12 December, 2016
Users Tap Mix Of Tools To Mine Big Data Analytics Architecture
Predictive modeling, machine learning and other advanced analytics applications help dig the business value out of big data systems — but it takes a lot of tools and effort.
Before it deployed a Hadoop cluster five years ago, retailer Macy’s Inc. had big problems analyzing all of the sales and marketing data its systems were generating. And the problems were only getting bigger as Macy’s pushed aggressively to increase its online business, further ratcheting up the data volumes it was looking to explore.
The company’s traditional data warehouse architecture had severe processing limitations and couldn’t handle unstructured information, such as text. Historical data was also largely inaccessible, typically having been archived on tapes that were shipped to off-site storage facilities. Data scientists and other analysts “could only run so many queries at particular times of the day,” said Seetha Chakrapany, director of marketing analytics and customer relationship management (CRM) systems at Macy’s. “They were pretty much shackled. They couldn’t do their jobs.”
The Hadoop system has alleviated the situation, providing a big data analytics architecture that also supports basic business intelligence (BI) and reporting processes. Going forward, the cluster “could truly be an enterprise data analytics platform” for Macy’s, Chakrapany said. Already, along with the analytics teams using it, thousands of business users in marketing, merchandising, product management and other departments are accessing hundreds of BI dashboards that are fed to them by the system.