Marieke Saeij, CTO, Onguard
The volume of data we are dealing with is growing exponentially with 90% of total available data produced in the past two years alone. It is estimated that one trillion data units are produced worldwide every day. Not only is big data growing in volume, but it is also increasing in value – you just need to know how to extract it. According to research from EY, 35% cite the key drivers of implementing big data is “to monetize existing data”, while 41% cite “to create new revenue streams”, showing organisations are beginning to recognise the financial value of big data. This potential is driving more and more organisations to use big data with adoption reaching 53% in 2017, up from 17% in 2015.
The financial sector was an early adopter of big data and in recent years it has become incredibly valuable to financial organisations. Through its FinTech Barometer of 1,004 finance professionals, Onguard found that data plays a role in 80% of their business. Big data offers a great deal of potential to organisations in a number of areas, including providing insights into processes and information flows, aiding the assessment of financial risk, increasing returns and scoping out new potential. Ultimately, big data has the power to cut costs and increase revenue for financial institutions – but, how?
Big data can be used to gain valuable insights which can then form the foundation from which businesses can substantially reduce overheads, as demonstrated by shipping and logistics firm UPS in recent years. Through the analysis of data, UPS found that it could significantly reduce costs if drivers made fewer left turns (on right-hand driving roads). This finding, which at times means driving in the ‘wrong’ direction, reduces the risk of accidents, due to left turns meaning turning against the flow of oncoming traffic, and saves on fuel. By implementing this finding into route planning, UPS saved 38 million litres of fuel in 2011 and 350,000 more parcels were delivered.
Big data can also be used by businesses to earn money. For instance, when a customer visits an online shop, every click is captured, so by analysing this data, it becomes possible to understand how customers reach buying decisions. This information can then be used to optimise online stores, ultimately resulting in more purchases.
The financial sector regularly uses past figures to generate management information. However, inclusion of big data encompasses data from customers and the market alongside past data from within the organisation, enabling finance professionals to be able to gain a better view of the future. (The predictive value that this provides means financial departments can be proactive and plan for the future, rather than be reactive).
For example, within an insurance company setting, if a controller sees that the number of claims over the previous winter increased enormously, they will be interested to understand why this might have been and if it was just a one-off occurrence. However, from existing data, they will only be able to see the number of claims, rather than the reasons for the claims. By adding big data to their existing data set, they will be able to make a correlation between the season and the level of claims. Any findings that aren’t tied to bad weather, for example, can then be used to provide predictions for the future.
Big data also has the potential to provide insights across a number of processes and information flows within the financial sector. Through the use of big data, finance departments are better able to assess risks to the organisation. For example, it is possible to gain a better picture of how the organisation’s finances stand at an earlier stage. Big data can also help assess the creditworthiness of customers, as unlike the current system, which is largely based on postcodes, big data provides a full picture of the person applying for credit. This reduces the risk of businesses taking on customers who will be unable to settle invoices on time, therefore improving cash flow.
Big data is of inestimable value to financial organisations, however, only if organisations stop dumping it in a data lake, instead, begin to introduce it to their existing processes and information flows. When used correctly, big data has the potential to provide full visibility into current processes and enable analyses to be run with predictive values on the basis of which higher management can make informed decisions and help the business further. The insights and analyses gained from using big data can also help organisations to streamline processes and implement direct changes, which can lead to significant cost reductions and potentially increase revenue.
Big data will also play a major role in the wider digital transformation which we are seeing within the financial services industry, and those organisations that introduce big data sooner, rather than later, will see the most benefit.