This is a glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the "DAMA Data Management Body of Knowledge (DAMA-DMBOK)". Topics include: Analytics & Data Mining; Architecture; Artificial Intelligence; Business Analysis; DAMA & Professional Development; Databases & Database Design; Database Administration; Data Governance & Stewardship; Data Management; Data Modeling; Data Movement & Integration; Data Quality Management; Data Security Management; Data Warehousing & Business Intelligence; Document, Record & Content Management; Finance & Accounting; Geospatial Data; Knowledge Management; Marketing & Customer Relationship Management; Meta-Data Management; Multi-dimensional & OLAP; Normalization; Object-Orientation; Parallel Database Processing; Planning; Process Management; Project Management; Reference & Master Data Management; Semantic Modeling; Software Development; Standards Organizations; Structured Query Language (SQL); and, XML Development.

Written by over 120 data management practitioners, this is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure. The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: Data Governance; Data Architecture Management; Data Development; Database Operations Management; Data Security Management; Reference & Master Data Management; Data Warehousing & Business Intelligence Management; Document & Content Management; Meta Data Management; Data Quality Management; Professional Development. As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are: To build consensus for a generally applicable view of data management functions; To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology; To document guiding principles for data management; To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches; To clarify the scope and boundaries of data management; To act as a reference which guides readers to additional resources for further understanding.