Data are of high quality "if they are fit for their intended uses in operations, decision making and planning" (J. M. Juran). Alternatively, the data are deemed of high quality if they correctly represent the real-world construct to which they refer. Furthermore, apart from these definitions, as data volume increases, the question of internal consistency within data becomes paramount, regardless of fitness for use for any external purpose, e.g. a person's age and birth date may conflict within different parts of a database.
AE5The first views can often be in disagreement, even about the same set of data used for the same purpose. This book discusses the concept as it related to business data processing, although of course other data have various quality issues as well.
This book is your ultimate resource for Data Quality. Here you will find the most up-to-date information, analysis, background and everything you need to know.
In easy to read chapters, with extensive references and links to get you to know all there is to know about Data Quality right away, covering: Data quality, Bit rot, Cleansing and Conforming Data, Data auditing, Data cleansing, Data corruption, Data integrity, Data profiling, Data quality assessment, Data quality assurance, Data Quality Firewall, Data truncation, Data validation, Data verification, Database integrity, Database preservation, DataCleaner, Declarative Referential Integrity, Digital continuity, Digital preservation, Dirty data, Entity integrity, Information quality, Link rot, One-for-one checking, Referential integrity, Soft error, Two pass verification, Validation rule, Abstraction (computer science), Ado.Net, Ado.Net data provider, Wcf Data Services, Age-Based Content Rating System, Aggregate (Data Warehouse), Data archaeology, Archive site, Association rule learning, Atomicity (database systems), Australian National Data Service,
Comments