Making Big Data Work in Retail Banking

Making Big Data Work in Retail Banking

          
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Making Big Data Work in Retail Banking

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    Retail banks are data businesses. Their value chains have always been supported by data, and a large part of their competitive advantage is based on better use of the information that data provides and the insights it originates. Banks, along with retailers and telecommunications companies, have long had more consumer data available to them than other businesses.

    Consumers embraced digital channels for all manner of commerce well before many businesses, and banks were among the first companies to take advantage of new streams of data. A few were early movers, employing advanced data analytics, establishing dedicated teams, appointing chief data officers, and investing substantial time, effort, and resources in building out infrastructure and enabling data analysis.

    All that said, The Boston Consulting Group’s work with leading retail banks around the world shows that despite the early start and formidable resources, most banks are far from realizing big data’s full potential.

    Data and analytics today bring the ability to combine three elements:

    • Vastly bigger volumes of data, including highly detailed data combined from different systems
    • Much more insightful models, powered by so-called machine-learning software, which can make data-driven predictions and decisions
    • More efficient technology, such as Hadoop software-hardware clusters, which are among the most cost-effective ways to handle massive amounts of both structured and far more complex unstructured data

    Beyond the basic roadblocks that hold up companies in every industry, such as resistance to change and lack of qualified resources, banks have their own reasons for not having made more progress with big data. These include competing priorities, such as addressing regulatory changes in the wake of the financial crisis; IT complexity (because of multilayered systems and siloed data, banks rarely use the full breadth and depth of data at their disposal); and a combination of lack of overall vision and widely dispersed and loosely coordinated efforts, which result in suboptimal allocation of human and technical resources and limited interaction and exchange of ideas. In addition, because banks often work with aggregated data served up by their systems, they do not always appreciate the potential that is embedded in the rich precision and detail of the data they possess.

    There are at least four areas in which focused and coordinated big-data programs can lead to substantial value for banks in the form of increased revenues and bigger profits. (See Exhibit 1.)

    exhibit
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