Case Study


Credit: Cloudera (Screenshot from video at

Credit: Cloudera (Screenshot from video at

Delivering personalised omni-channel customer experience through real-time analytics

Western Union’s business model has changed over the years. From connecting customers through communications with its telegraph system in the mid-1800s, today it connects people financially through cross-border, consumer-to-consumer money transfers, bill payments, and other financial services. But through it all, customer-centricity and connecting people has always remained at the core.

Based on the company’s 2016 Annual Report highlights, Western Union completes 31 transactions per second on average and transfers around USD $80 billion annually in consumer-to-consumer transactions.

But operationally supporting these transactions is not enough. The massive volume of transactions generates an equally enormous amount of transactional information, including data about both senders and receivers. This data contains insights which can help the company create products and services that are relevant to customers and help differentiate Western Union in a competitive marketplace. These insights can help the company simplify transactions from different channels and devices, protect transactions and provide better understanding of each customer.

Evaluation criteria

The company identified two key areas that would benefit from a solution that could bring together structured and unstructured data stores.

The first was customer experience. Based on user behaviour data, click stream information and mobile usage patterns, the company could drive a better, more personalised experience for senders as well as receivers.

The second area was security, risk, and compliance. By ingesting, processing, and applying analytic capabilities on multi-structured data streaming from mobile, web, and retail sources, Western Union could minimise risk and enhance anti-money laundering (AML) compliance at scale.

To accomplish these objectives, the team would need to revamp their engineering stack. Data would be the foundational pillar, on top of which they could apply insights.

For evaluating different technologies, the team identified several criteria. The first was performance and agility to handle structured, unstructured, and semi-structured information. Secondly the system should demonstrate rapid time to value and ability to make meaningful impact. The technology’s customisation capabilities should be able to support a global enterprise. Finally, management capabilities including data segregation, auditing, and monitoring and long-term cost efficiencies at large scale were factored in.

Western Union looked at several vendors and Cloudera had the highest aggregate across the above-mentioned criteria.


For an implementation of its size, Western Union anticipated to complete the project in a year. Exceeding expectations, the first production-ready Cloudera system was set up within just five months.  

Western Union’s enterprise data hub (EDH) was powered by a 64-node CDH cluster that was soon expected to grow to 100 nodes (case study is dated June 2014).

The hub feeds in structured data from multiple data warehouses as well as unstructured data including click streams, behavioral data, logs, and sentiment data collected by tools such as transactional, marketing, and other outreach systems.

A combination of Apache Flume, Apache Sqoop, and Informatica Big Data Edition (BDE) was used to collect data from the various sources. High-density Cisco Unified Computing System (UCS) servers formed the backbone of Western Union’s EDH.

The team was also building a transactional capability on top of the 100-terabyte (TB) hosted data set to provide rapid response times to critical systems servicing our customers cross-channel – from the web, mobile, even from retail agent locations.

The company’s 100 internal end users—including members of the business and engineering communities as well as data scientists—access the data in their EDH via Hue, offering a web interface for Hadoop, and Apache Hive, which offers a SQL-like interface.

Cloudera provided rigorous training for Western Union’s engineers, and hosted multiple training classes for about forty of internal end users, which accelerated adoption.

Business users also have access to several visualisation tools that integrate with the Hadoop cluster, offering an interactive, 360-degree view of the business against important trends and timelines, across its digital offerings.

Western Union’s data is secured and segregated with Apache Sentry (incubating) and Kerberos, and is monitored by Cloudera Navigator. This is of critical importance as Western Union is the custodian of its customers’ financial information. It was essential to ensure compliance and proper monitoring and auditing.


Responding to customer needs

The Cloudera enterprise data hub serves as a single repository to help Western Union understand its customers. It provides important insights from initial touch point and qualification and compliance checks, through the entire customer lifecycle, starting from the moment they come into one of Western Union’s networks—retail, web, or mobile. It allows customers to have a more seamless experience across multiple channels, to use products and services, and discover new ones.

This allows the company to deliver push relevant and meaningful offers. For example, in San Francisco, Western Union delivers targeted offers that are tailored to the Chinese culture at its Chinatown retail agent locations, messages tailored to Filipinos in Daly City, and to the Mexican community in the Mission District.

One insight revealed that many web and mobile customers frequently process repeat transactions. They send the same amount of money to the same recipient at the same time each month.

This then prompted Western Union to add a “Send Again” button to make the process of repeating payments much more convenient for the customer.

Improved risk management and compliance

The EDH delivered immediate value to Western Union by supporting predictive analysis on structured and unstructured data sets, at the time of transaction.

Consequently, the company was able to impact transactions in real-time and drive customer compliance in a way that drove better conversions for customers.

For instance, Western Union’s data hub revealed high transaction volumes between the US and one Asian community when it’s early morning on Wall Street, because the tech-savvy senders understand when new foreign exchange (FX) rates have just launched. This is just one example of the many variables Western Union could now use to anticipate and risk-decision its customers and their transactions.


In addition to the above benefits, implementing the EDH has lowered Western Union’s total cost of ownership (TCO). Managing these volumes of data in a standard data structure would have led to exponentially escalating costs.

The savings from the EDH can be invested towards new opportunities and new product innovation, as opposed trying to just pay for heavy-duty storage and database costs.

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