Locations

The most common challenges with location data include fragmented, siloed, and incomplete records spread across multiple systems and sources. This lack of data integrity makes it difficult to manage risk, reporting, and compliance; optimize routes, pickups, and deliveries; and reach and engage customers and other constituencies.

Accurate location data helps ensure goods move seamlessly from production to supplier to end customer. By enabling logistics teams to track shipments in real time, optimize routing, and accurately predict delivery windows, companies can avoid costly routing errors, shipping and delivery delays, and excess fuel consumption. 

Tamr’s Locations data product uses patented, pre-trained machine learning models, multi-source enrichment, and patented geospatial mastering to match, resolve, and enrich addresses and locations. Tamr also eliminates data silos to provide a unified view for locations such as retail stores, warehouses, clinics, campuses, or supply-chain nodes.

Yes, using latitude/longitude and polygon-overlap techniques, Tamr can match locations by physical proximity, even when the addresses look different.

Tamr’s AI-native master data management (MDM) solution complements traditional geocoding solutions by going beyond simple translation to provide multi-source enrichment. Using Tamr, organizations can combine data from various geocoding and enrichment providers to resolve, verify, and enhance every location point across the enterprise. Further, using geospatial and polygon matching, Tamr can match records that appear distinct but are actually the same.

With trusted location data, organizations can achieve measurable financial and operational outcomes including reduced costs, increased revenue, accelerated time-to-market, mitigated risk, better regulatory compliance, and improved customer satisfaction.