Bank Uses Big Data to Reduce Costs and Complexity

Full capacity analysis leads to better understanding of complex and inefficient technology investments


When a business experiences massive growth, it’s often hard to keep track of everything. A multinational bank's technology investments over the years had become increasingly complex and inefficient. Though they knew existing data might point to the source of the problem, they weren’t sure how to leverage that data to reduce complexity and save costs.

Six months prior, WWT had performed an initial assessment of the bank’s data to demonstrate our big data capabilities, provide a better understanding of the data they had, and outline what they could do with it.

When it was time to dig in and perform a full capacity big data analysis, the bank came back to our experts for help.


Because of our deep understanding of big data analytics  — combined with extensive networking and virtualization expertise — we were in an excellent position to deliver the capacity analytics the bank needed. After an initial technology briefing, we dove into the bank’s existing tools and capabilities. Through an in-depth, collaborative workshop, we then aligned the bank's needs with our data and analytics capabilities.

A good amount of data engineering, which is oft overlooked and comprises the majority of any data engagement, needed to be completed before the data science could be applied. While companies are increasingly “offshoring” big data work, WWT's in-house Data Analytics & AI team team had the people and resources to perform complex analyses and engineering 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 develop and deliver a detailed Network Traffic Assessment.


As a result of our big data analysis, the customer gained insight into where the majority of the complexity existed in their network and what could be done to reduce or offload the traffic. We estimated this could offer anywhere between 10 to 15 percent of overall cost reduction.

By being smarter about network complexity and traffic, the bank now has a better idea of potential savings and a solid roadmap for scaling up in the future.

In addition to the analysis, we trained bank engineers to apply these same kinds of techniques to their next project so they could be self-sufficient going forward.

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