First Data

Learn how one of the biggest U.S. payment processing companies improved their data quality, built a reliable Big Data reservoir, and uncovered millions of dollars owed to them in unpaid balances.

Business objective

Due to its strategy of growth by acquisition, First Data was composed of several different payment networks (acquired companies), each with their own technical stack and sales processes.

First Data aimed to reach a stage where all of its payment networks could access the same data and use standard practices, sales strategies, and technology.

Project requirements

  1. Move from data lake to reservoir: Transition away from data being stored, maintained, accessed, and utilized by individual payment networks, to data being collected, accessible, and usable in a single place.
  2. Be able to instantly work with data, including performing analytics and detecting real time patterns.
  3. Detect payment fraud: Although fraud is also handled by banks and payment technology companies like Visa and Mastercard, First Data wanted to expand their product offer with services such as fraud detection.

Solution

Ataccama migrated data from former individual companies to a single location with Hadoop, and implemented a data quality solution so that data could be better analyzed, understood, and utilized.

  • Hadoop Implementation: In the first stage, Ataccama helped First Data migrate their data from individual companies to a Hadoop cluster.
  • Data Profiling and Cleansing: Migrating data to a single location on Hadoop did not increase understanding of that data by itself. Ataccama made the company’s data more useful by implementing a comprehensive data quality solution, using profiling to allow First Data to better understand the contents of its data lake, and cleansing to improve and standardize their data.

Ataccama products used

  • Data Quality Center (DQC): First Data migrated all data to Hadoop, then used our DQC engine to determine the content of that data. They improved data quality by structuring data in a more useful way and cleansing it.

  • Master Data Center (MDC): Once their data was structured, First Data used Ataccama’s MDC to find duplicates in their client base for their sales team.

  • Big Data Engine (BDE): As First Data wanted to scale up with Hadoop, BDE was utilized to ensure the migration went smoothly.

  • Reference Data Manager (RDM): Ataccama’s RDM was used to define and standardize reference data values, and to identify fraudulent numbers with phone number blacklisting.v

Project phases

 

Proof of Concept and early work in Big Data and Reference Data: Ataccama gave meaning to Hadoop by defining reference values such as payment and error codes, saving the company $1 million at the start of cooperation.

 

Standardization: Data was not consistently organized across various platforms, so Ataccama worked to standardize all company data.

 

Enrichment: Ataccama helped the company enrich their data with information that was not originally available, such as addresses, postal codes, or transaction types.

 

Distribution and business user empowerment: Ataccama allowed all First Data analysts to access their standardized and enriched data in Hadoop. We also enabled them to build small databases on demand, which proved especially beneficial to their marketing department.

Stats (data volumes, sizing, performance):

  • 250 GB of data is compressed each day, equal to approximately 5000 transactions per second.
  • First Data’s Hadoop cluster was originally 12 nodes, and increased to 32 nodes during our cooperation.
  • First Data systems now include data dating back to 2012, which is roughly 1 terabyte of data for every 4 days.

Ready to harness the power of Big Data for your company?

Get in touch to kick off your Hadoop initiative with our free BDE test drive, including:

  • Hadoop cluster setup
  • Data assessment
  • Assessment of potential Big Data ideas
  • Implementation of one Big Data idea

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