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The Next Buzzword in Fixed Income: DATALAKE

Traders Magazine Online News, October 24, 2018

Paul Reynolds

Some people would say that we are in an era of life-changing, technology driven events that may perversely render human work output obsolete. The tendency these days to drop buzzwords like artificial intelligence, machine learning, and blockchain, as game-changing initiatives, is utterly irresistible.

Nowadays when I go to a conference, my “jargon bingo card” is often complete before networking coffee on day one. What troubles me though is, that I have yet to see a real world application of these exciting concepts that brings the hyperbolic material gains to our industry.

Like any fintech trading infrastructure firm, we are all highly motivated to discover and implement cost-saving efficiency and productivity tools, but until there is a clear business case and proven application, the quest remains unfulfilled.

Perhaps our objectives are too ambitious. Perhaps the ease with which a smart idea can be propagated into practicality is a result of our tendency to pursue the next big thing at all costs rather than take it step-by-step

As a technology firm, we have to be extremely thorough in our research and decisions about functionality upgrades. Luckily, we have an amazingly enthusiastic customer base that constantly encourages us to consider and evaluate their ideas that emerge from their daily workflows. From there, if we believe it benefits our broader community we start building (and naturally, justify a suitably punchy name for this new product).

We strongly encourage our clients to behave in this way. They can then benefit from sandboxing each other’s suggestions and we can evaluate the benefits to our community.

With the advent of MiFID II the abundance of data has created its own world of dilemma,. described by one of our customers as a “datalake.” As a TradeSmart EMS user, he creates a daily stream of order, RFQ, and execution data. Add in the incoming price, axe, and run data he receives from dealers, venues, and vendors and you begin to understand the “datalake” description. He mentioned a few points below to describe his particular issues with his lake of data:

  • How do you filter out the abundance and focus on the relevant?
  • How do you maximise the integrity and minimise the duplicates?
  • How do you contextualise the output to match your order configuration?
  • Do you understand and attribute the source of the data?
  • Is the data worth what you are paying for it?
  • Are you being adequately compensated for your data contribution?
    How can you improve your productivity and efficiency by better using the data?

This isn’t entirely unique and in a similar vein, it’s no surprise that most of our initial fixed income customer contact these days starts with a question about how to manage the tidal wave of data created by MiFID II.  

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