The banking industry is clear that there are big gains to be made from Big Data. For example, 90% of financial institutions in North America think that successful Big Data initiatives will define the winners in the future[i]. However, there’s a flipside to this bullish stance. Did you know that less than half of banks analyze customers’ external data, such as social media activities and online behavior[ii]? And that only 37% of banks have hands-on experience with live Big Data implementations? Given the banks’ pro Big Data stance, why are we not seeing greater progress?
The explanation could lie in the significant barriers that lie in the path of Big Data success: organizational silos; a dearth of analytics talent; a lack of strategic focus, with Big Data viewed as just another ‘IT project’; and the looming issue of privacy concerns.
Take the first point. Existing legacy systems are stopping banks from having a seamless, 360-degree view of the consumer. This is an issue that a major European bank, for example, has wrestled with. This bank has been working on a Big Data implementation since the beginning of 2012 in an attempt to analyze all of its unstructured data. However, problems have arisen while attempting to unravel the traditional systems – mainframes and databases – and trying to make Big Data tools work with these systems. While the bank is convinced that big, unstructured and raw data analysis will provide important insights, mainly unknown to the bank, getting those insights is an expensive proposition.
And while siloed structures are the major barrier to a successful Big Data implementation, the fact that three-quarters of banks do not have the right Big Data skill sets is of equal concern.
Seeing Big Data as just another IT project is another major pitfall. Big Data requires new technologies and processes to store, organize, and retrieve large volumes of structured and unstructured data. Traditional data management projects rely on relational data models, where historical data is analyzed within the system. Big Data, on the other hand, means dealing with large amounts of unstructured data, in real time.
Growing privacy concerns around Big Data also represent a significant issue. Research indicates that 62% of bankers are cautious in their use of Big Data because of privacy issues[iii]. This is because most Big Data projects tend to uncover hidden connections between seemingly unrelated pieces of data, which could reveal sensitive, personal information. Further, outsourcing data analysis activities, or distributing customer data across departments – in order to generate richer insights – can amplify security risks.
While these are significant issues, they cannot be allowed to derail Big Data efforts, and banks should focus hard on three areas: customer analytics; driving top-line growth; and limiting customer attrition. For example, in the field of customer analytics, Big Data analytics can help maximize lead generation potential. Research shows that banks that apply analytics to customer data have a four-percentage point lead in market share over banks that do not. Advanced analytics can also improve credit risk estimation by exploring diverse datasets.
This is certainly not an insignificant task. It will be a major effort, involving a fundamental transformation across culture, capabilities and technology. Banks will need to drive a shift in culture from “data as an IT asset” to “data as a key asset for decision-making”. They will need to develop analytics talent with a targeted recruitment process and continual training programs. Finally, banks will need to establish a strong data management framework for structured as well as unstructured data.
Big Data initiatives are typically time- and resource-intensive. To pave the way for a smooth implementation, we recommend a three-step approach that begins with an assessment of existing analytics capabilities and is followed by the launch of pilot projects, which are subsequently expanded into full-scale organization-wide programs. And underlying all of this is a critical and challenging need – to alter traditional mindsets. Big Data initiatives must be perceived differently from traditional IT programs. They must extend beyond the boundaries of the IT department and be embraced across functions as the core foundation for decision-making. Only then will banks be able to make the best use of their vast and growing repositories of customer data.
Curious to know more? For more information, please see our recent paper on “Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?”