Customer Scenario

Even in the entertainment industry, data drives business decisions. That’s why our client, a major entertainment company, wanted to find a way to gain better insight into credit card data across their lines of business and products. However, payment records from the various credit card providers were incongruent and cumbersome to read. The payment providers offered data dumps, but its current technology made it difficult to parse and analyze. They lacked visibility into the insights they desired from multiple angles and perspectives and clarity on rebates owed to the client from the providers. This resulted in additional costs, as the client employed a specialized third-party application for the information. Adding to the challenge was the client’s desire to continue using solutions they were already invested in, specifically WebMethods and Tableau.

Solution

The design of the solution involved first assessing and analyzing the existing data, architecture, and schemas. Once reviewed, a hybrid solution was developed. The solution plan included the client’s existing WebMethods implementation, SFTP, AWS, Tableau, and because massive amounts of data involved, Snowflake for data warehousing. 

The event-driven process is kicked off once the SFTP server receives a file. From there, transformation into a standard CVS format is done and loaded into a JMS topic for multithreading. Batch groups of files are then sent to an AWS S3 bucket, validated, and then consumed into a staging schema by Snowflake’s Snowpipe API. Once reconciliation of the files is complete, joins with various tables permit different stats, like the line of business and product, to be tracked and labeled with primary keys aligned with record and source type. This data was then available for use within Tableau for analysis by the client’s finance team.

Result

The solution created almost immediate business value. The finance team was able to track and analyze credit card data over time by source as well as align it to business units. 

Data-driven business decisions happen more quickly thanks to the credit provider information being normalized and staged for the existing data tools. The client was able to recognize an ROI for the project quickly, thanks to adding a few new elements to existing owned technology stacks.  Additionally, the ability to process and analyze the information reduces their need to rely on external third parties, further reducing cost.