Case Study

Multinational Banking Corporation Reduces Costs and Complexity with Big Data

WWT provides big data engineering to understand traffic and complexity


When you’ve grown so big, sometimes it’s hard to keep track of everything that’s going on. A multinational banking corporation had technology investments that were increasingly complex and inefficient. Although they knew they had lots of data that might point to the source of the problem, they weren’t sure how to use the data to reduce complexity and save costs.

Six months prior, WWT performed an initial assessment of the bank’s data to demonstrate our capability and to provide the bank with a better understanding of the data they had and what they could do with it. When it was time to dig in and perform a full capacity analysis, the bank came to our experts for help.


Because of our deep understanding of not only big data analytics, but also networking and virtualization, we were in an excellent position to provide the bank the capacity analytics they needed. After an initial technology briefing, we dived into the bank’s existing tools and capabilities. Then in an in-depth, collaborative workshop, we matched up the needs of the bank to our capabilities.

Much of the work of a big data engagement, and an often-overlooked aspect of big data analytics, is the data engineering needed before the data science can be applied. While companies are increasingly “offshoring” big data work, our in-house engineering team had the people and resources to perform complex analyses and engineer across a number of network layers and technologies. We were able to extract, clean and integrate network data from a variety of disparate sources to provide the bank a detailed Network Traffic Assessment.


As a result of our assessment, the multinational bank gained insight into where the majority of the complexity exists in their network and what can be done to reduce or offload the traffic, which should offer anywhere between 10-15 percent cost reduction overall. By being smarter about network complexity and traffic, the bank now has a better estimate of their savings potential and a solid roadmap for scaling up in the future.

In addition to performing the analysis, we’re providing training to the bank’s own engineers so they can apply these kinds of techniques for their next project and be increasingly self-sufficient going forward.