Urban Science Solves Big Data

This is the era of big data. The collecting of massive amount of Data is directly affecting financial health of the company. As more and more data pours through the networks of organizations worldwide, the race to extract right information form ‘big data’ begins.

To leverage big data, Urban Science has selected the Progress Corticon (News - Alert) business rules management system (BRMS). Based in Detroit, Michigan, Urban Science offers solutions to help automotive OEMs manage and optimize their dealer network.

Progress Corticon business rules management system will enable Urban Science to adopt a strategy to help automotive retailers to allocate resources, grow market share and improve profitability by using electronic sales leads solutions and other technologies.

"We held a five-month proof of concept exercise to rigorously test the performance of competitive products under high transaction volume and Big Data scenarios,” said Greg Davidson, chief information officer at Urban Science. “We found that the Progress Corticon BRMS solution produced the fastest results, and most importantly, kept us within the parameters of our service level agreements (SLA). Given this, we have now decided to implement a permanent, automated business rules solution that will help us continue to deliver competitive advantages to our e-Commerce customers.”

The Progress Corticon BRMS is used by customers to automate sophisticated decision processes and reduce development and change cycles.

 Urban Science will use the Corticon BRMS to capture leads from more than 30 online sources, analyze the data and route to the appropriate source – all within two seconds.

The solution enables automotive retailers an inherent ability to understand buyer behavior and act upon it extremely quickly, giving these retailers a powerful competitive advantage.

According to Dr. Mark Allen, chief technology officer of Decision Management at Progress Software (News - Alert), only the Corticon BRMS is proven to perform and scale to solve the most sophisticated decision-making problems.

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