Monday, December 17, 2007
|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
|Data Cleansing||Removing errors and inconsistencies|
from data being inported to a data
|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
|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
|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
|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
|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.