Report: Big Data Analytics Bottleneck Challenging Global Capital Markets Ecosystem
New research says converting massive quantities of financial data to information needs trusted collaborative partners and solution providers.
In new research, TABB Group says that big data issues are beginning to impact the global capital markets landscape with the management and consumption of big data seen creating an analytics bottleneck across the ecosystem.
According to TABB’s director of data and analytics (DnA), Paul Rowady, who wrote “Analytics Bottleneck: Battling the (Unfortunate) Shape of Big Data,” with few exceptions, such as some of the largest tier-1 banks, capital markets firms do not “produce” much big data, which means the big-data ecosystem” toolkit necessary to store and manage all that data has not yet known the widespread adoption we might expect to find in other industries that are more dependent upon digital-era capabilities.
He explains, however, that the extreme growth of data for the foreseeable future has distorted conversion rates of that data into new, and in some cases, desperately needed analytics. The increasing complexity of big data for capital markets use cases means that virtually no firm will possess the capability to manage big data challenges without the help of content aggregation and platform management partners.
Every data source requires some degree of handling, processing and technical infrastructure for end users to harvest a meaningful amount of information from it, Rowady says. “Even the simplest trading firm today may rely on hundreds of data sources – market data, fundamental, transactional and others – and the most complex global banks and asset managers are likely to rely on thousands of data sources, plus the temporal permutations of each of them.”
To satisfy a modern trading firm’s growing competitive requirements and that of their intermediaries and support counterparties, next-generation capital markets content platforms need to be able to leverage each of these data types, alone, in combination and respective of a very wide variance in update frequencies. This is why the prevailing trading-firm culture based on clusters of small teams that never talk or share data is no longer appropriate due to that framework’s inability to scale through collaboration.
“What’s required is a much more collaborative community of both internal and external specialists, representative of a new enterprise data management (EDM) strategy,” says Rowady. Competitive advantage will come from a combination of speed, smarts and uniqueness. As a result, today’s analytics need to be updated faster, be more accurate (by avoiding excess sampling), integrate data that few others have access to, or some combination of these.
When it comes to the growing spectrum of capital markets use cases, everything needs to be treated increasingly like big data. In parallel, he says, “given the need for speed, accuracy and uniqueness, more capital markets use cases need to be treated as high-performance. The conclusion for where we are headed follows easily from that: big data and high performance will converge.”