A multinational media and information company faced challenges maintaining critical, accurate data. It had outgrown its manual curation processes and looked to Tamr to provide a better solution for continuously connecting and enriching its core enterprise information assets (data on millions of organizations with more than 5.4 million records pulled from internal and external data sources).
Using Tamr, one project, estimated to take six months, was completed in only two weeks, requiring just forty man hours of manual review time — a 12x improvement over the manual process. The number of records requiring manual review shrunk from 30% to 5%, and the number of identified matches across data sources increased by 80%—all while clearing the company’s 95% precision benchmark. The disambiguation rate—the rate of resolving conflicts—rose from 70% to 95%. Furthermore, the knowledge Tamr gleaned from its machine learning activities means that future data integration will take even less time per source.
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