Data teams view language models (LMs) such as ChatGPT from OpenAI or BARD from Google with a mix of excitement and fear. Their fast, articulate answers to expert questions can help data teams discover datasets, write and debug code, document procedures, and learn new techniques as they build data pipelines. Exciting! But the fear also is justified. LMs can derail projects and undermine governance programs by giving answers that contain errors, sensitive data, or bias.
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Data teams view language models (LMs) such as ChatGPT from OpenAI or BARD from Google with a mix of excitement and fear. Their fast, articulate answers to expert questions can help data teams discover datasets, write and debug code, document procedures, and learn new techniques as they build data pipelines. Exciting! But the fear also is justified. LMs can derail projects and undermine governance programs by giving answers that contain errors, sensitive data, or bias.
Nearly 50% of the survey participants already use LLMs for tasks varying from data pipeline construction to documentation. Learn from their experience to boost your practice.
This eBook explores the emergence of LMs and LM-based tools as well as their implications for the discipline of data practice. And recommends ways to realize the productivity benefits of LMs while minimizing risk.
Table of Contents
- Chapter 1 Defines this market segment and examines adoption trends and use cases for combining them.
- Chapter 2 Explores governance strategies to handle the inherent risks of LMs.
- Chapter 3 Describes the emergence of domain-specific language models that support specialized data management use cases in a more governed fashion.
- Chapter 4 Recommends guiding principles for the successful usage of language models for data management.
