Enterprise Master Data Pipeline
Ten million fragmented customer records reduced to 2.5 million trustworthy ones.
10M
Records consolidated
2.5M
Golden records delivered
4x
Governance metric gain
30%
Pipeline efficiency gain
The problem
Customer data lived in regional systems that disagreed with each other. The same customer existed several times over under different spellings, which made unified CRM reporting and any downstream analytics unreliable.
Approach
An Informatica ETL pipeline consolidates ten million records from the regional systems into a single master flow, normalizing formats before any matching is attempted.
Deduplication and automated validation testing run inside the pipeline, which moved governance metrics four-fold. The improvement came from measuring quality continuously rather than auditing it periodically.
Python, Oracle PL/SQL, and REST enrichment lifted legacy pipeline efficiency by 30%, largely by replacing row-at-a-time work with set-based operations.
The result is 2.5 million golden records published to downstream Oracle CRM/CX systems as the authoritative customer view for AI-driven analytics.
Architecture
Decisions and tradeoffs
Conservative match thresholds
Prefer leaving a duplicate over merging two real customers.
- Why
- An incorrect merge is very hard to unwind and corrupts downstream reporting silently. A surviving duplicate is visible and fixable.
- What it cost
- A residual duplicate rate that needs periodic manual review.
Validation inside the pipeline
Automated quality tests on every run.
- Why
- Continuous measurement is what produced the four-fold governance improvement; periodic audits only find problems long after they land.
- What it cost
- Longer runtimes and a test suite that must evolve with the data.