What’s the Big Deal about Data Observability
Due to the ongoing exploding supply of data, enterprises keep constant watch over their data and the pipelines through which it is ingested and managed. Yet many organizations still have issues with data quality and reliability. Multidimensional data observability optimizes the health of data pipelines, data and the data processing of modern data systems.
Adding insights and analytics beyond monitoring, multidimensional data observability is the foundation that enterprises use to ensure data availability, reliability, efficiency, and performance – enabling data teams to deliver trusted data to consumers, and executives to align data ops investments to meet business requirements.
Tristan Spaulding is Head of Product at Acceldata. He was an early product manager at AI unicorn DataRobot, where he partnered with hundreds of leading enterprises on their machine learning initiatives, developed the product and design teams, and launched the DataRobot MLOps product. Before DataRobot, he worked in technical product roles in big data analytics at Oracle and a small startup. He lives outside Boston with his wife, two kids, and two cats.
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