Data management continues to evolve to address new use cases in advanced analytics, machine learning, and artificial intelligence.
Data management professionals must continue to evolve their skills, teams, platforms, and practices to address new opportunities associated with large language models, deep learning, generative AI, and other technological innovations. In this session, TDWI senior research director James Kobielus will explore such issues as:
- When and how should enterprises evaluate the potential of LLM-driven query generation tools as an adjunct to or replacement for existing SQL optimization tools?
- How should enterprises incorporate LLM-driven tools such as synthetic data generation and prompt-driven ETL code generation into their data engineering practices?
- Are vector databases the future of generative AI or is there a future of SQL, NoSQL, and other DBMSs in hybrid multiplatform deployments?
- How should organizations evolve their data lakes to speed the building, training, operationalization, and governance of high-impact generative AI applications?
- What roles, skills, and workflows should organizations adopt in their data governance practices to effectively curate training data, iteratively fine-tuning the outputs using prompt engineering?