TickSmith Turns Five Today: Milestones
We are celebrating our five year anniversary today and are taking some time to look back at all that we have achieved as well as all the exciting opportunities that lie ahead of us. We incorporated on this day in 2012 and started out in Montreal with the four founders. Since then we have successfully built a business that solves big data problems in financial institutions around the globe with staff now in Montreal, Toronto, New York, London, and a mature flagship product, TickVault. We describe, TickVault, as a Data Lake Platform for Capital Markets as it provides the foundation for a highly scalable centralized data repository to service a variety of data-intensive applications. Our use cases have expanded from data centralization and distribution to also encompass compliance, backtesting, market surveillance, risk management, strategy discovery analytics, “self-serve” machine learning, and AI.
THANK YOU ALL!
We are so grateful for all the support we have received over the years from our friends, families, staff, investors, clients and partners. None of the success we have would have been possible without the hard work and determination that is constantly surrounding us.
HOW DID IT ALL HAPPEN?
Well, it started back in 2011 when our founders (Francis, Marc-Andre, David, and Tony) saw an opportunity to work with a set of historical data at a scale, which was not being actively explored at the time. But how do you take 370 Terabytes of market data in various formats, make it usable, and easily distribute it to users that include hedge funds and trading applications?
Coming from the industry, the founders knew that this problem was not a unique use case and that every financial institution that produces or consumes data eventually has to face that very issue. No traditional technology could scale terabytes of financial data so the team looked to the latest advancements in big data technology to finally build a full stack on top of Hadoop. TickVault was born and first put in service in late 2013.
Within the first year of launching the platform we started growing and with some very reputable clients in the bag, we were able to steadily expand the platform to incorporate add-ons and address industry problems as they emerged. The first client to subscribe to TickVault was National Bank of Canada. Soon after, CME Group, the second largest exchange group in the world, signed up for TickVault and it is now the foundation of their Datamine historical data service.
Read about: National Bank of Canada using TickVault and AWS for faster and better post-trade analysis (TCA)
Fast forward to 2017- Everyone is talking about big data and understands the possibilities. In a sense, we were head and shoulders above other emerging technologies. Many big data solution providers offer their platform as a service whereas we only sell the platform. The advantage of selling just the platform is that it can be integrated into your own infrastructure, on-premise or in your preferred cloud provider. With our innovative technology solving problems at financial institutions, we gained the attention of many interesting VCs and received an investment from Illuminate Financial late this summer to grow our marketing and sales capabilities. We have already started to expand our global presence with new hires in New York City and London and plan to increase those resources. Mark Beeston and Mark Rodrigues, two accomplished industry veterans, also joined our board and help with overall strategy, customer service, and global expansion.
WHAT DOES THE FUTURE LOOK LIKE?
Data at financial institutions will only continue to grow; the good news is that we can help them with our platform and expertise. With the recent investment, we will continue to increase our presence around the world and spread the word. We will also continue to evolve TickVault and provide new features to our current and future clients to adapt to changing needs.
We have many new features in our roadmap and our platform is tackling a number of interesting data challenges including data distribution, TCA, FRTB and more advanced analytics. To name but one example, our next module will expand the “self serve” data accessibility approach by giving more control to power users, internal development teams, and data scientists to map fields, ingest and normalize new sources of proprietary data.