DataWare Housing Glossary of Terms - 'D'

Data	Facts, concepts, or instructions that a computer records, stores and processes. Used in conjunction with INFORMATION SYSTEMS, “raw data” is organized in such a way that people can understand the results.
Data Cleansing	Removing errors and inconsistencies from data being inported to a data warehouse.
Data Dictionary	a software tool for recording the definition of data, the relationship of one category of data to another, the attributes and keys of groups of data, and so forth.
Data Driven Development	the approach to development that centers around identifying the commonality of data through a data model and building programs that have a broader scope thn the immediate application.
Data Driven Process	a process whose resource consumption depends on the data on which it operates.
Data Mart	A department-specific data warehouse. A) Independent – fed from legacy systems within the department B) Dependent – fed from the enterprise data warehouse (preferred)
Data Mining	The process of finding hidden patterns and relationships in data. For instance, a consumer goods company may track 200 variables about each consumer. There are scores of possible relationships among the 200 variables. Data mining tools will identify the significant relationships.
Data Scrubbing	Removing errors and inconsistencies from data being imported into a data warehouse.
Data Transformation	The modification or alteration of data as it is being moved into the data warehouse.
Data Type	A data type defines the type of data stored in a specific database column, such as date, numeric or character data. Significant differences in data types exist between different platforms’ databases.
Data Warehouse	A data warehouse is a subject oriented, integrated, non volatile, time variant collection of data. The data warehouse contains atomic level data and summarized data specifically structured for querying and reporting.
Data Warehousing	An enterprise-wide implementation that replicates data from the same publication table on different servers/platforms to a single subscription table. This implementation effectively consolidates data from multiple sources.
Database Schema	The logical and physical definition of a database structure.
Date/Time Stamp	A stamp added by an application that identifies a task or activity by the date and time it was initiated and/or completed. This can appear as part of a transaction log, message queue content in job logs.
Decentralized Database	A centralized database that has been partitioned according to a business or end-user defined subject area. Typically ownership is also moved to the owners of the subject area.
Decentralized Warehouse	A remote data source what users can query/ access via a central gateway that provides a logical view of corporate data in terms that users can understand. The gateway parses and distributes queries in real time to remote data sources and returns result sets back to users.
Decision Support Systems (DSS)	Software that supports exception reporting, stop light reporting, standard repository, data analysis and rule-based analysis. A database created for end-user ad-hoc query processing.
Denormalization	the technique of placing normalized data in a physical location that optimizes the performance of the system.
Derived Data	Data whose values are determined by equations or algorithms.
Dimension	A Dimension is typically a qualifiable and text value, such as a region, product line, and includes date values. It defines the secondary headings or labels that make up the body of the report. Each of the dimensions is repeated within each group. Usually, you use items containing text values (for example, Year or item type) for table dimensions. For example, if you select Item Type to be your table dimension, Item Type is a dimension within each group header. Under the dimension "Item Type," appears the name of each kind of item (for example, CD ROM, or HARD Drive). and corresponds to the . A fact is an quantifiable value, such amount of sales, budget or revenue.
Drill Down/Up	The ability to move between levels of the hierarchy when viewing data with multiple levels. A) Drill down – changing a view to a greateer level of detail B) Drill up – changing a view to a greater level of aggregation.