Is Your Data Ready for AI/ML?
Artificial Intelligence (AI) and Machine Learning (ML) initiatives are top of mind at many mid-market and enterprise organizations, but one key component can hold these projects back – a poor and misunderstood data foundation.
A recent report from AWS found that 46% of Chief Data Officers (CDOs) rank data quality as a top challenge for realizing the potential of AI. Similar sentiments were found in a Database Trends and Applications report, where 31% of respondents stated that data quality is a constant, ongoing issue in the age of AI, and only 23% of organizations have full confidence in their data.
In this white paper, we’ll help you understand the common mistakes that happen when implementing AI/ML solutions, the challenges with data quality and preparation, and the foundation for AI/ML success.
You’ll learn about:
- 8 common mistakes in implementing AI/ML
- Challenges in data preparation for AI/ML
- Data foundations for AI success
- How to accelerate your AI/ML initiatives
Ready to prepare your data foundation for AI/ML? Fill out the form on this page to get the white paper.
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