The DATAMATION news site published a list of 5 Big Data Apps with Effective Use Cases. Tamr’s engagement with Thomson Reuters was included in the article:
#5. Big Data application: Tamr
How this Big Data app: Tamr is a data-connection and machine-learning platform designed to make enterprise data as easy to find, explore, and use as Google. According to Tamr, due to the cost and complexity of connecting and preparing the vast, untapped reserves of data sources available for analysis, most organizations use less than 10 percent of the relevant data available to them.
It’s just too manual, too inefficient and too expensive to connect and ready the massive variety of internal and external data for analytics and other applications critical for business growth. Tamr argues that if the industry is going to be successful at helping customers manage the growth and variety of data that lies ahead – from internal sources, external public and private sources, Internet of Things feeds, etc. – a complete overhaul of traditional methods of information integration and quality management will be required.
Use case of note: Multinational media and information company Thomson Reuters 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 that Thomson Reuters estimated would take six months was completed in only two weeks, requiring just forty hours of manual review time – a 12x improvement over the previous process. The number of records requiring manual review shrunk from 30 percent to 5 percent, and the number of identified matches across data sources increased by 80 percent – all while achieving Thomson Reuters’ 95-percent precision benchmark.
Tamr says that the disambiguation rate (or the rate of resolving conflicts) rose from 70 percent to 95 percent. Furthermore, the knowledge Tamr gleaned from its machine learning activities means that future data integration will take even less time per source.”
Read the Full Article (External Link)