Optimizing business performance analytics delivery for a leading customer analytics company

Business Need

Enhancing the Productivity and Operational Efficiency

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.

Technology Challenge

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.

This collaborative approach with the client delivered benefits by addressing the major pain areas first, and incrementally building towards a mature, modern insights platform for business performance reporting. The client was quickly able to move to the next logical phase of maturity. They are now enjoying more comprehensive business intelligence made possible because of the advanced cloud data warehousing techniques used. In addition we are using advanced analytics to predict the future performance based on trends and various indicators. We are now working with the client on creating recommendations and next best actions using a variety of advanced analytics algorithms.

Benefits Delivered

  • Timely delivery of business performance analytics including finance and sales

  • Reduction of overheads by improving data integration and data quality

  • Ability to adopt higher data science maturity processes such as predictive & prescriptive analytics

Recognitions

Thought Leadership

Our authors

Jaideep Prabhu

Professor, University of Cambridge

John Monks

IT Leadership Roles, NYPD & HP

Alison Booth

IT Leadership Roles, Balfour Beatty, Allianz

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