CASE STUDY

Customer Analytics Through a Cloud Data Platform

CLIENT’s Challenge

• Legacy data environment lacked agility resulting in long time-to-market for new analytical solutions and higher operating costs
• Gaps in customer data created a disjointed customer experience leading to missed cross selling and conversion opportunities
• Data was poorly understood, scattered across a variety of repositories, and delivered in batches with limited access to real-time data
• Legacy platforms in data center and private cloud could not scale and had increasing costs
• Security and privacy risks for the data platforms were managed reactively

OUR APPROACH

• Delivered multi-layered architecture for a Cloud Data Platform including a data lake, streaming hub, data refineries, data warehouse, user sandboxes, analytic workbenches, and data governance tools
• Introduced best practices for team-based architecture work including decision management and architect core hours
• Guided delivery teams using enterprise and data architecture as “North Star”
• Enabled automated ingestion and lineage for data lake using a data registry
• Architected solutions for security, access control, monitoring, data tokenization, and individual rights
• Applied agile delivery principles and orchestrated a multi-team program; provided Scrum training and coaching
• Established operational data governance framework

skills & tech leveraged

AWS Data Lake Architecture, Data Pipeline Design – Confluent Kafka, Data Security and Tokenization, Agile Delivery, Informatica Data Governance Suite Implementation

THE RESULTS

• Enabled agile analytical solutions: Every day, 1000+ datasets are automatically ingested from files and streams, 40 data scientists are developing models, hundreds of data pipelines populate data warehouse tables for user queries and dashboards
• $7.5M – 30M projected revenue uplift expected as a result of optimized global marketing
• The platform enabled improved propensity and acquisition models resulting in a +8.5-10% lift
• Enabled retirement of legacy systems resulting in over $3M savings in operating costs and deferred investments