Lecture Notes: Creating Data Catalog Content with ChatGPT

Introduction

• ChatGPT is being used in various ways to enhance data cataloging.
• Data cataloging is evolving from a data dictionary to a knowledge base including:
  • Business process descriptions
  • Metric/KPI definitions
  • Common term definitions
  • Sharing of BI reports and building community expertise
• The aim is to support data culture and literacy beyond just technical users.

Automating Knowledge Enrichment with ChatGPT

• Goal: Automate enrichment of metadata in catalogs.

Term Glossaries

• Importance of term glossaries for shared understanding in data culture.
• Use ChatGPT for generating industry-specific glossary terms in a load-ready format.

Metric/KPIs

• Approach for defining industry/org-specific metrics and KPIs using ChatGPT.
• Example: Requesting common HR metrics, and further querying for calculations.
• ChatGPT aids in generating starting points for reports and gap analysis.

Table and Column Descriptions

• Challenge in maintaining descriptions due to volume of data assets.
• Shift from relying solely on data stewards to a crowd-sourcing approach.
• Use ChatGPT to describe columns and tables based on their names.
• Provides a starting point for data consumers to refine descriptions.

SQL Explanations

• Alation catalog benefits in writing, sharing, and publishing queries.
• Difficulty in understanding queries for non-experts.
• ChatGPT assists in describing queries, providing a better starting point for understanding.

Closed Loop Automation

• ChatGPT can automate and scale descriptive metadata.
• Potential to shift from relying on busy individuals to validating AI-provided content.
• Testing thresholds for human validation based on critical data elements.
• Vision of a closed-loop improvement process.

Conclusion

• ChatGPT offers opportunities to enhance data management and governance.
• Encouragement to experiment with ChatGPT for evolving data catalogs.
• Contact details for further assistance and exploration.