Case Study: Multinational Media and Information Company (Thomson Reuters) achieves 92% faster integration and 90% fewer manual reviews with Tamr Inc.

Information Services Leader Uses Tamr to Automate Data Connections, Slashes Manual Reviews by 90% and Cuts Process Time by Months

The Multinational Media and Information Company faced mounting scalability and quality problems as its data sources grew to hundreds and it managed more than 5.4 million records on millions of organizations. Manual curation (spreadsheets, dispersed subject-matter experts) was slowing operations and hurting service levels, so the company engaged Tamr Inc. to automate continuous connection and enrichment using Tamr’s machine‑learning data integration and curation platform.

Tamr Inc. converted source XML to CSV, ingested and de‑duplicated three core sources, and applied machine learning with targeted expert review to generate suggested matches and resolve conflicts. The project completed in two weeks versus an estimated six months (≈92% time saved), cut records requiring manual review from ~30% to 5% and manual review to about 40 man‑hours (~5 days), increased identified matches by ~80%, and improved disambiguation/accuracy from 70% to 95% while maintaining a 95% precision benchmark.