XBRL: XBRL and Big Data

By Brands, Kristine | Strategic Finance, August 2013 | Go to article overview

XBRL: XBRL and Big Data


Brands, Kristine, Strategic Finance


A lot of buzz has been circulating about Big Data recently. What is the hype all about, and what does it mean for management accountants? This month we'll look at Big Data, its challenges, strategies for harnessing it, and how eXtensible Business Reporting Language (XBRL) plays into the equation.

What Is Big Data?

Big Data is a term that was coined a few years ago to represent the massive increase in data and information being generated by organizations. International Data Corporation (IDC), a global information research and media firm, predicts that the amount of data will increase 44 times between 2009 and 2020 to 35.2 zettabytes, or 1.8 trillion gigabytes. Big Data includes traditional data that's generated within an organization and that can be stored in an organization's database or data warehouse, such as financial and administrative information, customer relationship management (CRM), supply chain and operations, and information technology (IT) management. It also includes other varieties of data, such as video, social media, e-mail, and sensor data (i.e., data generated by following Web clicks on a social media site), data that has mushroomed in the Internet Age.

With analytical tools, data can be sliced and diced to identify patterns and trends, perform business intelligence, and make predictions to provide information for decision making. While adoption of enterprise resource planning (ERP) systems like SAP and Oracle has shown companies that they can share data and information throughout the organization to run their businesses, the availability of Big Data takes data sets and their analytical potential to a higher and much more complex level. For example, a few years ago traditional sales forecasting models may have been based only on a company's sales history and input from the sales force. Big Data dramatically expands information resources that can feed the forecasting process by allowing companies to include other information, such as clicks on their product websites, competitive research, and economic forecasts. The Securities & Exchange Commission (SEC) has embraced Big Data analysis by contracting a system to monitor real-time trading activity on U.S. stock exchanges to look at suspicious activity that might indicate fraud. The system collects all order and trade activity to the 1/1,000th of a second and performs sophisticated analytics. The Trading and Markets and the Risk, Strategy, and Financial Innovation divisions of the SEC use the analysis generated by this system.

Challenges

There are several issues surrounding Big Data: the volume of data, a company's infrastructure to manage it, lack of qualified Big Data analysts, categorizing and managing the information, and data integrity risk. Organizations are being swamped with information and may not know how to manage and analyze it. The amount of data collected and gathered is too big to handle and analyze using traditional database analytical tools because organizations' IT infrastructures may be constrained by server and storage media size. …

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