Optimizing business performance analytics delivery for a leading customer analytics company
Our client, a leading customer analytics company in UK, wanted to improve their client relationship management capabilities by creating data-driven growth conversations. Timely and accurate business performance analytics for their customer accounts was needed in multiple areas such as finance, projected performance, media advertising effectiveness, conversions, and sales. However, information asymmetries and latencies were resulting in significant overhead and inconsistency. In addition, the client was finding it difficult to systematically adopt more advanced analytics and insights until they had the basics in place. Timely and accurate business analytics & recommendations would also improve the calculators used to project future performance by leveraging benchmarking across portfolios.
Conventional machine learning (ML) model execution systems were impacting timely generation of insights. This required migrating to a modern machine leaning platform. There were a number of systems of record, and data from each of them needed to be aggregated, cleaned and then made available for reporting. The current architecture was leading to significant reconciliation effort. A cloud data strategy had been adopted but it wasn’t being optimally executed because of master data management challenges and data pipeline management issues. The end user channels for consuming the insights were also based on legacy technologies and not scalable. Thus significant effort was being expended in keeping the lights on for business analytics.
Solution & Approach
Ignitho collaborated with the client at every step to define and execute a phased technology & data strategy to effectively resolve the challenges and optimally utilize cloud data platform investments. A systematic approach was adopted to achieve this transformation and reduce technical debt.
- Conducted detailed analysis to select & implement the right machine learning platform technologies. This research based approach helped to migrate conventional systems with latencies of several hours to a modern platform that completes in a matter of minutes.
- Created a map of data quality, sources, & inconsistencies to fixed them upstream as much as possible to avoid creating additional technical debt.
- Automated the data pipeline and put in place monitoring mechanisms
- Consolidate and organized the data into the cloud platform and optimized it for business reporting and analytics.
- Upgraded the information distribution and consumption mechanism so that insights are available in real time and easy to use.