Thomson Reuters seeks openness, builds hooks into core products

Thomson Reuters builds open systems

While some of its competitors have built black boxes, Thomson Reuters has made openness a core value at the organizational level. That’s because the firm, which now bills itself as the “answer company”, believes that opening up its platform products makes them better longer term and produces better customer outcomes.

And that’s something in a market that’s facing challenges from all directions.

Financial firms contend with unprecedented challenges

Regulation and technology are exerting pressures on financial firms that require them to considerably up their games. That’s not really anything new and generally business-as-usual for an industry that’s still emerging in the wake of a global financial crisis. More expansive regulation, increased capital requirements, market structure changes, digitization and automation are all pushing financial firms to become more efficient at what they do and rationalize all the businesses they continue to compete in.

The speed required for innovation, though, is getting so fast that it’s hard for individual financial firms to keep up, according to Thomson Reuters’ Abel Clark, who heads up the firm’s activities in the financial industry. “We went from analog to digital, from voice to digital trading — these digitization trends have been with the industry for a long time, but we’re reaching an accelerated chapter in the financial industry’s evolution,” the executive said. “We’re seeing a step change, an accelerated evolution.”

It’s within this environment that Thomson Reuters has made a big push to open up its core products, creating platforms for its financial-industry customers and partners.

Managed services an industry-wide opportunity

In the wake of this increased pace of change, which requires continuous cost reduction, financial institutions are taking long, hard looks at what parts of their organizations are core to their businesses and jettisoning the rest. Given its positioning within the industry, Thomson Reuters sees an opportunity to provide shared infrastructure, much like Amazon has done with its AWS for the technology industry.

An example where the industry benefits from collaboration, TR’s Clark cites Know Your Customer (KYC) programs, required at all financial institutions. KYC is hugely important and every organization needs to run this process. “Because everyone runs their own KYC programs, there’s huge duplication across the industry,” said Clark. “There was an opportunity for Thomson Reuters to step in and provide industry-wide KYC services.”

Org ID is the name of the firm’s global KYC managed service. The service collects, classifies, and verifies a client’s identity in compliance with global regulators.

Openness a core value

At the core of Thomson Reuter’s KYC managed service is the firm’s open-sourced PermID, a unique identifier assigned to every piece of information collected about an individual or a firm. In fact, PermID is more than just a building block for good database management — it speaks directly to the firm’s strategy in building open systems. This theme of open systems is one Clark continuously comes back to. “Openness is core to our strategy — core to our firm — and existed long before the debate of open vs. closed began and before the terms were even coined,” he said.

TR’s Eikon terminal and its messenging product were constructed so that the financial industry can plug-and-play their own data and applications into them. Customers and partners can build proprietary apps directly into Eikon for internal distribution using a firm’s own data, Thomson Reuters’ feeds, and 3rd party data. The open research and trading platform is also well-suited, according to TR’s Clark, to act as a distribution mechanism for fintech startups. Financial technology firms can focus on what they’re good at and can use TR’s platforms as broad distribution channels.

Front end tools to information backbone

This ability to play nicely with customer and competitive systems is exemplified by the firm’s Thomson Reuters Enterprise Platform, or TREP. This middleware product serves as the information backbone of financial organizations. Clark says that TREP, which underpins the majority of trading floors globally, has also attracted a vibrant ecosystem that’s grown up around it. With 3rd party APIs, partners know that if they build out functionality on TREP, they’ll have access to a large global customer base. Conversely, customers know that when they choose an open system like TR’s Enterprise Platform, there are thousands of partners with whom to collaborate.

And this comes full-circle to TR’s belief that openness makes for better products and customer experience. “Openness wins longer term because it provides for more customer choice,” Clark said. “When there’s sharing and collaboration happening between partners, the financial industry will be better equipped to thrive in this challenging era of innovation.”

Photo credit: cogdogblog via / CC BY

Unbundling financial data, Tammer Kamel’s Quandl is powering an entire finance app ecosystem

interview with tanner kamel of quandl

Tammer Kamel is CEO and co-founder of Quandl

What is Quandl and what was the inspiration for creating it?

Tammer Kamel, Quandl
Tammer Kamel, Quandl

Quandl is a service that delivers financial data.  We bring together over 25 million financial datasets on a single website, and make this data available to analysts in any form they want.

The inspiration for Quandl was my own frustration working with data when I was an analyst. I’ll give you one example among many: I was working in Python trying to create a simulation around oil and uranium prices for the past 20 years. It was a struggle to find the datasets on Google. When I finally did, the formats were a mess. It took half an hour to get them formatted and merged. Then I had to repeat the same exercise every day to update the model.

This lead me to think, “Why isn’t there a platform where I can type in ‘uranium prices’ and get a clean dataset? Why can’t I get it directly into Python, or other tools I use, like Excel, R and Matlab?”. I knew that a platform that could take a tedious, half-hour process and cut it down to 10 seconds would make life easier for me and probably millions of other people. That was the inspiration.

Why has it been hard historically for everyone to access financial data? Analysts had it but the rest of us didn’t. Why?

