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How Observability is Advancing Data Reliability and Data Quality
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Inhalt bereitgestellt von Damien Deighan and Philipp Diesinger, Damien Deighan, and Philipp Diesinger. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Damien Deighan and Philipp Diesinger, Damien Deighan, and Philipp Diesinger oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.
Modern Data Infrastructures and platforms store huge amounts of multidimensional data. But - data pipelines frequently break and a machine learning algorithm's performance is only as good as the quality and reliability of the data itself.
In this episode we are joined by Lior Gavish and Ryan Kearns of Monte Carlo, to talk about how the new concept of Data Observability is advancing Data Reliability and Data Quality at Scale.
Episode Summary
- A overview of Data Reliability/Quality and why it is so critical for organisations
- The limitations of traditional approaches in the area of Data Reliability
- Data observability and why it is different to traditional approaches to Data Quality
- The 5 Pillars of Data Observability
- How to improve data reliability/quality at scale and generate trust in data with stakeholders.
- How observability can lead to better outcomes for Data Science and engineering teams?
- Examples of data observability use cases in industry
- Overview of O’Reilly’s upcoming book, The Fundamentals of Data Quality.
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