Manual Curation vs AI Automation at Scale

The Scaling Problem: Manual documentation and tagging produce high quality metadata. A data steward writes clear descriptions, assigns accurate business terms, and validates PII classifications. But this approach collapses at scale. If you have 50,000 datasets and each takes 15 minutes to document, that is 12,500 hours of work. At one person documenting full time, that is 6 years. Automatic approaches scale effortlessly but make mistakes. An AI classifier might tag a user_id field as PII when it is actually a non sensitive anonymized identifier. Schema crawling captures technical metadata but produces no business context. Query log analysis infers usage patterns but cannot explain why a dataset is deprecated. The Hybrid Strategy: Practical systems adopt a tiered approach based on dataset importance. Critical datasets, perhaps the top 5 to 10 percent by usage, get manual stewardship. A dedicated owner reviews AI suggestions, writes clear documentation, and validates classifications monthly. These are the datasets that power executive dashboards, regulatory reports, and key product metrics. The long tail of thousands of datasets gets automatic treatment. AI infers descriptions from schema and query patterns, classifies columns using trained models, and extracts basic lineage from job metadata. The error rate might be 5 to 10 percent, but that is acceptable for datasets queried once per month. AI Techniques in Discovery: Modern discovery platforms layer AI in several ways. Column level classification uses machine learning models trained on labeled examples to detect PII, financial data, and other sensitive types. The model examines column names, data types, sample values, and statistical distributions. Description generation analyzes schema, sample data, and query patterns to produce natural language summaries. For a table with columns order_id, user_id, total_amount, and frequent joins to a users table, the AI might generate: "Order transactions with user information and payment totals." Recommendation engines suggest related datasets based on co usage patterns. If 80 percent of queries against table A also query table B within the same session, the system recommends B when users view A. When Automation Fails: AI automation has clear limits. It struggles with domain specific terminology not present in training data. It cannot understand business logic or explain why a dataset was created. It produces generic descriptions that lack the context human users need. The risk is creating a catalog that appears complete but is actually misleading. Users find tables with AI generated descriptions that sound plausible but miss critical details: "This table is deprecated, use payments_v2 instead." Only a human steward knows to add that note.