From Big Data to Big Gains – Take a Step Back
While there is no argument about the potential benefits of big data, the road ahead for organizations to realize those benefits is still being paved. “Overwhelming” is the buzz word and it is not just related to the volume of the data, but also to the implementation efforts required, and the risks associated with missing the big (data) bus.
Atul Singh, Capco
More questions than answers
For financial institutions (FIs), the post-financial crisis cost pressure adds another dimension. On the one hand is the significant benefits potential from big data (through better and faster insights, new products and services, improved customer experience, etc.). On the other hand are the additional investment and the challenges related to implementation and integration with the traditional analytics. On top of these is the still unclear return on investment (ROI), which is making it tougher for the leadership to justify the infusion of resources towards big data.
Big data is still emerging and evolving rapidly. While there is general awareness and agreement of big data and its benefits, there is not enough information or examples of successful implementation, especially in financial services. ROI estimations are unclear because it is based on assumptions that are too fluid at this stage. Equally important and uncertain are the questions related to the incremental ROI in the coming years. It is likely that in the next decade or so, when big data is established and stabilizes, its incremental ROI will begin to shrink when the adoption becomes widespread.
Moreover, in a competitive world, big business opportunities tend to gravitate towards economies of scale, with consolidation in the industry and niche players evolving as large-scale providers (we can already see some trends). Shrinking incremental ROI will only hasten this process. These issues beg the question for FIs whether to go ahead with big data implementation or to wait and watch while other industries test the waters and let the technology mature before full-scale implementation. There are more questions than answers at this stage but that is not necessarily a bad thing.
Cart before the horse?
With unprecedented increase in the rate of data generation in financial services, institutions might be tempted towards what big data has to offer without first understanding what their organizational needs are. In a rush to ride the big (data) wave, it is likely that FIs focus more on the benefits of big data rather than their own current challenges (internal and external) and how it can benefit from big data. This knee-jerk reaction however, will be counterproductive and lead to unnecessary roadblocks resulting in suboptimal results.
It is therefore imperative for FIs to first understand the current state of organizational challenges and its readiness for big data implementation. Some organizations may be better suited and more ready than others to integrate Big Data with traditional analytics. This could be because of several factors, including IT infrastructure, leadership support, and workforce composition, etc. A strategic step back to first assess the organizational priorities and how to align them with Big Data can go a long way.
The roadmap of successful implementation of Big Data should start with the FIs identifying the areas that are performing at sub-optimum levels and prioritizing top areas for improvements (in terms of cost-benefit analyses, profitability, mission-critical nature, and overall impact on the organization). From these areas, identifying the ones that will benefit from improved analytics and business intelligence will result in the best candidates for big data implementation.
[Big Data: No Replacement for the Financial Data Warehouse]
Having identified the right load for the cart, a well-planned implementation and deployment strategy is required to put the necessary horse power in place. This process should start with forming a core team for big data implementation with experts (in-house and external) and influential leaders from the senior management. This team should make an assessment of the required resources (human and technology) to implement big data in the identified areas of the organizational priorities. The core team must be governed by a well-defined but flexible agenda and should be responsible for gathering the appropriate resources and tool/technologies required for implementation. Budgetary allocations and targets must also be established by this team for a phased implementation of big data (including training needs). Because of the fast evolving nature of big data, the progress of the implementation efforts should be regularly and periodically assessed and a fast and lean decision-making process should be established for rapid adjustments to the implementation plan.
But the time is now
Increase in customer base for FIs has tapered off and the total customer universe has more or less stabilized in developed economies. New customers primarily come from acquisition (one bank’s gain is another’s loss). Customer expectations are at an all time high and customer loyalty is a fence sitter (switching between banks is increasingly commonplace). While big data is still evolving, the potential benefits are undeniable and can lead to significant improvements in overall customer experience. A wait-and-watch strategy for banks can be equally hurtful as a knee-jerk reaction.
Big data is changing the face of business intelligence and analytics. For FIs, there are only two options; be the change or chase the change. Some will take the lead in implementing big data analytics to provide new products and improved customer experience, consequently winning over the competition and cutting in on the customer base of the laggards. Early adopters can shape the changing landscape and remain ahead of the curve.
The ideal way forward for FIs to take advantage of big data is to take a step back, assess and prioritize their organizational needs, assess how big data can provide a meaningful solution, and then forge ahead with the implementation. This assessment itself is a significant effort and will require time.
Some FIs have already initiated steps in this direction and with timely thought leadership, sponsorship, and action, some FIs will emerge as leaders. Big data is no more a question of “whether-or-not” to implement. Nor it is a question of “when”. It is now a question of “how” to implement. Therefore, the time is now to pave the way, push the pedal and steer in the direction where Big Data will bring big gains. The biggest challenges that FIs must overcome for successful implementation of big data are self-assessment (to identify areas of priority), thought leadership (to identify better ways of implementation), and corporate sponsorship (to muster the resources required for implementation).
What has been your experience so far with big data implementation in financial services? Do you see any leading practices or institutions emerging as leaders? What do you think are the biggest challenges/concerns for FIs to successfully implement big data?
[Check out ways companies are Navigating the Big Spectrum of Big Data’s Solutions at Interop, which runs from September 30 through October 4 in NYC.]
Atul Singh is a senior consultant at Capco, where he serves clients in the areas of finance, risk, and compliance. He has over 12 years of diverse industry experience including financial services, IT, healthcare, and software development.
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