All those bits and bytes only add up to something when they’re organized, arranged and made coherent.
Since the earliest days of the financial markets, information has been a key element of success. In the past, market information was conveyed by methods that now seem quaint: carrier pigeons, personal conversations, printed materials sent through the post.
Today more data is generated in a 24-hour period than in entire centuries of the past, traveling at lightning speeds to all corners of the globe. Accessing, sorting, compiling and leveraging that information is increasingly important in fast-paced markets and changing regulatory landscapes.
In this data economy, all kinds of businesses – from online retailers to pharmaceutical giants – are mining a wealth of information to better serve their customers, stay ahead of rivals and improve the bottom line. The task is no less crucial in financial services.
The term “Big Data” has been used in a variety of ways, applied to everything from traditional relational databases to Web-based sentiment-analysis tools. Just remember the three “V”s: the increasing velocity, volume and variety of information available from a growing range of sources. All those bits and bytes only add up to something when they’re organized, arranged and made coherent.
Not all analytics or data processing are Big Data. Trading and securities processing technologies have long been able to scale to meet the increased flow of electronic data resulting from market-structure change and increased electronic activity; high-speed trading is a good example. Complexity doesn’t necessarily mean Big Data. But Big Data is almost always complex – which means it requires intelligent solutions if its potential is to be tapped.
Big Data is directly tied to the rising importance of information management as a function within financial institutions. Regulatory, client and internal drivers have forced most firms to reevaluate the core reference data sets on which they base their trading, risk management and operational decisions. The proliferation of C-level executive positions dedicated to championing data management and data governance is a sign of this enhanced focus. Still, the majority of firms don’t have a Big Data strategy in place across the enterprise. And few that do are equipped to manage the available data by themselves. That’s where firms such as Thomson Reuters can help.
As things stand now, Big Data can be very useful in analytics for trading and quantitative research, both linked to revenue generation. An increasing number of firms are attempting to gain insight from unstructured sources such as Twitter®, news sites and blogs while mining internal data sets. Our guidance and tools help clients sort, connect, understand and leverage this data.
For example, through our vast Legal, Patent, and Life Science databases, we know exactly where any given drug stands in the FDA approval pipeline. That can have a huge impact on how the stock of a small pharma or biotech company dependent on those drugs performs. Similarly, we collect real-time data from satellite imaging systems and combine it with weather and historical ag data to make early predictions on crop yields – indispensable to any company engaged in the commodities markets.
Big Data challenges – and getting past them
One of the biggest challenges in Big Data management is matching business requirements with the appropriate technology. Many firms have yet to formulate a Big Data strategy, while others relegate it to specific tasks in siloed departments. If a clear aim is not articulated, the data could be misunderstood and the return on investment will be sub-optimal.
Another challenge is hiring employees or consultants with a deep understanding of both the financial services business and data technology. Some firms prefer to hire a team of individuals with the combined skills instead of a single person.
Data privacy is a major concern tied to the implementation of cloud computing technologies. Many firms are worried about putting proprietary information in the cloud, and though some have created private cloud networks, such projects can be costly.
Not everyone is convinced of the ROI when it comes to Big Data strategies. At Thomson Reuters, we think pressure to formulate a clear Big Data strategy will become central to top financial services firms, with benefits that far outweigh difficulties involved in implementation. We are working with our clients and partners now to aid this process.
Taking home the prize
The use of Big Data in capital markets is in an early stage, but there is little doubt it is growing as firms look for insight, speed of response and future scalability. A few have been early-adopters, mostly in specific areas such as trade analytics, market surveillance and risk modelling. As more capital-market-specific use cases for Big Data become apparent, our clients will increasingly adopt appropriate strategies. Many current developments are taking place in the front office, but the future will see more middle- and back-office implementation of Big Data strategies. As this transition occurs, Thomson Reuters won’t be passively observing from the sidelines – we’ll be on the playing field, in the middle of the fray, helping clients separate the “signal” from the “noise,” finding the key nuggets that allow them to gain insight, take action, and get results. In short, to be winners.
About the author
As Global Head of Content, Analytics and Valuation Services, Debra Walton leads more than 4,000 employees around the world with responsibility for setting the strategy and managing the operations of Thomson Reuters information and analytics offerings.
Debra has been a member of the Thomson Reuters senior management team since 2003 with roles across sales, marketing strategy, and product development. Prior to joining Thomson Reuters, Debra held senior executive roles at Cantor Fitzgerald, Nucleus Financial, and Dow Jones Telerate.