Leading Fintech Eliminates Analytical Silos with OLAP on BigQuery

A leading US financial technology services company faced the daunting task of creating and automating a single holistic view of all its data for hundreds of users. With a worldwide reach spanning over 100 countries, they were processing more than 50 billion transactions annually, to analyze payment services they were offering to 3.5 million merchants, including retailers and financial institutions.

Despite their data being stored in Google Big Query, reporting was siloed due to the inherent limitations of their MicroStrategy environment. Since the amount of data each cube could hold was limited, they were forced to create 20 different cubes for core reporting, using combinations of geographic regions and data sources.

This created critical BI challenges for the fintech company as insights were delayed significantly. Publishing a single cube required 6 hours of processing and incremental refreshes took over 24 hours of reprocessing with no user access to the system in between.

Download this case study and learn how they used Kyvos to achieve:

• Ability to build and use a single cube for all their data and eliminate silos
• A consolidated view of all their data across geographies, merchants, and data sources
• Exceptional BI performance with less than 10-second query response SLA
• Ability to analyze 25 months of data vs. 16 months in the past
• Reduced compute costs on Google Cloud and savings on MicroStrategy costs