Data used to be a scarce resource: hard to produce, hard to acquire, hard to use effectively. As a result, only the largest institutions could afford to pay for data. Individuals and smaller firms were priced out.

But that era is gone. Today, we live in an age of data ubiquity: there’s data everywhere. Anyone with a web connection can get stock quotes, or currency exchange rates, or company financials, or demographic forecasts. Access to raw data is no longer a limiting factor. That’s a huge contributor to the rise of solutions like Quandl.

Of course, raw data can only take you so far. For data to be usable, it needs to be cleaned, structured, documented, and quality-controlled. This is where the democratizing power of the internet comes into play. Quandl’s marketplace model replaces inefficient, pre-internet, “factory-style” data production with a network of specialist data creators and vendors.

Quandl gives these publishers a distribution channel and a transaction mechanism. It lets them make their databases available to the whole world. Thanks to Quandl, they can start competing with larger publishers. And they’ve been amazingly effective at that, as our success shows.

A second dynamic at work is unbundling. It’s similar to what is happening to cable TV. With traditional terminals, users were forced to pay an annual subscription to every database, whether they needed it or not. It was the business model, just like cable’s business model forced consumers to buy a bundle of 500 channels when they only watched an average of 15. With Quandl, users only pay for the databases they need so we’re much more accessible.

Both of these trends – scarcity replaced by abundance, and bundles replaced by choice – are patterns we’ve seen play out time and again in other industries. Internet platforms tend to disrupt pre-internet businesses. We’re seeing it happen now in the world of financial data.

Looking at Product Hunt, it appears a lot of new apps are being built using your APIs. What’s happening here and what does this ecosystem look like years down the road?

Yes, this is an exciting development for us. Currently, there are over a hundred financial and analytical apps built on top of Quandl’s API, all created by the Quandl community.

You can get Quandl data into scientific and mathematical tools like Mathematica, Matlab, Maple, Octave, R, SAS and Stata. You can also access our data from general purpose languages like C/C++/C#, Java, Julia, Python and Ruby. There are integrations for trading platforms like AmiBroker, Money.Net, Quantopian, Tiingo and TradingView; and for analytic tools like Mode, Plotly and Statwing. Finally, there are dozens of business intelligence and financial apps with sophisticated, next generation analytics – like Ayasdi, Domo, Kensho and Premise – that use our data.

Our philosophy here is quite simple: “Let a thousand flowers bloom.” Instead of getting into analytics or visualization ourselves, we empower other businesses to build their own solutions. We’ve seen other data providers restrict users to the analytics already present in their terminals. This means that users can’t choose the tool they want to work in.

It also means that they’re paying for all the analytics, just like they’re paying for all the databases, even if they only use a fraction of them. It’s the 500 channel problem all over again – they have to pay for a bundle of analytics, everything from yield curve calculators to option pricers to commodity shipping analysis, just to get the few that they actually need.

If you’re a bond trader, you don’t need an equity valuation model. If you’re a commodity trader, you don’t need a yield curve calculator.  If you’re a market technician, you don’t need a fundamentals-based screener. But if you’re a terminal subscriber, you’re paying for everything.

The app model, on the other hand, allows each analyst to select their own tools. Not only is it more economic, it’s also more powerful: it allows users to pick and choose the precise apps that are optimal for their needs, instead of a generic one-size-fits-all approach.

Our vision for a few years down the road is a rich ecosystem of apps powered by Quandl.  Each app is hyper-focused and tailor-made for each specific use case: a tool for every task, and every task with its own tool. We believe this is the future of data analysis.

Are there particular challenges you’ve encountered in building out Quandl — how did you overcome them?

Building a delightful user experience is always a challenge. There are so many ways that people use data. We want to give our users maximum flexibility, but we also want to keep it simple. We think we’ve achieved this balance well so far, but there’s always a temptation to add more complexity. We’ve put a lot of resources into making the experience faster, easier and more intuitive. This hasn’t been a priority in the data industry, but it’s a priority for us.

While the front end might appear simple, the back end is anything but.  We’ve had to solve some hard technical challenges to build Quandl.  Bringing together millions of datasets from thousands of different publishers on a single, unified platform requires non-trivial advances in data parsing, structuring and delivery.

Another challenge is scale: building infrastructure to serve 50 million data downloads a month with a millisecond response time. Our clients include many of the biggest banks, asset managers and hedge funds in the world, so speed and reliability are crucial. We’ve done some exceptional engineering around the Quandl API to make this happen.

What’s next for 2016?

Our highest priority, for 2016 and probably forever, is adding new data to the platform. The more data we have, the better we become for all our users.

In addition to expanding our time-series data coverage, we will be adding a couple of new data types to Quandl: non-time-series data and intraday data.  We’ll also be adding more vendors and unique databases.

The Quandl website, ecosystem and feature set will continue to evolve as we listen to what our customers need. That’s a never-ending process of improvement for us.

Photo credit: Glyn Lowe Photoworks. via Visual hunt / CC BY