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Published on Dec 3, 2016
The Howard Hughes Corporation partnered with 47Lining to develop a managed enterprise data lake based on Amazon S3. The purpose of the managed EDL is to fuse relevant on-premises and third-party data to enable Howard Hughes to answer its most valuable business questions. Their first analysis was a lead-scoring model that uses Amazon Machine Learning (Amazon ML) to predict propensity to purchase high-end real estate. The model is based on a combined set of public and private data sources, including all publicly recorded real estate transactions in the US for the past 35 years. By changing their business process for identifying and qualifying leads to use the results of data-driven analytics from their managed data lake in AWS, Howard Hughes increased the number of identified qualified leads in their pipeline by over 400% and reduced the acquisition cost per lead by more than 10 times. In this session, you will see a practical example of how to use Amazon ML to improve business results, how to architect a data lake with Amazon S3 that fuses on-premises, third-party, and public data sets, and how to train and run an Amazon ML model to attain predictive accuracy.