Data enhancement is a process to add value to information by accumulating additional information about a base set of entities and then merging all the sets of information to provide a focused view of the data.

There are two approaches to data enhancement. One focuses on incrementally improving or adding information as data is viewed or processed. Incremental enhancements are useful as a component of a later analysis stage, such as sequence pattern analysis or behavior modeling. The other approach is batch enhancement, where data collections are aggregated and methods are applied to the collection to create value-added information.

Data can be enhanced with different kinds of information, including the following.

• Auditing information, which provides some kind of historical tracking information through which the sequence of business processes applied to data can be traced.

• Temporal information, where data is enhanced by incrementally adding timestamps noting the time at which some event occurred. With data, this can refer to the time at which a transaction took place, the time at which a message was sent or received, the time at which a customer requested information, etc.

• Contextual information, which describes the place where some action was performed.

• Geographic information, which provides location information, such as locality coding, neighborhood mapping, latitude/longitude pairs, and other kinds of regional codes.

• Demographic information, such as customer age, marital status, gender, ethnic coding (for people), annual revenues, number of employees, and size of office space (for companies).