FT Live: The Data and Disruptive Technology Forum

Last week we attended the Financial Times' Data and Disruptive Technology Forum, held at The Berkeley Hotel in Knightsbridge, London. The event brought together experts in analytics, banking, compliance, and data to discuss how technology is transforming the industry, and how businesses should approach the wave of transformation.

Advancements in technology have opened the floodgates to innovation - whether every business sees this an opportunity remains to be answered. There are the pros and cons: adopting new technologies create a more nimble business model which is data-driven, yet on the other hand, some businesses face severe obstacles if they rely on outdated back-end systems, and upgrading these decade-old infrastructures is expensive and risky. Not doing so puts them at risk of losing out. Doing so may create unnecessary problems for business.

The opinion from this conference, as you may have guessed, favoured towards using technology to innovate and revolutionise the way businesses in the industry operate. Here’s our highlights from the event.

Mathematics, not magic

Our very own head of machine learning, Eddie Bell, was a speaker on the panel “Chief Data Scientists: The CEOs of tomorrow?” Bell spoke alongside Kate Land from Havelock London, Yaacov Mutnikas from IHS Markit, Stephen Roberts from Oxford University and Javier Rodriguez-Alarcon from Goldman Sachs Asset Management. The talk was moderated by Joy Macknight from The Banker (Joy is also a recent guest on our Fraud Academy podcast, which you can listen to here).

Joy opened up the discussion by speaking about the importance - and lack of - data scientists in the industry. She asked panelists whether the role of the data scientist was evolving. “Originally a data scientist may have been a glorified data analyst, but now the role is expanding and specialising,” said Bell.

“It’s evolved to those that do engineering and build products, those that do complex analysis and generate insights and visualisations from data; and those that research and publish academic papers.”

The panel debated whether rigor and mathematics was still at the core of the data scientists role, or whether a more pragmatic experimental methodology has taken the reigns. Panelists agreed that science remains at the core but, especially in the tech industry, there is sometime the need to side-step rigor and embrace pragmatism. Just as every role is being disrupted by some form of digital transformation - the same applies for the data scientist.

“Machine learning is half magic, half science”, added Bell.

And what about the skills that are needed in a good data scientist? “To be inquisitive”, said Kate Land, as well as knowing how to analyse and understand the relevant data that may not be always easy to spot

Great insights from today's #FTDisruptiveTechForum , talking all things #AI #blockchaintechnology #BigData in financial services. Lots of thought-provoking questions, e.g. "If data is the new oil, are data scientists the CEOs of tomorrow"? Fascinating! pic.twitter.com/9TwNdpoNKL

— Joanna Turner (@JoTurnerprop1) June 12, 2018

Contrasting cultures

The topic of creating a data-led culture was discussed by Dan Cobley, MD of Blenheim Chalcot. Cobley discussed how life is much more digital, how the availability of tools and platforms have streamlined business processes, and as an industry, how we have become a lot more tech-focused. And technology moves quickly. So how can financial services follow suit?

For businesses, there are contrasting cultures - some can leverage using machine learning to operationalise the data management system and decrease the amount of mundane work that once had to be done. In some businesses, however, this isn’t always the option. To create a data-led environment means shifting the focus to the customer. As long as you make the customer journey more streamlined, the rest doesn’t matter, according to Cobley.

He concluded his keynote speech by comparing the incumbent vs fintech approach to highlight the power of data in financial services. “Incumbents think that the business is the people and they are supported by the data. Challengers know that the business is the data, and it is supported by the people.”

To learn more about machine learning, visit our insights page.