AI-Powered Data Catalog & Metadata Management
Use AI to automatically discover, document, and maintain a searchable catalog of all data assets. Essential for data teams managing 500+ tables across multiple data sources who need to reduce 'data discovery tax' and improve governance without slowing down analytics.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Data assets are undocumented and hard to find. Analysts spend hours searching for "the right table." Duplicate datasets created because teams don't know what exists. No data lineage. Compliance risk from unknown data usage. Data teams at growing ASEAN companies spend 30-40% of their time just finding and understanding data, with tribal knowledge concentrated in a few long-tenured analysts who become bottlenecks.
After
AI-powered data catalog automatically indexes all data assets, generates metadata, maps lineage, and suggests documentation. Search finds relevant datasets in seconds. Duplicate data reduced 60%. Compliance visibility improved. Any analyst can discover, understand, and assess the quality of any dataset in minutes through natural language search, with full lineage visibility from source to dashboard.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Deploy AI Data Catalog Platform
3 weeksImplement: Alation, Atlan, Collibra, or open-source (DataHub, Amundsen). Connect to: databases, data warehouses, data lakes, BI tools, ML platforms. AI automatically discovers: tables, columns, schemas, relationships, usage patterns. For mid-market companies, DataHub (open-source) offers strong capabilities without licence costs — evaluate it before committing to Alation or Collibra. Connect to your most-queried data sources first (check query logs) rather than trying to catalog everything at once. Budget 1 week per major data source for connector configuration and initial indexing.
Auto-Generate Metadata with AI
2 weeksAI analyzes data and generates: column descriptions, data types, sample values, null rates, uniqueness, value distributions. Identifies: PII (personally identifiable information), sensitive data, business-critical datasets. Tags assets automatically. AI-generated descriptions for technical metadata (data types, null rates, uniqueness) are 95%+ accurate and can be trusted immediately. Business descriptions require human review — assign data owners to validate AI-generated descriptions for their top 20 most-used tables within the first 2 weeks. Flag PII columns automatically and route them to your data privacy officer for classification under PDPA or equivalent ASEAN data protection laws.
Map Data Lineage & Impact Analysis
3 weeksAI traces data flow: from source systems → ETL → data warehouse → dashboards → ML models. Shows upstream dependencies and downstream impacts. Enables "what-if" analysis: "If I change this table, what breaks?" Alerts owners before breaking changes. Start lineage mapping from your most critical dashboards and work backwards to source systems — this 'demand-driven' approach covers the most valuable data paths first. Test impact analysis by simulating a schema change on a non-critical table and verifying the system correctly identifies all downstream dependencies. For organisations with complex ETL layers, expect lineage to be incomplete initially — treat it as a living map that improves with each pipeline instrumentation.
Enable Semantic Search & Recommendations
2 weeksUsers search in natural language: "customer churn data" → AI returns relevant tables ranked by: relevance, data quality, popularity, freshness. Suggests related datasets: "Users who queried this also queried..." Learns from usage patterns. Seed the search system with 50+ example queries from actual analyst requests (pulled from Slack or email) to train relevance ranking. Add synonyms for business terms that differ across departments — 'customers' vs. 'accounts' vs. 'clients' should all return the same datasets. Track search success rate (did the user find what they needed?) and refine based on zero-result queries.
Tools Required
Expected Outcomes
Reduce time to find relevant data from hours to minutes
Decrease duplicate data creation by 60%
Improve data documentation coverage from 10% to 80%
Enable impact analysis for schema changes (prevent breakages)
Improve compliance through PII/sensitive data discovery
Reduce time to find relevant datasets from hours to under 5 minutes
Improve data documentation coverage from under 10% to 80%+ within 3 months
Prevent 60%+ of data-related incidents through proactive impact analysis before schema changes
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Common Questions
For technical metadata (data types, nulls): 95%+ accurate. For business descriptions: 60-70% accurate initially. Improve by: crowdsourcing corrections from data owners, learning from user feedback, importing tribal knowledge from Slack/wikis.
AI prioritizes: start with most-used datasets (query logs show this), business-critical data (revenue, customers), compliance-sensitive data (PII). Gradually expand coverage. Focus on quality over quantity—catalog 100 important datasets well vs. 10,000 poorly.
AI continuously syncs with data sources: detects new tables, schema changes, usage pattern shifts. Auto-updates metadata. Alerts data owners when descriptions are outdated. Gamify contributions: leaderboards for most documented datasets.
Ready to Implement This Workflow?
Our team can help you go from guide to production — with hands-on implementation support.