplainers explores Energy Asset Management—how organizations track, maintain and optimize critical power infrastructure to ensure reliability while facing growing demand and sustainability challenges.

This episode of Techsplainers explores asset lifecycle management—how organizations track, maintain and optimize valuable resources from purchase through disposal using IoT and AI technologies.

This episode of Techsplainers explores dark data - information organizations collect but never use - and how proper management can transform this hidden liability into valuable business insights.

This episode of Techsplainers explores data observability - how organizations monitor data health to prevent costly errors and maintain quality across systems, pipelines and processes.

This episode of Techsplainers explores data reliability—how organizations measure, maintain and improve the consistency and accuracy of their data to enable trusted insights and better decision-making.

This episode of Techsplainers examines the six core data quality dimensions that form the foundation of trusted data for analytics and AI: accuracy, completeness, consistency, timeliness, validity, and uniqueness.

This episode of Techsplainers explores data quality, examining the seven dimensions that determine if data is fit for purpose and how poor quality data undermines AI systems and business decisions.