In today’s GenAI-driven landscape over 80% of enterprise data is unstructured and largely unchecked. As a result, organizations risk missing out on valuable insights, expose themselves to compliance violations, and undermine the effectiveness of GenAI investments. This session outlines a two-part strategy: (1) identifying and correcting issues to ensure clean, GenAI-ready datasets, and (2) extracting actionable insights to fuel strategic decision-making. Using Anomalo’s newly released Unstructured Data Monitoring product, enterprises can now transform hidden, unstructured data into trusted, high-impact assets that power scalable, responsible, and intelligent AI applications.

• Recognize the critical role of unstructured data in enabling trustworthy and effective GenAI outcomes.
• Identify common data issues—such as PII, outdated content, and duplication—that can undermine AI confidence and compliance.
• Apply a step-by-step workflow to cleanse, govern, and prepare unstructured data for GenAI and ML applications.
• Transform customer feedback and other unstructured sources into actionable insights that inform strategy and improve customer experience.
• Integrate data quality checks and insight extraction into existing enterprise workflows to accelerate time-to-insight and reduce manual effort.

Speaker

Vicky Andonova GM of Generative AI Products Vicky Andonova is the GM of Generative AI at Anomalo, where she leads the company’s efforts to bring innovative AI-powered data quality products to market, with a focus on unstructured data. A founding team member, Vicky has spent the past six years pioneering Anomalo’s machine learning capabilities, helping enterprises trust and act on their data. Prior to Anomalo, she was a product leader at Instacart, where she built the company’s experimentation platform and drove initiatives to improve grocery delivery quality. She began her career as a data scientist and engineer focused on machine learning applications in consumer technology. Vicky is passionate about applied AI and the intersection of data infrastructure and product innovation. She holds a BE in Computer Engineering from Columbia University.