Speakers: Per Hillertz – IT Site Lead at AstraZeneca Gothenburg and Director of M&A IT, AstraZeneca
Igor Printsev – Sr Delivery Management Director, EPAM
Description
Session outline:
Across industries, organisations hold as well as acquire decades of legacy, unstructured, and highly specialised data, often heavily domain related (such as scientific data). Advances in LLMs and Agentic AI now make it possible to convert these assets into structured, AI-ready datasets that fuel analytics, automation, and innovation at scale. In this session, EPAM and AstraZeneca will be taking about approaches to deal with legacy in-house as well as M&A data, show how modern approaches unlock value from legacy data for any data-driven enterprise, and also talk about the future of legacy data.
Key takeways:
- How modern LLMs and Agentic AI technologies transform unstructured legacy data into high-quality, AI-ready datasets.
- How three real use cases illustrate methods from scientific industry
- Why traditional data processes cannot keep up with the scale, complexity, and heterogeneity of decades of enterprise data including acquired one.
- How building an AI-ready data foundation increases accuracy, speeds time-to-insight, and enables more effective decision-making in any data-intensive organization.
- Future of legacy data
Session sponsored by EPAM
