While nobody can predict the overall future of generative AI, the business case for AI-driven data management is becoming clear. Most early adopters estimate that large language models such as ChatGPT and Bard make them up to 30% more productive, and new domain-specific tools from data pipeline vendors aim to beat these gains.
Data teams use language models to document their environments, build pipeline code, run data quality checks, learn new techniques, and more. But Chief Data Officers and business leaders must tread with caution. They must understand new risks for data quality, privacy, intellectual property, fairness, and explainability. They must adapt their data governance programs to control these risks, train their teams on prompt engineering, and give careful thought to evolving team responsibilities.
By adopting these guiding principles, CDOs and business leaders can democratize business consumption of data for analytics--unleashing unprecedented productivity gains. Watch this strategic discussion with Kevin Petrie, VP of Research at Eckerson Group, and Heine Iversen, CEO of TimeXtender, to explore the impact of language models on data management.
You will learn:
The definition of generative AI and language models
The business benefits and costs of applying language models to data management
Guiding principles for successful adoption and bottom-line results