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LESSON - Performance Matters for Business Intelligence in Financial Services

By Michael Weir, Senior Director of Marketing, ParAccel

Rapidly accumulating business intelligence data presents an amazing opportunity for all types of corporations. But “big data” can be overwhelming—and analyst firm IDC predicts this data will multiply by 44 times over the next decade, so it won’t get any easier to make sense of.

Historical and real-time data could give corporations a major competitive advantage, but they must figure out how to access and analyze it quickly enough to make a difference. Legacy row-based database solutions weren’t built for it; they’re slow, expensive to maintain, and require static and clumsy work-arounds to get answers. Fortunately, new analytic database solutions can handle the avalanche of data by finding smart and efficient ways to access and analyze it quickly across many sources.

Financial institutions in particular need high-performance analytic databases more than ever. Rapidly changing markets, new regulations, new investment options, and new market opportunities must be constantly and rapidly analyzed for new insights with risk analysis, which relies on terabytes and petabytes of historical data. This requires the superior performance delivered by today’s analytic databases.

Is Performance Just Query Speed?

Performance is speed sounds simple, but there are many reasons speed plays an integral part in the performance of an analytic database.

First, there is the speed to deploy. After the market and housing collapse, financial institutions faced new regulations that required greater and more intelligent access to their existing data warehouses. For example, analytic solutions are now integrated to execute standardized portfolio stress testing, drilling into the complexities of a multitude of investments never previously explored.

Once operational, answers must be accurate and delivered in time to make a difference. Financial services firms react immediately to market turns, predicting them to optimize trades for their clients and bottom lines. Queries that identify a trade milliseconds faster than competitors can mean millions of dollars in additional revenues.

Performance is speed sounds simple, but there are many reasons speed plays an integral part in the performance of an analytic database.

In addition to speed, businesses must be able to administer agile, complex, and flexible queries. By being iterative and not incremental when making database queries, firms can create hundreds of new query models in the same time it previously took to develop a few. This leads to deeper exploration of market change risk analysis, and entirely new predictions or new business models. More important, complex analytic queries against massive amounts of data can identify fraud risks before fraudulent transactions are made.

Asking these new questions is critical, but businesses won’t advance if questions aren’t answered quickly. With financial firms trying to predict the future, make instantaneous trades, or identify fraud before it happens, queries that previously required hours or days must be answered in minutes or seconds.

Speed to return on investment (ROI) is the final component. The faster these steps occur—the analytic database is deployed, queried, and delivers answers—the faster a company sees ROI. To do so, businesses should look for analytic databases that reach these high-performance standards, both in analytic speed and ease of deployment and operation.

Meeting the Analytic Challenge

Today’s analytic databases meet the business intelligence challenge by advancing performance in novel ways. Combining shared-nothing massively parallel processing (MPP) with end-to-end columnar approaches, overall scale and analytic speed-to-answer are infinitely improved. Query optimization and analytic extensibility allow businesses to utilize and move beyond existing SQL to any level of complexity without hindering performance.

To quickly realize ROI, software-only analytic database solutions give corporations the flexibility and affordability of commodity hardware, retrieve existing data wherever it’s traditionally stored, and use the tools and people already in place. With no tuning or additional workflow required, DBAs can spend more time running queries and analyzing answers, and less time extracting and tuning data.

Whether in finance, retail, or medical research, businesses rely on insight derived from their rapidly expanding data stores. Today’s analytic databases deliver superior performance by creating smart ways to access and manage the data explosion across a broad spectrum of sources, quickly delivering new business value.

This article originally appeared in the issue of .

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