DataWare Housing Glossary of Terms - 'D'

Monday, December 17, 2007

DataFacts, 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
Data CleansingRemoving errors and inconsistencies
from data being inported to a data
Data Dictionarya 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 Developmentthe 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 Processa process whose resource consumption
depends on the data on which it operates.
Data MartA department-specific data warehouse.
A) Independent – fed from legacy systems
within the department
B) Dependent – fed from the enterprise data
warehouse (preferred)
Data MiningThe 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
Data ScrubbingRemoving errors and inconsistencies from
data being imported into a data warehouse.
Data TransformationThe modification or alteration of data as it
is being moved into the data warehouse.
Data TypeA 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 WarehouseA 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 WarehousingAn 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
Database SchemaThe logical and physical definition of a database structure.
Date/Time StampA 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 DatabaseA 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 WarehouseA 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
Denormalizationthe technique of placing normalized data
in a physical location that optimizes the
performance of the system.
Derived DataData whose values are determined by
equations or algorithms.
DimensionA 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/UpThe 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.


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