Data quality assertions and business rules

Abstract

This chapter describes the process of measuring the levels of data quality with respect to the dimensions of data quality, defining data quality expectations while maintaining high-quality information levels that allow a business to achieve a high level of automated information processing. High level of automated information in turn allows for greater scalability as bad data affects scalability operations—data quality rules like Null value rules include specifications, null values, and non-null values, Value rules restricts the values that can populate an attribute, domain membership rules cover restricted sets of values with business meanings from which attributes can draw their values, and domain mappings along with relation rules are business rules that are used for a number of benefits, especially when it comes to managing business and data quality rules as content. In particular, there are opportunities for automating a large part of the data acquisition and validation process, as well as the opportunity to pinpoint problem processes with respect to poor data quality.