Big Data Analytics in the Banking and Finance Sector – Challenges and Emerging Best Practices
In the words of Irvin Berlin – A Russian Composer “The toughest thing about being a success is that you’ve got to keep on being a success”.
Banks and other financial institutions are under similar pressure to sustain their competitiveness. The aftermath of 2008 financial crisis has unleashed a new wave of stricter and newer regulations, intense competition, and lower customer trust for the banking and financial industry. With such myriad of challenges, banks are under great pressure to adapt and evolve.
Considering immense and growing ocean of data from multiple sources including web and social media, big data analytics has evolved as one of the key technology trends to look out for. Banks and other tech savvy financial businesses have begun to take a pragmatic approach to adopting big data. In fact, they have started realizing that there is tremendous untapped value locked away in these internal systems.
Considering several barriers to effective implementation of big data, most financial services companies are still focusing on either developing big data strategies or pilots, but only few have strived to embed analytics into operational processes.
A quick look at some of the biggest impediments to big data success can help drive strategic decision making, while providing a roadmap for banks and other financial institutions to derive valuable insights.
- Organizational Silos: Usually, data resides in silos or is distributed across different systems making it hard to collect. With too many data silos, data is not pooled for the benefit of the organization.
- Dearth of Skilled Resources: Banks require analytics professionals with solid understanding of data management, technology, and emerging and existing regulatory requirements. Such talent is usually scarce and expensive.
- High Cost of Data Management: Cost of storing and analyzing large data sets is usually prohibitive.
- Lack of Strategic Focus: The vision, strategy and requirements for big data within an organization should be defined. Lack of strategic vision by senior management has proved to be a major stumbling block.
- Inadequate data and Systems Infrastructure: Traditional and inflexible legacy systems that impede data integration are inadequate to process large volume and variety of unstructured data. A state-of-the art IT infrastructure to manage this growing variety and volume of data is the key solution.
Best practices to mitigate the aforementioned impediments include:
- Leveraging offshore resources for data integration and data cleansing. Beyond cost arbitrage, offshoring offers access to skilled resources, ability to work in different time zones, while managing volume fluctuations for data management.
- Creating an agile organizational structure that can adapt and change quickly. This includes leveraging server virtualization and other cloud solutions.
Additionally, effective big data strategies that besides identifying the business requirements could leverage the existing infrastructure, data sources and analytics to support the business opportunity, need to be developed.
To compete in a customer-centric economy, it has clearly become obvious that banks and financial market firms must leverage their information assets. However, given the ground realities of functional silos, talent crunch, outdated data/ system infrastructure, institutionalizing and operationalizing analytics to take smart business decisions will always be a challenge.