Utilized SQL Server for staging and modeling the client's source system data into data marts so that we could provide a single source of truth to users.
Data integration solution to extract, transform, and load (ETL) data into data marts. This provides a modularized and systematic way of loading data for easy administration and maintenance.
SSAS Tabular models have also been developed so that data analysis could be done using multiple visualization tools while keeping consistent across reports.
Here are some insightful blog posts from the BlueGranite team for banking, insurance, and capital markets topics and scenarios.
Clients prize a global insurance giant for its custom coverage, product diversity and worldwide presence. Specialized offerings in multiple markets have cemented the company’s foothold as an industry trailblazer. When leaders here wanted to increase service speed and agility, they explored the possibility of expanding their business intelligence platform to the cloud.
Accuracy is crucial when it comes to managing money – especially when tackling hundreds of millions of dollars in annual revenues. A U.S.-based financial services powerhouse monitors its money and operations with precision, but the complex process was recently on the verge of becoming a major headache. The company’s services division handles all efforts from the consumer banking department, including checking, loans, and wire transfers. To better understand the function and performance of each department under its umbrella, the services division was tracking over 600 metrics manually every month. As the company grew, so did the data – adding more complexity.
Waiting on developers for crucial business insights can be costly. That’s why the internal spending and accounting division of a U.S.- based insurance trailblazer decided to take matters into its own hands. The forward-thinking group made the decision to step off the reporting treadmill – that endless cycle of business users asking developers for reporting revisions, waiting weeks for request fulfillment, then starting the cycle over by asking for additional changes. In the process of forging a new path, it discovered major savings.
Learn about the value of predicting customer churn rates with historical data
In today's competitive market, maintaining a high customer retention rate is critical to success. Understanding when a customer may be at risk to break ties with your organization could help you take a more targeted approach to relationship management, effectively plan for future financial impact, and even prevent the loss of customers in the first place.
Using the right tools, it is possible to proactively plan for customer churn by analyzing historical data from previous and existing clients. In the webinar recording below, we demonstrate the value of customer churn prediction as well as discuss how to accurately predict which customers are likely to turn over.