Let’s share some genAI learnings. What challenges or considerations have you encountered when integrating generative AI into your data management processes?
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Chief Data Officer in Software5 days ago
Generative AI based systems are built on, and optimized by, unstructured text data. In looking to integrate GenAI based systems using copilot functionality, there are significant barriers to be able to leverage data in more structured data sources as insights for GenAI copilots. Creating complex RAG patterns that use Graph to create the context needed by GenAI can help, but don't go nearly far enough to enable the full depth of insights that are buried in mountains of structured data.
First, data privacy is a critical concern. We need to be extremely cautious about what data is exposed to the AI models, particularly sensitive customer information. During training, we mitigate risks by using synthetic data, but when preparing for production, privacy becomes even more important. Extensive testing is required to ensure real data is handled securely and in compliance with regulations.
Second, data quality proved to be equally challenging. The effectiveness of AI-generated responses depends on the quality of the data it's trained on. Inconsistent or incomplete data led to inaccurate outputs. We had to invest heavily in data cleansing and enrichment to ensure that the AI produced relevant and valuable results for customers.