In the era of AI-driven decision-making, the success of AI agents depends on high-quality, well-managed data. Data lifecycle management (DLM), governance, and structured processing are fundamental pillars for enabling reliable AI workflows. Without proper data stewardship, AI models risk bias, inconsistency, and unreliable outputs.

This session explores best practices for managing enterprise data, ensuring data quality, lineage, governance, and compliance throughout the AI pipeline. We will discuss the scope of enterprise data, including structured, unstructured, audio, video, code, and documents, and how to process, store, and make it consumable for AI/ML models.

Attendees will gain insights into building scalable data products, qualifying data for AI use cases, and implementing governance frameworks that ensure AI-driven systems remain trustworthy, explainable, and compliant.

Key Takeaways

• Understanding the Data Lifecycle – Managing data from creation, processing, storage, retrieval, and archival for AI applications.
• Best Practices for Data Governance – Ensuring privacy, security, and compliance while making data AI-ready.
• Building Enterprise-Grade Data Products – Transforming raw data into well-structured, high-quality assets for machine learning and AI models.
• Measuring and Maintaining Data Quality – Techniques to qualify, validate, and monitor data for bias, drift, and inconsistency.
• Processing Multi-Modal Data – Handling structured, unstructured, video, audio, and document-based data for AI consumption.
• AI-Ready Data Pipelines – Designing data ingestion, transformation, and feature engineering pipelines optimized for AI agents.

Target Audience

• Data Scientists & AI Engineers looking to ensure high-quality training data for AI/ML models.
• Data Architects & Engineers working on scalable data infrastructure for AI-driven applications.
• Enterprise Data Governance & Compliance Teams ensuring secure and regulatory-compliant AI models.
• AI Product Managers & Decision Makers strategizing data-driven AI adoption in enterprises.

AI agents are only as good as the data they consume. By implementing robust data lifecycle management and governance, enterprises can ensure trustworthy, explainable, and high-performing AI systems. This session will equip attendees with best practices, frameworks, and real-world insights to establish data as the foundation for enterprise AI success.