18 – Data Enhancement

Publisher Summary Data enhancement is a process of increasing the value of a data instance (or data set) by appending additional value-added knowledge. Enhancement is performed by mapping one data set to other data sets and pooling information taken from multiple sources. The goal of enhancement is to identify actionable knowledge from collections of data that are used to improve consuming business processes. This chapter focuses on the data enhancement process and its dependence on parsing, data standardization, identity resolution, and matching, and how they are used as part of the enhancement process. The results of enhancement can only be trusted under the presumption that the source data sets are of sufficient quality to meet business objectives. In some instances, there is a predisposition to consolidate data from multiple sources without assessing their quality first. This process cannot be performed effectively without qualifying the expectations as well as the source data. Some aspects discussed in the chapter are worth being reviewed before implementing the matching and linkage. The process for answering these questions relies on the data quality principles and processes: data quality assessment, profiling, and the components of data validation and cleansing.