Data accuracy: The component of data integrity that deals with how well data stored in the data resource represents the real world. It includes a definition of the current data accuracy and the adjustment in data accuracy to meet the business needs.

Data architecture: The component of the data resource framework that contains all activities, and the products of those activities, related to the identification, naming, definition, structuring, quality, and documentation of the data resource for an organization.

Data cleansing: The process of manipulating the data extracted from operational systems so as to make it usable by the data warehouse.

Data completeness: An indication of whether or not all the data necessary to meet the current and future business information demands is available in the source systems data resource.

Data concurrency: When replicated data values are synchronized with the corresponding data values at the official data source. When the data values at the official data source are updated, the replicated data values must also be updated so they are consistent with the official data source.

Data conversion: The process of changing data from one physical environment to another. This process makes any changes necessary to move data from one electronic medium or database product to another.

Data extract: Data which normally resides on an operational system and which is removed from that system for loading into a data warehouse.

Data mapping: The process of assigning a source data element to a target data element.

Data mart: A subset of the data resource, usually oriented to a specific purpose or major data subject, that may be distributed to support business needs. The concept of a data mart can apply to any data whether they are operational data, evaluation data, spatial data, or metadata.

Data Mining: Data mining is the process of sifting through large amounts of data to produce data content relationships. A user who is data mining is looking for particular patterns or trends in his or her information.

Data modeling: A method used to define and analyze data requirements needed to support the business functions of an enterprise. These data requirements are recorded as a conceptual data model with associated data definitions. Data modeling defines the relationships between data elements and structures.

Data repository: A logical (and sometimes physical) partitioning of data in which multiple databases which apply to specific applications or sets of applications reside. For example, several databases (revenues, expenses) that support financial applications (A/R, A/P) could reside in a single financial data repository.

Data restructuring: The process of restructuring the source data to the target data during data transformation.

Data source: A specific data site where data is stored and can be obtained. Any source of data from a specific organization, such as a database or data file. A data source may include non-automated data, but it does not include unpublished documents containing data.

Data store: A place where data is stored; data at rest. A generic term that includes databases and flat files.

Data synchronization: The process of identifying active data replicates and ensuring that data concurrency is maintained. Also known as data version synchronization or data version concurrency because all replicated data values are consistent with the same version as the official data.

Data transformation: (1) The formal process of transforming data in the data resource within common data architecture. It includes transforming disparate data to an integrated data resource, transforming data within the integrated data resource, and transforming disparate data. It includes transforming operational, historical, and evaluation data within common data architecture. (2) Creating "information" from data. This includes decoding production data and merging of records from multiple DBMS formats. It is also known as data scrubbing or data cleansing.

Data visualization: The process of creating and presenting a chart from a set of data based on a set of attributes. It deals with understanding patterns, trends, and relationships in historical data, and providing visual information to the decision maker. 

Data Warehouse: A large, sharable database that allows users to tap into a company's vast store of operational data to track and respond to business trends and facilitate forecasting and planning efforts. The data warehouse is the heart of a business intelligence or eCRM solution.

Database Marketing: A term used to describe the art/science of selecting a database of a potential set of customers for a given product or need. For example, defining a target mailing list for people likely to acquire a new mutual fund product.

DBA: Database Administrator, the person in charge of maintaining the database.

Decision Support (DSS): see Business Intelligence.

Decision trees: A tree-shaped structure that represents a set of decisions. These decisions generate rules for the classification of a dataset.

Demographic data: Any data that locate, identify, or describe populations and their properties or characteristics. For example, demographic data will describe the age groups of people living in certain geographies, perhaps in certain income categories. Other dimensions or characteristics of demographic data include race, religion, political preferences, spending preferences, family size, and so on.

Derived data: (1) Data that is the result of a computational step applied to reference or event data. Derived data is the result of either relating two or more elements of a single transaction (such as an aggregation), or of relating one or more elements of a transaction to an external algorithm or rule. (2) Data that is derived from other data through a data derivation procedure, not by the measurement or observation of an object or event.

Derived data maintenance: The process for ensuring that active data derived is properly re-derived when their contributing data characteristics values change or when new contributing data characteristics appear.

Dimension: In data analysis, dimensions are variables in a situation. For example, time, product type and region are three dimensions of a sales' situation: product types are sold over time in different regions.

Direct marketing: A technique that brings the vendor's message directly to a market segment that has been identified as a potential buyer for the goods and services. Methods include direct mail and telemarketing.

Distribution channel management: Same as Sales Cycle Analysis but focuses on the distribution channel and partners.   

Drilling: When you want to click for deeper information within a report, this is known as drilling. (When you clicked the hyperlink to see this definition, that counts as drilling too!)