As AI adoption increases rapidly and organizations clamor to take advantage of OpenAI, Copilot, and other powerful tools, many mistakes and oversights are made. These mistakes manifest themselves in security breaches, invalid results, and even offensive content. The immediate results are lost time, money, and embarrassment. What do these problems have in common? Bad data!

Quality AI solutions require clean, documented, and validated data. This session dives into data quality with a strong focus on how data is maintained as it moves from transactional to analytic workloads.

Some topics include:

• How transactional data be maintained effectively without compromising performance.
• The importance of documentation in ensuring data is used correctly.
• How to validate data as it is moved, transformed, and crunched.
• Security implications of handing off data to AI applications.
• Ensuring that a source-of-truth exists for each data source.