INDUSTRY: Financial services
SOLUTION: Data-driven decision making
TECHNICAL USE CASE: Data ingest and ETL, machine learning
Facteus (formerly ARM Insight) is a leading provider of financial data business intelligence (BI) solutions for processors, investment companies, financial institutions, and retail corporations. Struggling with legacy systems that couldn’t scale and siloed resources that blocked innovation, Facteus leveraged the Databricks unified data analytics platform to make intelligent business decisions, automate manual operational tasks and optimize their data strategies.
Legacy systems resource intensive and complex to manage
Most of their financial services customers are running on legacy mainframe systems. This made it challenging and resource-intensive for them to efficiently extract data and analyze it with machine learning. Furthermore, Facteus lacked the ability to perform distributed analytics which limited their ability to support all their customers.
- Limited to analyzing small samples of data, resulting in poor visibility and insights into customer requirements and industry trends.
- Processing 100 billion+ transactions were slow due to a lack of scale and processing power — taking weeks to uncover insights for customers.
- Internally at Facteus, their data science and engineering teams were siloed and working off single-node machines, impeding their ability to scale analytics across 6000+ financial services customers.
- PCI compliance to protect consumer data was paramount. Other tools didn’t give them the control they required to ensure consumer data was protected.
Scalable infrastructure and improved data science productivity
Databricks provides Facteus with a unified data analytics platform that has fostered a scalable and collaborative environment across data science and engineering, allowing data teams to more quickly innovate and deliver ML-powered services to Facteus customers.
- Fully managed platform with automated cluster management simplifies the infrastructure and operations at any scale.
- Collaborative notebook environment with support for multiple languages (SQL, Scala, Python, R) enables a diverse team of users to work together in their preferred language.
- Native support for MLflow enables data science teams to easily track model performance and rapidly iterate across their millions of models in a systematic fashion.
- Native support for Delta Lake allows their data engineering team to reliably run and scale both batch and streaming pipelines on the same data.
- Leveraging their own AWS environment for compute and storage provides them with complete control over their data, ensuring compliance with PCI requirements.
Delivering new data products to market faster
Onboarding banks faster is Facteus top priority. The faster Facteus can turn their data into revenue and insights back to their customers. With Databricks, they have been able to significantly increase processing speeds while doing so at massive scale, improve collaboration across teams, and leverage machine learning to deliver timely insights to their customers.
- Improved operational efficiency: Features such as auto-scaling clusters and support for Delta Lake and MLflow has improved operations from data ingest to managing the entire machine learning lifecycle.
- Better cross-team collaboration: Shared notebook environment with support for multiple languages has improved team productivity.
- Faster time-to-insight: Improved response times and delivery of insights to customers by up to 20x. What used to take weeks to access and process large volumes of data now takes only hours.