Drive trust in AI with data observability & data quality

The results are in – 92% of data leaders plan to make data observability a core part of their strategies in the next one to three years. Why is this becoming a key focus? Poor data quality, such as incomplete, biased or outdated data, can lead to flawed AI models, resulting in incorrect or harmful outputs.

So, data observability and data quality are critical if you want to build trustworthy AI initiatives.

In this report from CDO Magazine, you’ll learn:

  • Best practices of data quality management
  • Steps to build trustworthy AI initiatives
  • 4 key aspects of data observability
  • How to foster a culture of data quality and observability
Data Quality & Observability Are Key to Building AI Trust

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Data Quality & Observability Are Key to Building AI Trust

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Data Quality & Observability Are Key to Building AI Trust

Thank you for your interest in Informatica. Please complete the form below to have this item emailed to you.

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Data Quality & Observability Are Key to Building AI Trust

Thank you for your interest in Informatica. Please complete the form below to have this item emailed to you.

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Informatica will use data provided here in accordance with our privacy policy. For California consumers, see our California privacy notice.

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