The client was a leading Australian multinational bank with business spread across New Zealand, Fiji, Asia, USA and the United Kingdom. It provides a variety of financial services including retail, business and institutional banking, funds management, superannuation, insurance, investment and brokering services.
A leading US company needed to consolidate customer master data in order to get high quality ‘golden’ customer information from a large number of disparate source systems.
Assessment and Implementation of enterprise wide data quality initiative as per Human Resource enterprise application standards that includes Definition, Discovery, Assessment, Remediation, Enrichment and Monitoring of Corporate Systems.
Retail organization needed to migrate a system that generates a propensity score that indicates how likely are customers to buy protection agreements on the home appliances that they purchased from SAS to Hadoop in order to cut costs and be able to scale to the growing size of the datasets.
In order to provide a better customer experience, one of the world’s largest full-service dining companies required a solution that leverages machine learning to predict how long customers will have to wait for their order to be served.
A direct banking and payment services company with one of the most recognized brands in US required a solution for lifting and shifting traditional legacy application to AWS using cloud native approach.
