Big Data and Risk Management

The volume of data most businesses collect, process and analyze continues to grow exponentially. New technologies and mobile accessibility are driving this “Big Data” issue to the executive- and even board-level at most companies. Before they can effectively tackle this issue, however, management must first agree on what it means.

Various definitions have been introduced, but the clearest, least complicated characterization of Big Data is this: the volumes of structured and unstructured data produced as a by-product of operating a company. That’s it. But it includes a lot.

Big Data includes, to name just a few categories, customer data, financial filings, employee information and operations data. And while it is starting to affect most industries, we can find the best lessons of how to manage Big Data’s ramifications in the financial services space.

The institutional investment community has been overwhelmed with data for years. For these professionals, Big Data is not a new concept. By creating models to analyze large, complex regulatory filings, financial institutions have long been mining Securities and Exchange Commission (SEC) documents for intelligence to make better investment decisions. For professional investors, the risk of missing some key intelligence hidden in readily available market data can have a huge impact on investment returns.

The insurance industry has not embraced Big Data in the same way, however. A recent report from analyst group Novarica describes the use of Big Data and analytics as “tepid” within the industry and anticipates a broadening analytics gap between other industries and insurance. And in a December 2011 Economist Intelligence Unit survey, only 39% of executives said they felt that they were making the right decisions for their companies. This uncertainty is only increasing.

Rather than shying away from the vast data stores, executives in other sectors must begin to use all this data to make better decisions and minimize risk within their firms. As the influx of information continues to grow, managers understand they must get in front of trends hidden in their firms’ data and find value in the intelligence derived to make better, more informed decisions. When decisions are data driven, the company’s productivity increases and enterprise risk levels decline.

In order for the data to be successfully used and analyzed, executives must first look at their own data creation. How will the data be used? What is its value? How will it be compared to other firms in the industry and other benchmarks? Once management agrees on a “top down” strategy as to what they believe Big Data can do for them in their risk management efforts, all stakeholders throughout the firm can better manage the Big Data problem and its inherent risks.

It is important to note that solving the Big Data problem cannot be seen solely as an IT exercise. By having the entire organization focus on the business goals in the planning stage, enterprises can zero in on both the technical and business aspects of Big Data.

Early on, during the data collection process, this can best be achieved through a simple structure. A rigid structure, meaning one an with in-depth classification system, will limit how the data may be analyzed in the future. Instead, an open “taxonomy” will allow executives to later parse the data in various ways. This is how the company will provide structure to the unstructured data being collected.

Once data is gathered, businesses must look at how the data is processed. A set of guidelines and best practices should be clearly outlined, shared and followed by all executives using the data. For instance, the SEC has mandated the use of a standards-based system, called “eXtensible Business Reporting Language” (XBRL), for all public company financial data. This classification system provides a uniform method for data collection and processing that allows firms to report information easily and analyze that data against competitors. If the XBRL format is adopted, not only is data entry easier, but companies may readily adhere to compliance regulations and internal GRC processes.

The additional benefit of using a standard method for collecting and processing data is the ability to use analytic tools specifically designed to bring context and meaning to the data. Ultimately, without proper analysis, you cannot act upon the information appropriately.

Big Data does not have to become a big problem. In fact, with a capable structure for collecting, processing and analyzing data, businesses can turn it into an asset.