Governed Data Lake for Customer Critical Data Analytics

Retail chains that have brick and mortar stores as well as online platforms often struggle in identifying the customers visiting their site. Even with all the information available at their disposal, the probability of identifying the customers accessing their website is a mere 30%.
This blog discusses on the system used to tracking and identifying customers who interact with our client’s online portal and then shop in-store and vice-versa.

Benefits

Identifying the change in customer preference based on the changes in climate and geography and its impact on the customer’s buying patterns is possible only if the system can synthesize fragments of customer data existing in data lakes and create a customer 360 view.
Identifying potential correlations of these factors with their buying pattern would enable our clients to target potential customers with more relevant marketing messages and customized offers.

Building the Data Lake using Big Data Technology

The above stated benefits can be addressed by crunching historical data and performing advanced predictive analytics on top of it. With different forms of data coming in from multiple channels across the organization, having a single location which can hold all the data is desirable.
This calls for a Data Lake implementation that can be had as a foundation for running the analytics program. After reviewing several potential vendors who could implement a Data Lake, InfoTrellis was chosen based on a successful proof of concept delivered by InfoTrellis.
A managed Data Lake was built using Big Data Technology centered on Hadoop, which can hold multi-channel data of various formats. This Data Lake supports the ongoing scheduled and ad-hoc analytics by different teams across various functions of the client. It will also serve as the foundation for all analytic and reporting activities in the future. Our team assisted the Marketing IT team of the client with the design and ingestion of huge volume of customer and marketing data into the Data Lake.

Data Lake Architecture

Over the course of the project InfoTrellis ingested data from over 40 different data sources, bringing in over 100TB of data into the Data Lake. Data Sources were either near-real-time or scheduled batch. The ‘near-real-time’ data had to be handled “as soon as possible” but without a strict latency requirement.

For more information:http://www.infotrellis.com/governed-data-lake-customer-critical-data-analytics/