Big Data at an International Financial Services Firm
For one multinational financial services institution, cost savings is not only a business goal, it’s an executive mandate. The bank is historically known for its experimentation with new technologies, but after the financial crisis, it is focused on building its balance sheet and is a bit more conservative with new technologies. The current strategy is to execute well at lower cost, so the bank’s big data plans need to fit into that strategy.
The bank has several objectives for big data, but the primary one is to exploit “a vast increase in computing power on dollar-for-dollar basis.” The bank bought a Hadoop cluster, with 50 server nodes and 800 processor cores, capable of handling a petabyte of data. IT managers estimate an order of magnitude in savings over a traditional data warehouse. The bank’s data scientists—though most were hired before that title became popular—are busy taking existing analytical procedures and converting them into the Hive scripting language to run on the Hadoop cluster.
According to the executive in charge of the big data project, “This was the right thing to focus on given our current situation. Unstructured data in financial services is somewhat sparse anyway, so we are focused on doing a better job with structured data. In the near to medium term, most of our effort is focused on practical matters—those where it’s easy to determine ROI—driven by the state of technology and expense pressures in our business. We need to self-fund our big data projects in the near term. There is a constant drumbeat of ‘We are not doing “build it and they will come’—we are working with existing businesses, building models faster, and doing it less expensively. This approach is more sustainable for us in the long run. We expect we will generate value over time and will have more freedom to explore other uses of big data down the
Source: Big Data in Big Companies, Thomas H. Davenport and Jill Dyché, May 2013 (Go to Suggested Readings to view full article)
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