No industry is more data driven than Financial Services.
From market data to customer data to the back office, financial services firms generate, consume and trade in data in ways that no other industry does. As a result, financial services firms have the opportunity to analyze financial data and fundamentally transform their clients’ experiences, capitalize on evolving market conditions, prevent fraud, and mitigate risk using the vast amounts of customer, product, and market data at their disposal.
Financial Data itself does not create value, however, until it is put to use to address important business challenges or create competitive advantage.
The challenge facing financial services firms today, however, is the speed of their analytical capabilities. Billions of dollars have been invested in core technology to create competitive advantage from fiber to algorithmic trading models, but the speed of hypothesis generation and hypothesis testing has lagged, pulled down by legacy CPU technologies that are ill suited to querying and visualizating billions of records with millisecond delays.
Yet that is what every financial services organization is built upon, billions of records - growing with each passing second. This data may consist of many billions of records of trades and quotes of securities with up to nanosecond precision — which can translate into many terabytes of data per day. Making accurate, time-sensitive financial forecasts without consistent real-time data is virtually impossible.
This is precisely the problem that one of our hedge fund clients was experiencing. This quantitative shop had developed a rich proprietary dataset that grew exponentially over time. Their ability to efficiently ask questions of that dataset had not kept pace. Even with “bleeding edge” CPU technology and generous budgets, to find the answers to hard questions, to test new hypotheses would take minutes. The opportunity cost of minutes can run to the millions of dollars - on a single trade.
Working with MapD's GPU accelerated processing, the company was able to query their largest datasets and render the results graphically in milliseconds. Now investment ideas can be tested immediately, leading to a more fluid and creative process for hypothesis generation.