{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Data Science Tech Brief By HackerNoon","title":"When a Dashboard Looks Perfect but the Data Is Wrong","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/f2a0214b\"></iframe>","width":"100%","height":180,"duration":452,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/when-a-dashboard-looks-perfect-but-the-data-is-wrong.\nData definitions are crucial for accurate metrics, and their absence can lead to believable but wrong numbers that nobody questions.\nCheck more stories related to data-science at: https://hackernoon.com/c/data-science.\n            You can also check exclusive content about #data-science, #data-engineering, #data-analytics, #data-visualization, #data-governance, #data-testing, #automated-data-testing, #business-intelligence,  and more.\nThis story was written by: @kkj-data. Learn more about this writer by checking @kkj-data's about page,\n            and for more stories, please visit hackernoon.com.\nData definitions are crucial for accurate metrics, and their absence can lead to believable but wrong numbers that nobody questions. Centralizing definitions and putting business logic in versioned, tested models can prevent metric disputes and ensure trust in data-driven decisions.","thumbnail_url":"https://img.transistorcdn.com/8VxAgS1Ll3FJEERcAdhFdqqXJMnE7OfD2RUvrjauLt0/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjY4LzE2ODM1/ODI1ODUtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}