Big Data and Retail: Building Shopping Lists and Data Processing Engines

Point Inside works with top retailers including Target, Lowe’s, and Meijer to combine the physical location of products in-store with enterprise data and mobile shopper usage into a single, scalable platform that answers shopper’s top 2 questions, “Do you have it?” and “Where can I find it?”

Retailers can stock hundreds of thousands of products per store, but how do shoppers find all of them and how does HQ manage them from store to store? In this talk, we’ll discuss two recent retail big data projects that we’ve recently completed to help retailers manage and optimize their data:

· A shopping list builder that helps choose complementing items based on collaborative filtering approach using sales history

· A project using AWS managed services to build a real-time data processing engine in 1 day

SPEAKER BIOS

Yerbolat Dosbayev is a Sr. Research Scientist at Point Inside and has over 20 years of IT experience of which the last 10 have been in Big Data and Machine Learning applied to various industries and areas, including bioinformatics, social networking, genealogy, online advertising, and retail. Yerbolat holds masters degrees in both Applied Maths and Computer Science.

Sergey Podlazov is a Software Development Manager, Big Data at Point Inside. Sergey started his career in technology consulting and later transitioned to product development in the Business Intelligence space. Prior to Point Inside, Sergey spent four years at Amazon.com where he built the company's financial data warehouse with the processing capacity of a billion transactions per day. Sergey holds a Master's Degree in Information Systems from the University of Kansas.