There is no unassailable definition of big data but it’s useful to regard it as data that, due to several varying characteristics, is difficult to analyse using traditional data analysis methods. Thus, it requires new forms of processing that are better suited to the specific characteristics of that data; namely, volume, velocity, variety, and veracity.

Listed below are the top big data predictions, as identified by GlobalData.

Many leading incumbent banks, such as BBVA, DBS, and La Caixa, have publically equated digital transformation with data transformation – transforming the bank into a data-driven organisation.

In 2020, big data will remain the critical enabler for a variety of new business models. In particular, virtually all of the largest and/or fastest-growing new digital banks rely on big data to make high-risk segments, such as small and/or rural SMEs, commercially viable. Incumbent banks will increasingly explore non-traditional metrics to tap into these new segments and drive new revenue.

Big data predictions suggest that most incumbent IT systems simply cannot collect, store, and analyse the requisite data efficiently, which could threaten the stability of the bank’s entire IT system. As such, banks will continue having to boost their storage and processing capacities or,  completely overhaul existing systems, with successful big data, cloud, and core systems.

Just the data piece will present formidable obstacles. Banks will have to centralise many different types of datasets in one place including structured and unstructured data from proliferating channels. Yet centralising that data into fewer locations will increase cybersecurity risks.

Banks will mitigate their tech disadvantage, certainly compared to Big Tech, and focus on their trust advantage, seeking to carve out a new role as data custodians, as they once did around money custodians.

Willingness to give personal data, and let it be used for certain purposes, is dependent on variables such as how much the user trusts that organisation and what information is being asked for. And it’s dynamic too, which presents problems with a binary, yes-or-no consent approach. But even graduated consent, such as “are you willing to share your mobile location?” or “can this app access your camera?” can’t get at the intelligent nature of AI-enabled big data with algorithms that learn.

Leveraging branch networks, leading banks will formalise an approach here, training staff to handle objections and clearly explain privacy laws and the benefits of sharing data with the bank. Data stores, giving consumers transparency around what data is shared when and with whom, will build trust. Internal training around data democratisation will help make data and insight part of everyone’s jobs. But the imperative of broadening access data will create tensions with risk and compliance.

This is an edited extract from the Banking & Payments Predictions 2020 – Thematic Research report produced by GlobalData Thematic Research.

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