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Data Lifecycle Management as Foundational Pillar to AI Agents
Thu, 24 April
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.
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About the speaker
Karthik Birudavolu
Lead Data Solutions Architect, Lloyds Technology Centre
Karthik is a data and analytics product leader with over 18 years of experience in information technology. He specializes in driving innovation and delivering scalable solutions, particularly in engineering and implementing governed analytics on Google Cloud Platform and other public clouds. His expertise enables enterprises to leverage data across various use cases effectively.
He focuses on developing Generative AI models to address enterprise business needs, utilizing advanced Agentic AI capabilities. His career highlights include establishing robust data movement pipelines, implementing stringent controls and governance for analytics, and ensuring comprehensive data management, security, and risk mitigation.
Karthik has led IT strategy, infrastructure, cloud migrations, and enterprise architecture for clients in the banking, insurance, and healthcare sectors. With a proven track record of delivering programs and products within budget and timeline, he excels in managing end-to-end implementations and developing strong governance models for effective transformation.
