Trends: Rich Data or only Data Rich? Have banks figured out Big Data? | Fintech Recap 2017

Over the last few decades, big banks have treated transactional data as any old tit and tat to be crammed and forgotten into the landing cupboard; they just didn’t know what to do with it. Now the fintechs are demanding access, not only to stack it neatly, but to find value there. Indeed, for years …

by | December 15, 2017 | bobsguide

Over the last few decades, big banks have treated transactional data as any old tit and tat to be crammed and forgotten into the landing cupboard; they just didn’t know what to do with it.


Now the fintechs are demanding access, not only to stack it neatly, but to find value there.

Indeed, for years the big banks have been keeping the great mass of customer data jealously guarded in siloes, with no access or use.

And when the fintechs come to the bank boards with proposals, they’re bringing the latest buzzwords in tow – AI, Machine Learning and Advanced Analytics; it’s lost on them that all those words come under the same banner. Anyone can compile a few transactions in a glorified Excel sheet, apply a few analytics and sell it off as an ‘AI enabled recommendation’ product.

The term Big Data is troublesome in itself as a misnomer. Computing vast oceans of data which, in itself can be complex, does not do justice to the actual complexity of the task; that of computing the vast variables that sit within the data. Figuring out the combination of variables to fit the operating model is the crux of the problem.

The true value of Big Data is three fold. The first looks at cleaning the Big Data – sorting out a suitable distribution, segmenting into the relevant variables as mentioned above. The second has only really been possible in the last half decade with the rise of Fast Data – where improved infrastructure has opened the door to ‘Real time’ capabilities. Telematics, Smart devices and even your trusted house robot all relay data in ‘real time’ to build the current schematic that they then look to improve.

The combination of Clean Data and Fast Data offer up Rich Data. Having sieved out the grit, constructed responsive models that can compute constructive variables at whim, this is when the third parties strut their stuff to enrich the customer experience, post PSD2.

Crucially, legacy systems are notoriously inept at cleaning, quickening and enriching that data.

Does PSD2 loosen the big banks’ monopoly on data and market share of consumers?

Those innovators out there lick their lips at the thought of disintermediated banks performing the same function as utility providers. Whilst I do not believe this will happen overnight or in five years for that matter, it is an inevitability. Therefore, the banks that will make a success of PSD2, in the short term and the long term, will be those with the most accessible and ultimately, successful APIs; access to the Big Data.

In this way, will banks figure out the great behemoth they’ve been hoarding for decades.

For more reading, here are our top performing articles on Big Data:

"It's Moore's law on steroids" – Fast data meets Big Data and a whole new technological generation is born

Influencer Interview: Chris Gledhill cuts through the emerging tech hype Pt.1 & Pt.2

Interview with CEO of DataStax: “People say data infrastructure might not be sexy, but get it right, and you’ll go far

Getting to grips with data security: Top 10 tips for success

What does the future hold for Big Data?

 

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