Leading Fintech Eliminates Analytical Silos with OLAP on BigQuery
Company Size
1,000+
Region
- America
Country
- United States
Product
- Kyvos
- MicroStrategy
- Google BigQuery
Tech Stack
- OLAP
- Cloud Computing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
The customer is a US-based financial technology services company with a worldwide reach spanning over 100 countries. The company processes more than 50 billion transactions annually, providing payment services to approximately 3.5 million merchants and business locations, including retailers and financial institutions. Despite having uniform data, the company was facing challenges in creating and automating a single consolidated view of all its data for hundreds of users. The company was using 20 different cubes for core reporting, which was leading to siloed reporting due to the inherent limitations of the analytical environment. The company was also facing challenges in terms of high time-to-insights, as the time to publish the cube, even for a single data source, exceeded 6 hours. Changes in data or incremental refreshes required full reprocess downtime, which took over 24 hours, and users could not access the system for this duration. The company had maxed out the capacity of their Google environment and was spending massive amounts on cloud computing.
The Challenge
The US-based financial technology services company was struggling with creating and automating a single consolidated view of all its data for hundreds of users. Despite having uniform data, reporting was siloed due to the inherent limitations of the analytical environment. They initially attempted to build MicroStrategy intelligent cubes from data residing in Google BigQuery. However, there were limits on the amount of data each cube could hold. As a result, they were forced to split their data and create separate cubes for combinations of geographic regions and data sources. This led to an inability to get a single consolidated view of their data as they were using 20 different cubes for core reporting. The time to publish the cube, even for a single data source, exceeded 6 hours. Changes in data or incremental refreshes required full reprocess downtime. Reprocessing took over 24 hours, and users could not access the system for this duration. They maxed out the capacity of their Google environment. Despite running on a large server, there was no room for additional data. They were spending massive amounts on cloud computing.
The Solution
Kyvos helped the company consolidate 20 intelligent MicroStrategy cubes into a single cube for all data sources, geographic regions, and merchant information. Cloud-native, Smart OLAP™ technology enabled aggregations on a much broader dataset to build a holistic cube versus piecemealing smaller cubes. With a single cube approach, Kyvos enabled 1.6 Billion total fact records aggregated in the Kyvos cube, 2.4 Million unique Merchant IDs and 840K unique Customers IDs, 25 months of historical data, 90-minute cube build time, and Query response SLA <10 seconds. Besides, Kyvos eliminated in-memory limitations as it stores the cube on Google storage. Kyvos expanded the horizon of MicroStrategy to build the OLAP layer directly on the cloud. A single cube had the entire structure of dimensions, measures, and hierarchies. Users could plug in MicroStrategy directly into the Kyvos cubes and instantly access the Kyvos semantic layer. There was no need to do any manual settings or recreate the cube. They could pull everything from Kyvos. Performance improved substantially as instead of plugging MicroStrategy directly on BigQuery, they could now get their queries answered from the Kyvos cube itself. This also helped control compute costs and save on MicroStrategy usage.
Operational Impact
Quantitative Benefit
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