James Blake, CEO of Hello Soda
Big data is an area that companies are increasingly keen to invest in, and for good reason – with more data being collected and analysed than ever before, it’s essential for companies to understand it and make use of it in the best way that they can.
Many of the decisions that are made within the financial services sector are driven by data, from trading algorithms to risk assessment. But just how far can big data take us? Has it reached its peak or is there more potential to explore? We’ve taken a look at what the future may hold for big data in the finance industry.
Consumers have come to expect an increasingly personal service from every business that they interact with, including their banks. In order to compete with fintech start-ups and consumer lending apps, personalisation isn’t just desirable – it’s essential.
Big data can play an important role in analysing customer data and predicting behaviour, and it can come from unexpected places – geo-spatial data and information from social media can help organisations to offer targeted marketing, personalised products and offers.
One significant benefit of big data is that it encompasses a large number of data points from a range of sources, meaning that harnessing the digital footprint of consumers can provide a much more accurate picture of an individual customer at one point in time than organisations could have dreamed of ten years ago.
The use of personalisation allows banks to differentiate themselves in an increasingly crowded market, and target new customers by offering them relevant packages and services, such as offering them mortgage packages if they are talking about buying a house, or student accounts if they are about to start university.
2. Artificial intelligence
Algorithms are crucial in organising and analysing large amounts of data, particularly when so much of that information is unstructured. By 2020, the amount of data that could provide business insight is predicted to double, meaning that machine learning algorithms are likely to not only increase in use, but develop in sophistication.
The use of machine learning algorithms has massive potential for organisations within the financial services industry. It allows companies to carry out real-time analysis and make effective decisions based on the data available about their customers, which is particularly useful when it comes to making decisions about risk in the insurance sector.
3. More demand for analytical skills
It goes without saying that companies can’t rely entirely on algorithms – the supervision and development of machine learning applications require a lot of new skills. A survey last year by NewVantage Partners found that 54% of firms surveyed now report having appointed a chief data officer, up from just 12% in 2012.
The increased demand for big data will undoubtedly lead to businesses needing to recruit staff with the relevant skill sets, or at least working with an external specialist to keep up with industry competition and customer expectations. It is also likely that we will see an increase in people with job titles including data scientists, data modellers, data analysts, developers and more, due to the increase in the amount of data and the need to analyse it.
Ultimately, it doesn’t look like big data is going anywhere – in fact, it is the future for many sectors. A report by IDC claims that worldwide revenues for big data and business analytics will grow from $130.1 billion last year to $203 billion in 2020.
It’s certain that, when used effectively, big data has the potential to transform the financial services industry, from acquiring and retaining customers to making reactive decisions to benefit businesses.