Cultivating Big Data Adoption in Banking: Part 2
This is our seventh and final post in a series presenting the findings from the IBM Institute for Business Value and University of Oxford’s Big Data study and excerpts from the report, “Analytics: the real world use of big data in financial services.”
Analysis of the findings by my IBM colleagues David Turner, Michael Schroeck and Rebecca Shockley provide new insights into how banking and financial markets companies at each stage are advancing their big data efforts.
Driven by the need to solve business challenges, in light of both advancing technologies and the changing nature of data, banking and financial markets companies are starting to look closer at big data’s potential benefits. To extract more value from big data, the report offers a broad set of recommendations tailored to banks and financial markets firms.
Start with existing data to achieve near-term results
To achieve near-term results while building the momentum and expertise to sustain a big data program, it is critical that banking and financial markets companies take a pragmatic approach. As our respondents confirmed, the most logical and cost-effective place to start looking for new insights is within the organization’s existing data store, leveraging the skills and tools most often already available.
Looking internally first allows organizations to leverage their existing data, infrastructure and skills, and to deliver near-term business value while gaining important experience as they then consider extending existing capabilities to address more complex sources and types of data. While most organizations will need to make investments that allow them to handle either larger volumes of data or a greater variety of sources, this approach can reduce investments and shorten the timeframes needed to extract the value trapped inside the untapped sources. It can accelerate the speed to value and enable organizations to take advantage of the information stored in existing repositories while infrastructure implementations are underway. Then, as new technologies become available, big data initiatives can be expanded to include greater volumes and variety of data.
Build analytics capabilities based on business priorities
The unique priorities of each financial institution should drive the organization’s development of big data capabilities, especially given the tight margins and regulatory compliance requirements that most banks and financial markets firms face today. The upside is that many big data efforts can concurrently reduce costs and increase revenues, a duality that can bolster the business case and offset necessary investments.
For example, several financial institutions leverage customer insights gleaned from big data to design marketing activities, execute campaigns and capture sales leads across all channels, product lines and customer segments. This can improve relationships and lower the cost of operations while increasing revenues. Others are using big data technologies to enable data integration across channels. This positions them to provide superior and consistent channel user experience, improve customer satisfaction and reduce costs.
Banking and financial markets companies should focus on acquiring the specific skills needed within their own organizations, especially those that will increase their ability to analyze unstructured data and visually represent it to be more consumable to business executives.
Create a business case based on measurable outcomes
To develop a comprehensive and viable big data strategy and the subsequent roadmap requires a solid, quantifiable business case. Therefore, it is important to have the active involvement and sponsorship from one or more business executives throughout this process. Equally important to achieving long-term success is strong, ongoing business and IT collaboration.
Getting on track with the big data evolution
An important principle underlies each of these recommendations: business and IT professionals must work together throughout the big data journey. The most effective big data solutions identify the business requirements first, and then tailor the infrastructure, data sources, processes and skills to support that business opportunity.
To compete in a consumer-empowered economy, it is increasingly clear that banks and financial markets firms must leverage their information assets to gain a comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, employees and more. Financial institutions will realize value by effectively managing and analyzing the rapidly increasing volume, velocity and variety of new and existing data, and putting the right skills and tools in place to better understand their operations, customers and the marketplace as a whole.
To learn more
- Read the research report Analytics: the real world use of big data in financial services
- Part 1 of Bob's series: Looking at New Research on Big Data in Financial Services
- Part 2: Customer Analytics Drive Initiatives in Financial Services
- Part 3: Big Data for Banking: Depends on a Scalable, Extensible Information Foundation
- Part 4: Financial Services Focused on Gaining Insights from Internal Data
- Part 5: Big Data Requires Strong Analytics Capabilities
- Part 6: Cultivating Big Data Adoption in Banking: Part 1
- See more blog posts, videos, podcasts and reports on banking
- Watch this short animated demonstration of big data and analytics at work in banking