Given all the hype about getting closer to customers, you’d think that banks would be first in the queue to invest in technology that enables them to follow individual lifestyles based on unstructured data such as social media, photo uploads and online shopping.
But while no one disagrees that better understanding of customers is a good idea, few banks are currently undertaking big data initiatives. And when you think about it, there are plenty of good reasons why.
Firstly, that word ‘unstructured’. With systems and process investments under greater scrutiny than ever before, is now really the right time to invest in a new system capability development where the core ingredient - unstructured data - is so nebulous
and hard to quantify?
There are equally difficult questions surrounding privacy. Even social media leaders such as Facebook and Google struggle at times to convince end-users that their data isn’t being misused or shared unfairly with third parties. Would banks fare any better,
especially given the struggle to rebuild trust with customers in the wake of the financial crisis?
It’s also painfully obvious that banks’ appetite for such initiatives has been supressed because many are still occupied with historical business intelligence investments that attempt to mine legacy data, improve analytics and support the immediate needs
of analysts, marketing and sales.
Making the case
Even so, there’s little doubt that some banks are at least trying to build a case for investment in big data strategies. In most cases, these projects are driven by the opportunity to better understand the customer and increase services
and revenues, while reducing exposure and regulatory compliance risks. But many practical constraints remain:
- Increasing customer wallet share is harder than ever given current economic and employment conditions. As a result, big data related business cases, especially ROI are under increased scrutiny.
- Banks are yet to leverage and achieve the full benefits of their current data and channel capability initiatives.
- The received wisdom in many institutions is that their current efforts will satisfy most information and business intelligence needs.
Ask the right questions
How then do you develop a big data strategy that includes customer needs and clear business goals supported by a roadmap that describes tangible business benefits? In addition, how do you undertake necessary due diligence in order to understand the real
cost and value benefits that support big data related investment decisions?
The trick is to ask the difficult questions up front. Start with the following:
- How will my customers benefit from an investment in big data capabilities?
- How can the investment improve customer experience across different channels?
- What are the specific capability needs, and do I really need big data capabilities now or any time soon?
- How much business revenue will be generated thanks to the new data insights and capabilities? How will this be achieved and when?
- How do the required capabilities support the recruitment and retention of customers and existing risk strategies?
- Is it possible to align big data investment with business and IT strategies and roadmaps in the near to medium term?
- Can these benefits be obtained using existing analytical capabilities at lower costs?
- What investment is required to develop and sustain a credible big data capability that supports strategic business needs?
- How do you recruit the talent that helps you squeeze the most out of your investment? And in the coming months and years, what technology advances could impact the benefits you hope to achieve?
This isn’t an exhaustive list by any means. But it does give an idea of the depth of thinking that must precede any investment of time, effort and money in a big data strategy.
Does this ring true? At what stage are you with your big data investment strategy? What constraints, if any, are preventing you from getting closer to customers and gaining a real business advantage from big data? Share your experience in the comments below.