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Thomson Reuters, the world’s leading source of intelligent information for businesses and professionals, has today published the results of a commissioned survey ‘Big Data in Capital Markets: At the Start of the Journey,’ prepared by Aite Group to assess the implementation of big data strategy among financial services firms.
The research reveals that the majority of firms active in the capital markets do not have a big data strategy in place across the enterprise. Those firms implementing big data are mostly doing so in specific functional areas; the most popular uses for big data within respondent firms are analytics for trading and quantitative research. Other key takeaways from the research include:
- Half of respondents have invested in big data already. Most firms that have implemented a big data project hail from the banking and hedge fund communities, though a handful of asset managers have invested in such projects.
- Half of respondent firms either currently employ or plan to hire a data scientist in the next 24 months, correlating directly with these firms’ use of big data.
- The aspects of most importance to respondent firms in terms of big data deployments signal that actionable information and insight are equally pegged with scalability for future data volume increases.
- Respondents cite inadequate technical knowledge as the most commonly encountered challenge during a big data project, highlighting the need for appropriately skilled staff to implement such a strategy.
- As more capital-market-specific use cases for big data become prevalent in the market, firms will become more comfortable with these strategies.
- Current investments in big data are largely focused on revenue generating opportunities in the front office, but the future is likely to see much more focus on client retention, compliance function support, and enterprise risk management and governance.
“As more financial services firms move off the sidelines and ramp up their big data strategies, they will need to find insight, speed of response, and future scalability in order to boost their success in the market and take their business to the next level; we can help them find those corresponding keys to success,” said Debra Walton, chief content officer, Financial & Risk, Thomson Reuters. “Managing big data is increasingly becoming essential for success in finance, and Thomson Reuters is the essential partner for firms seeking to leverage it.”
“The capital markets have been relatively slow to adopt big data strategies, but they have begun to make some impact in a select few areas of the markets over recent years, including within sentiment analysis for trading, risk analytics, and market surveillance,” added Virginie O’Shea, senior analyst at Aite Group. “Priority levels for data management and analytics have risen as adoption of electronic trading has spread across different regions and asset classes, and the diversity of data sources and sheer volume of data have increased substantially over the last decade. In addition to traditional market data, growing interest around nontraditional, unstructured data has also added more complexity in terms of firms’ ability to deal with data.”
‘Big Data in Capital Markets: At the Start of the Journey,’ commissioned by Thomson Reuters and produced by Aite Group, explores the development of big data strategies and technologies across the buy-side and sell-side capital markets communities. It identifies use cases, challenges, and opportunities for big data technology in the sector and is based on May and June 2014 Aite Group surveys with 22 capital markets firms split between buy-side and sell-side participants. Participants are a subset of 423 firms contacted for the survey and represent firms that have some experience or knowledge of big data; the majority of those contacted indicated that they do not.
Thomson Reuters has been increasingly using Big Data technology for data management and analytics development. Thomson Reuters provides equities and credit based alpha and risk factors for investment managers and risk managers. Thomson Reuters has long been known for its enhanced sell-side forecasts; one example is StarMine SmartEstimates, which predict the direction of earnings surprises with a success rate of around 70% when the SmartEstimate significantly differs from the consensus estimate. The StarMine Text Mining Credit Risk model, developed using Big Data techniques, sits alongside the StarMine Structural Credit Risk model and the StarMine SmartRatios Credit Risk Model, forming part of StarMine’s credit risk offering.
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