While banks have historically been good at running analytics at a product level, such as credit cards, or mortgages, very few have done so holistically, looking across inter-connected customer relationships that could offer a business opportunity - say when an individual customer works for, supplies or purchases from a company that is also a client of the bank. The evolving field of data science facilitates this seamless view.
Blockchain as the new database
Much more is yet to come. Blockchain, the underlying disruptive technology behind cryptocurrency Bitcoin, could spell huge changes for financial services in the future. Saving information as 'hash', rather than in its original format, the blockchain ensures each data element is unique, time-stamped and tamper-resistant.
The semi-public nature of some types of blockchain paves the way for an enhanced level of security and privacy for sensitive data - a new kind of database where the information 'header' is public but the data inside is 'private'.
As such, the blockchain has several potential applications in financial markets - think of trade finance, stock exchanges, central securities depositories, trade repositories or settlements systems.
Data analytics using blockchain, distributed ledger transactions and smart contracts will become critical in future, creating new challenges and opportunities in the world of data science.
Getting ready for the Big Data revolution
While the potential of Big Data is beyond dispute, the problem for banks is that the data very often sits in large, disparate legacy systems. Making data science tools work with legacy platforms and databases sitting in silos is a huge challenge.
As organisations embrace Big Data, the other key challenges are mindset and finding skilled people to solve problems using the right techniques, and, ultimately, to wring out insights that can be acted upon. This requires a collaborative - almost philosophical - ongoing dialogue between the business owners and the data scientists.
Data science helps in finding correlations without going into causality but the data doesn't just hop out and explain itself. Smart people are still required to interpret the results meaningfully.
Coping with the sheer volume of insights produced by Big Data presents its own set of challenges. A virtual tsunami of data points is being thrown at today's managers.
There is simply too much information out there for knowledge workers to visualise effectively using traditional methods. Here, however, help is at hand: 'Advanced data visualization', an offshoot of the Big Data revolution, is the newest approach to business analytics and intelligence. Its ability to present huge, complex data sets in ways that can be read by non-experts promises to transform the way businesses - including banks - make use of number-driven insights. Artificial intelligence too is helping to pave the way.
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