Techsplainers by IBM

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.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo

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.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo

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.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo

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.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo

This episode of Techsplainers explores data quality—the measure of how well datasets meet criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose. We examine the seven key dimensions of data quality and explain how they impact business decisions, processes, and customer satisfaction. The discussion highlights the critical distinction between data quality, data integrity, and data profiling, while explaining why poor quality data costs organizations an average of USD 12.9 million annually (according to Gartner research). We also explore the growing importance of data quality for AI and machine learning systems, where the "garbage in, garbage out" principle directly affects outcomes. Whether you're in marketing, supply chain management, or healthcare, understanding data quality fundamentals is essential for making reliable, data-driven decisions.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo