The Quality of Data and Information

Data can be thought of as having five basic characteristics that measure its quality. These five characteristics are accuracy, completeness, relevance, timeliness, and auditability.

Accuracy means freedom from errors. Hopefully, the data edit processes of an information system will detect and reject inaccurate data. It is possible, however, that inaccurate data may go undetected, as if, for example, $10.00 rather than $100.00 is entered.

Completeness of data is needed to ensure that valid information is derived from the data. Each piece of data received may be completely accurate, but if only half of the data are received then any information derived from the data may be invalid.

Relevance means that the data must pertain to the decisions at hand to be useful. A common complaint of managers today is that they receive too much data instead of just the information they need to make decisions.

Timeliness of data is important for decisions that involve rapidly changing conditions. Operational decisions about what must be done today to keep the business running require timely data.

Auditability of data refers to the ability to verify the accuracy and completeness of the data. For example, in accounting systems that help people manage the assets of the company, auditability is an important characteristic.

Because information is derived from data, these same five characteristics apply to it. For example, with market-intelligence information, some inaccuracy and incompleteness is acceptable, but timeliness is essential. For example, market intelligence may warn us that our competitors are about to make a major price cut. The exact details and timing of the price cut may not be as important as being warned far enough in advance to plan how to react. On the other hand, accuracy and completeness are critical for data used in accounting for the use of company assets such as cash, inventory, and fixed assets.