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