This episode of Techsplainers explains data reliability—the completeness and accuracy of data across time and sources. We explore how reliability is measured through validity, completeness, and uniqueness, and distinguish it from related concepts like data quality and validity. The discussion covers common challenges organizations face with data reliability, from collection methods and human error to changing sources and duplication issues. We provide practical steps for ensuring reliable data, including standardized collection, proper training, regular audits, and strong governance. Finally, we examine how data observability transforms reliability management by enabling real-time issue identification and resolution before bad data impacts decision-making. For organizations seeking competitive advantage through data-driven decisions, establishing robust reliability practices is no longer optional but essential.&nbsp;&nbsp;Find more information at https://www.ibm.biz/techsplainers-podcast&nbsp;Narrated by Mimi Sun Longo&nbsp;

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. 

What is data reliability?

This episode of Techsplainers explores the five types of preventive maintenance strategies that help organizations avoid costly equipment failures. We dive into usage-based, time-based, condition-based, predictive, and prescriptive maintenance approaches, highlighting how each provides unique benefits for different operational priorities. The discussion covers major advantages of preventive maintenance including extended asset lifespans, significant cost savings, reduced downtime, improved workplace safety, and enhanced sustainability. We also examine how modern technologies like IoT sensors, AI analytics, and computerized maintenance management systems are transforming maintenance practices, enabling more sophisticated approaches that optimize equipment performance while minimizing costs. This episode builds on our previous discussion of preventive maintenance fundamentals to provide a comprehensive understanding of this essential facilities management practice.&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/what-is-preventive-maintenance" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/what-is-preventive-maintenance</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a>&nbsp;&nbsp;Narrated by Ian Smalley

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. 

What is preventive maintenance?

This episode of Techsplainers explores Energy Asset Management (EAM), the specialized practice of maintaining, monitoring, and optimizing energy infrastructure throughout its entire lifecycle. We examine how organizations track and manage critical assets like power plants, electrical grids, and renewable installations to extend equipment lifespan, increase efficiency, and reduce operational costs. The discussion covers the five key elements of effective EAM: asset tracking, performance management, data management, risk management, and lifecycle management. We highlight the major challenges facing the energy sector, including growing demand (4% annually), aging infrastructure, sustainability goals, security threats, and market volatility. The episode also provides historical context on energy's critical importance—from deciding the outcome of World War II to fueling modern geopolitical conflicts—and explains how advanced technologies like IoT sensors and AI are transforming asset management in our increasingly electrified world.&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/enterprise-asset-management" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/enterprise-asset-management</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a>&nbsp; Narrated by Ian Smalley

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. 

What is enterprise asset management (EAM)?

This episode of Techsplainers explores asset lifecycle management (ALM), the comprehensive process that helps organizations maximize the value and lifespan of their valuable resources. We break down the four key stages of ALM—planning, procurement, usage, and disposal—and explain how modern technologies like IoT sensors, digital twins, and enterprise asset management systems enable real-time monitoring and preventive maintenance. The discussion covers various asset tracking methods including RFID tags, GPS monitoring, and QR codes, while highlighting the major benefits of effective ALM: extended asset lifespan, reduced costs, minimized downtime, and increased operational efficiency. We also examine how cutting-edge technologies like artificial intelligence, augmented reality, and robotics are transforming asset management by enabling predictive maintenance, remote inspections, and enhanced worker capabilities.&nbsp; Find more information at <a href="https://www.ibm.com/think/topics/asset-lifecycle-management" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/topics/asset-lifecycle-management</a>Find more episodes at <a href="https://www.ibm.biz/techsplainers-podcast" rel="noopener noreferrer" target="_blank">https://www.ibm.biz/techsplainers-podcast</a>&nbsp; Narrated by Ian Smalley&nbsp;

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. 

What is asset lifecycle management (ALM)?

This episode of Techsplainers explores dark data - the information assets organizations accumulate but fail to use for analytics or business purposes. We examine how prevalent this issue is, with surveys showing 60% of organizations reporting half or more of their data remains unused. The discussion covers why dark data accumulates (inexpensive storage, "just in case" mentality), the various causes (lack of awareness, data silos, poor governance), and the three types of dark data (structured, unstructured, and semi-structured.) We also detail the substantial costs beyond storage, including liability, missed opportunities, inefficiency, and risks. The episode concludes with practical strategies for managing dark data through improved data governance, breaking down silos, and leveraging AI/ML tools to uncover valuable insights from previously hidden information.&nbsp; Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Mimi Sun Longo&nbsp;

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. 

What is dark data?

This episode of Techsplainers introduces data observability, the practice of monitoring data health across an organization. We explore why data observability matters, with 80% of executives distrusting their data and companies like Unity Software losing $110 million due to bad data. The discussion covers the three stages of the DataOps cycle (detection, awareness, and iteration), the five pillars of data observability (freshness, distribution, volume, schema, and lineage), and how data observability differs from data quality and governance. We also examine the hierarchy of data observability and provide a practical roadmap for implementing a data observability framework to ensure reliable, trustworthy data for better business decisions.&nbsp; Find more information at https://www.ibm.biz/techsplainers-podcast&nbsp;Narrated by Mimi Sun Longo&nbsp;

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

What is data observability?

This episode of Techsplainers explores data quality dimensions – which provide the structured framework for measuring and evaluating data trustworthiness. We explain the six core dimensions: accuracy (correctness of data), completeness (presence of all required values), consistency (uniformity across systems), timeliness (currency of information), validity (conformity to rules), and uniqueness (absence of duplicates). The episode delves into why these dimensions matter – with poor data quality costing organizations millions annually – and outlines a three-step implementation process: assessment, measurement, and continuous improvement. We also highlight key benefits, including better decision-making, regulatory compliance, workflow optimization, customer satisfaction, and risk reduction. These dimensions provide the foundation for trusted data that powers reliable insights and automation. &nbsp;&nbsp;Find more information at https://www.ibm.biz/techsplainers-podcast&nbsp;Narrated by Mimi Sun Longo

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. 

What are data quality dimensions?

Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new. This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.

What is data reliability?

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Recent Episodes

What is data reliability?

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Recent Episodes

What is preventive maintenance?

What is enterprise asset management (EAM)?

What is asset lifecycle management (ALM)?

What is dark data?

What is data observability?

What are data quality dimensions?