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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    Getting the Most from Big Data

    Data and analytics are powerful tools, but they are also complex, requiring technology, technical expertise, organizational and resourcing support, and, quite often, a test-and-learn approach to capitalize on their potential. It takes time to build, staff, test, adjust, and perfect big-data programs so that they function at full potential. For banks, as for other companies, big data is a journey. (See Exhibit 2.)

    exhibit

    Most banks have already run pilot or proof-of-concept projects, and rightly so. This is the best way to validate the potential, identify issues, and get the first quick wins from big data. Speed and agility are crucial in creating big-data applications. Short cycles, iterative development, and frequent pilots should be the rule. Risk taking should be encouraged and mistakes accepted. Big data is often uncharted ground, and even disappointment—or, at least, carefully analyzed disappointment—can be a good teacher. Since companies can evolve and mature, even after an imperfect start, most banks will be able put themselves on the road to high-impact big-data success. We have created a basic roadmap to follow.

    Assess your current situation. Most banks are already using big data, sometimes even without knowing it. Every retail bank has teams that use data and relatively advanced analytical techniques in everyday tasks, such as risk assessment and pricing and campaign management. And most banks have started experimenting with the new big-data technologies. More often than not, however, these efforts are carried out in a piecemeal and uncoordinated fashion. Even more often, data governance is administered on an ad hoc basis and based on purely technical, not business-related, considerations. In many cases, banks also fail to integrate new analytical opportunities and roles and responsibilities to create more data-driven, customer-centric organizations.

    It is paramount for a bank to run a thorough diagnostic of its current data and analytics situation to identify the areas and capabilities in which it is close to achieving its desired state (or to aligning with the current state of the market) and those to which it needs to devote attention.

    Develop a big-data vision. In our experience, the next step is the one that causes many banks to falter: moving beyond the diagnostic stage and building a vision of the role that data will play in the value chain, which includes identifying and prioritizing future applications and opportunities and evaluating the capabilities that the bank needs in order to successfully implement its plan. Too often, banks take a narrow view of the opportunities and capabilities necessary to succeed. The most innovative—and potentially most lucrative—opportunities usually are not readily apparent. That vision also shapes the role and place of big data in the organization and helps determine budgets, staffing, and organization structure. Strong sponsorship at senior levels sends a signal to the rest of the bank that top management attaches high importance to data and analytics.

    Banks need to create an environment in which novel applications—ideas that truly differentiate a company from its competitors—can be quickly identified and developed. The exploration of new data applications should be encouraged at all levels of the organization, with employees given time and resources to pursue their ideas.

    Bring the organization along. Ensuring widespread success means overcoming organizational inertia and skepticism. It’s hard to overstate the importance of this step. The wide range of expertise needed to identify and develop applications will require the skills of many individuals across the company. It’s vital, therefore, to create strong links among professionals who may well have very different backgrounds and very little experience in working with one another. Frequent dialogue and ongoing collaboration will help these interdisciplinary teams zero in on, and prioritize, the most relevant business problems and opportunities. Formal processes can spur this kind of collaboration, as can a more informal push from the top. Establishing a clear roadmap for success that focuses not only on building capabilities but also on continually demonstrating the value of big data is essential to achieving buy-in and building momentum.

    Cultivate the critical capabilities. Similarly, banks need to recognize that the requisite big-data capabilities are not limited to high-price, state-of-the-art hardware and software plus a team of data scientists. All too often, the inability to recognize the breadth of the capabilities required hinders the organization’s data enablement and restricts the impact of big data to a few very specific, and often limited-impact, areas. Banks end up building small pockets of excellence but fail to instill in their organizations an appreciation of the power that big data can bring.

    Big data capabilities fall into three domains:

    • Data Usage. How does the bank generate and manage new ideas? How does it secure data? Does it use customer trust as a key competitive differentiator?
    • Data Engine. What are the key combinations of technology and people necessary to build an efficient data engine? What is the best operating model for each particular bank?
    • Data Ecosystem. Who are the partners, and what are the relationships that a bank needs? Which roles are internal, and which are external? What is the optimum strategy for building the ecosystem? What is the bank’s own role in it?

    Banks need to address all three domains as they move from vision to execution. (See Exhibit 3. See also Enabling Big Data: Building the Capabilities That Really Matter, BCG Focus, May 2014.) These capabilities need to be built by completing specific, discrete projects with measurable business cases and clear milestones. Large foundational programs that take years to deliver business value—if they ever do—should be avoided.

    exhibit

    Working on data and analytics requires compiling the right mix of skills early on, with dedicated resources working in multidisciplinary teams that combine businesspeople, data scientists, and IT experts. The teams should be tightly linked units that are core to the business.

    Last but not least, banks need to understand that operating pace is key: it is not so much what you do, but how fast you do it. The focus of banks and their big-data teams needs to be on the speed to market from idea generation to final implementation. Building the ideal organization structure is less important than working cross-functionally and integrating data and analytics into day-to-day business processes, with the goal of rapidly generating tangible value.




    For retail banks, big data is already big business. But for many, it can be much bigger still, as the volume and depth of the available data grow, analytical models improve, and the sophistication of banking executives and data scientists increases with experience and success. There is no bigger playing field for big data than banking. Banks that raise their game first will not only reap immediate financial rewards but will also establish data and analytics capabilities that will be hard for competitors to overcome.