Infographic: Observability for AI Innovation

This survey examines adoption drivers of observability processes and tools, obstacles, challenges and best practices for success.

Measuring and Optimizing Data Quality, Pipelines and AI/ML Models

As AI amplifies the risks and benefits of analytics, the transparency and reliability of inputs and outputs are becoming increasingly important for organizations. This makes it even more important to understand why, where, and how users are implementing observability.

This BARC study examines three key observability disciplines – data quality, data pipeline and AI/ML models – with a focus on measuring, monitoring and optimizing them.

The infographic summarizes the key findings. You can download the full study “Observability for AI Innovation – Adoption Trends, Requirements and Best Practices” for free here.

Infographic Observability for AI Innovation
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Author(s)

VP of Research at BARC US

Kevin Petrie is the VP of Research at BARC, where he leads the data management practice and writes about topics such as AI, data integration and data governance. For 30 years Kevin has deciphered what technology means to practitioners, as an industry analyst, instructor, marketer, services leader, and tech journalist.

Kevin built a data analytics services team for EMC Pivotal in the Americas and EMEA, and ran field training at the data integration software provider Attunity (now part of Qlik). A frequent public speaker and co-author of two books about data management, Kevin most loves teaching data and AI leaders about evolving strategies, tools and techniques to capitalize on the value of data.

Senior Analyst Data & Analytics

Timm Grosser is a Senior Analyst Data & Analytics at BARC with a focus on data strategy, data governance and data management. His core expertise is the definition and implementation of data & analytics strategy, organization, architecture and software selection.

He is a popular speaker at conferences and seminars and has authored numerous BARC studies and articles.

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