Case Study
How a global provider of business decisioning data created an analytics sandbox solution for customers
OUR CLIENT
This American company was founded more than a century ago as a credit reporting agency. The business has evolved to become a leader in the business data and credit industry offering subscription-based products, business reports, data licensing agreements, and other services. The company generates more than $2 billion in annual revenue.
THE CHALLENGE
Changing the old ways—Creating a sandbox studio data analytics environment for customers
The customer was evaluating service providers with product offerings to accelerate its sandbox environment for data analytics to enable end-user customers to collaborate, create insights, and merge third-party data for their own model production. The solution needed to work with the company’s existing Spark cluster, powered by Databricks. Specific focus was on leveraging user-friendly drag and drop capabilities to enhance data scientist productivity and enable business analyst persona users to realize more use cases and accelerate revenue.
THE TRANSFORMATION
Technology empowered a custom-made solution for digital data transformation
The UST Data Platform Workflows was adopted as the low-code Spark-based data preparation and artificial intelligence/machine learning (AI/ML) modeling solution powering the customer’s sandbox environment. With Workflows’ native integration with Databricks and its prebuilt toolkit of 300+ processer nodes, we built a comprehensive user interface (UI) portal, delivering an end-to-end framework enabling end-user customers to identify a qualified list of prospects. Within the analytical sandbox, customers’ data engineers were able to design an application using Workflows’ visual designer to enable modelers to combine traditional credit/fraud detection techniques with advanced ML models. Workflows was used to execute smart entity matching functionality enabling new user functionality.
THE IMPACT
Extensive transformation through data engineering
Workflows prebuilt processer nodes enabled productivity for all three key target personas in the following ways:
- Data scientist—drove productivity by reducing data preparation and simple model build efforts
- Business analysts—enabled business analysts who understood analytics but were not proficient coders to unlock new use cases
- Data engineers—drove productivity when doing data evaluations, extract, transform, and load (ETL) processes for model deployment into production, and managing diagnostic analyses with simple, easy drag and drop UI capabilities, powered by UST Data Platform Workflows
With our domain expertise and technological competencies, we can help you with the right solutions. Learn more about the expertise and resources that helped us achieve success on this project.
RESOURCES
https://www.ust.com/en/what-we-do/digital-transformation
https://www.ust.com/en/what-we-do/digital-transformation/data-analytics