This is our sixth post in a series of seven presenting the findings from the IBM Institute for Business Value and University of Oxford’s Big Data study, “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.
Commit initial efforts to customer-centric outcomes
It is imperative that organizations focus big data initiatives on areas that can provide the most value to the business. For most banking and financial markets companies, this will mean beginning with customer analytics that enable better service to customers as a result of being able to truly understand customer needs and anticipate future behaviors. Financial institutions use these insights to generate sales leads, enhance products, take advantage of new channels and technologies (for example, mobile), adjust pricing and improve customer satisfaction.
To effectively cultivate meaningful relationships with their customers, banking and financial markets companies must connect with them in ways their customers perceive as valuable. The value may come through more timely, informed or relevant interactions; it may also come as organizations improve the underlying operations in ways that enhance the overall experience of those interactions.
Banking and financial markets companies should identify the processes that most directly interact with customers, then pick one and start. Even small improvements matter as they often provide the proof points that demonstrate the value of big data, and the incentive to do more. Analytics fuels the insights from big data that are increasingly becoming essential to creating the level of depth in relationships that customers expect.
Define big data strategy with a business-centric blueprint
A blueprint encompasses the vision, strategy and requirements for big data within an organization. It is critical to aligning the needs of business users with the implementation roadmap of IT. A blueprint defines what organizations want to achieve with big data to help ensure pragmatic acquisition and use of resources.
An effective blueprint defines the scope of big data within the organization by identifying the key business challenges involved, the sequence in which those challenges will be addressed, and the business process requirements that define how big data will be used. It is not meant to “boil the ocean,” but rather to serve as the basis for understanding the needed data, tools and hardware, as well as relevant dependencies. The blueprint will guide the organization to develop and implement its big data solutions in pragmatic ways that create sustainable business value.
For banking and financial markets organizations, one key step in the development of the blueprint is to engage business executives early in the development process, ideally while the company is still in the Explore stage. For many banking and financial markets organizations, engagement by a single C-suite executive is sufficient. But more diversified companies may want to tap a small group of executives to cross organizational silos and develop a blueprint that reflects a holistic view of the company’s challenges and synergies.
In our final blog in this series, we will complete our review of the recommendations for companies that want to extract more value from big data.
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
- See more blog posts, videos, podcasts and reports on banking
- Watch this short animated demonstration of big data and analytics at work in banking