Understanding data quality

Organizations everywhere are looking to refresh their data strategies to take advantage of the recent advances in AI and position themselves to capitalize on the newly available capabilities. However, no matter how advanced AI models get, they’ll rely on quality data to be effective. New research from Informatica concluded that 42% of data leaders cite data quality as their top obstacle, while 40% highlight data privacy and governance, and 38% point to AI ethics as significant challenges.1

The quality of your data is not going to improve without a strategy that clearly articulates why doing so is critical for your organization to achieve its AI goals. This report gives you everything you need to clearly articulate why you need to focus on the quality of your data as a key part of your overall data strategy. You will also discover some direct and applicable actions you can take to make data quality the foundation of your data culture.

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