Case Study Big Data Analytics
Our Investment Bank client wanted to summarise all electronic touch points for their 300k strong workforce to assist with the detection of data anomolies and provide company processing efficiencies. This project extracted 20m + daily data points from around 100m rows of data covering internet usage, application usage, emails, system processing and location data.
The ETL (Extract Transform and Load) process pulled the geographically disparate data into a single data warehouse, modelling the data using a Microsoft SSAS cube. The result was a powerful analytics system that the business used to understand behavioural patterns en-masse.