Join the data revolution
Michael Gorriz, group chief information officer of Standard Chartered, discusses the implication of data revolution for banks and how big data can become a very powerful tool in this changing times.
- Gorriz believes banks need to re-invent how they work, given the exponential speed at which technology is evolving
- Banks and other service providers have to tread a fine line between being helpful and being intrusive
- Data quality is one of the biggest problems in the big data space, exacerbated by the diverse nature of data coming from both internal and external data sources
The explosion in data sources – mobile data, real-time social data, and the Internet of Things – combined with the coming of age of data science and open-source data technologies, has created a clear divide between the banks that are ready to embrace the data revolution, and those that are not.
Banks need to re-invent how they work, given the exponential speed at which technology is evolving. At Standard Chartered, we’ve made harnessing our data assets a key priority.
Data ownership and privacy
Our data-driven world raises questions about privacy and who owns the data when someone starts to share their personal information. This debate has existed since the advent of the internet.
Organisations that collect big data want to run analytics to understand their customers and improve the quality of their services, while others are advocating for users to regain data sovereignty.
Collecting and storing data, in addition to abiding by ever-increasing levels of privacy and regulatory compliance, make for a deeply complex operating environment for banks.
Some have suggested that privacy will become mathematically impossible in a matter of years when artificial intelligence (AI), combined with data analytics, can start to plug knowledge gaps by inferring from known data.
What is important is making sure people have more direct control over their data and can choose what they make available. Generally, people don't mind giving out data if they get something in return. As long as customers are given a choice, see the benefits and are asked for their agreement, they are more likely to share their data. Banks and other service providers have to tread a fine line between being helpful and being intrusive.
When used correctly, big data is very powerful. Our team in India has worked out how data analytics could be used to identify potential instances of money laundering, and address financial crime risk. With the rise in regulation since the 2008 financial crisis, we are also exploring solutions to improve reporting that meets the requirements of central banks.
We have invested to build our own ‘data lake’ – a state-of-the-art platform that allows us to embrace the data revolution and depart from the traditional data warehouses that were functionally limited, expensive and slow to use.
The success of any venture into big data depends on data you can trust. Indeed, data quality is one of the biggest problems in the big data space, exacerbated by the diverse nature of data coming from both internal and external data sources.
Making sense of data in a unified model is crucial. Without that, we end up with data but not information. As a bank, we are focusing on the root of this problem. We are looking at open standards like Financial Industry Business Ontology (FIBO) to help us achieve this. There are also novel techniques in the areas of machine learning and AI that are accelerating the convergence of data models across disparate sources.
Despite the prevalence of smart algorithms capable of using data to derive intelligent conclusions, I’m of the view that we remain years away from being able to be rely on machines to run our lives.
A colleague described a situation in which he received a threatening call from a debt collection agency, only to find out later that the machine had matched him with the data of someone else with the same name. Clearly, banks and many institutions still require experts in data quality governance.
While it is important for Standard Chartered to strive to become truly data driven, our business isn’t a technical machine with input and output factors. Big data is a means to an end and not an end in itself.
We don’t measure success by the amount of data that we are able to harness or the number of apps we’re able to invent, but by the extent to which big data helps us gain more insights into the real, human needs and desires of our clients.
I’m a firm believer that with the advancements in machine learning, humanity will still be the architect of the world that we live in.
Michael Gorriz is the group chief information officer of Standard Chartered. The views expressed herein are strictly of the author.
Keywords: Standard Chartered, FIBO, Technology, IoT, AI