Herein we provide an introduction and overview to the field of risk management for interested non-specialists, with a particular focus on the data and information requirements. Financial institutions produce enormous amounts of data. The challenge for risk managers is finding those data most useful for the task at hand, which might involve analyzing market, credit, operational, liquidity or systemic risk. All data are not equal, and their interpretation has been the subject of much debate, especially after the recent financial crisis. Some have asked whether data provide guidance or distraction in understanding financial risks. This chapter irst discusses some fundamental distinctions to keep in mind relative to all risk indicators used when actually managing risk. It closes with a brief tour of some of the core risk modeling tools in current practice, with an emphasis on their data and information needs.

Data versus information

The value and pitfalls of “actual” data

The modern world, characterized by pervasive computing power and massive electronic storage, is awash in data. As far back as 2007, Google was reportedly maintaining 100 exabytes of data (100 EB = 1 × 1020 bytes = 1 billion GB), largely to index the far more voluminous material on the Internet. Overall global storage capacity is measured in zettabytes (1 ZB = 1 × 1021 bytes; see Hilbert and López, 2012). In their raw and unorganized form, however, the data that surround us do not constitute information.