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Decision Support Systems (DSS) - Coggle Diagram
Decision Support Systems (DSS)
DSS
The Need for Data Analysis
External and internal forces require
tactical and strategic decisions
Search for competitive advantage
Business environments are dynamic
Decision Support Systems
Different managers require different
decision support systems (DSS) to analyse data to make decisions
Decision Support
Is a methodology
Extracts information from data
Uses information as basis for decision making
Arrangement of computerized tools
Used to assist managerial decision
Extensive data “massaging” to produce information
Used at all levels in organization
Tailored to focus on specific areas and needs
Interactive
Provides ad hoc query tools
Operational vs Decision Support Data
Operational Data
Relational, normalized database
Optimized to support transactions
Real time updates
DSS Data
Snapshot of operational data
Summarized
Large amounts of data
Data Warehouse
Database schema
Support complex (non-normalized) data
Extract multidimensional time slices
Database size
Very large databases (VLDBs)
Contains redundant and duplicated data
Operational Data
Data Extraction
Extract
Filter
Transform
Integrate
Classify
Aggregate
Summarize
Data Warehouse
Integrate
Centralized
Holds data retrieved from entire organization
Subject-Oriented
Optimized to give answers to diverse questions
Used by all functional areas
Time Variant
Flow of data through time
Projected data
Non-Volatile
Data never removed
Always growing
Data Marts
Single-subject data warehouse subset
Decision support to small group
Can be test for exploring potential benefits of
data warehouses
Address local or departmental problems
Online Analytical Processing (OLAP)
Advanced data analysis environment
Supports decision making, business modeling, and
operations research activities
Characteristics of OLAP
Use multidimensional data analysis techniques
Provide advanced database support
Provide easy-to-use end-user interfaces
Support client/server architecture
ROLAP (Relational OLAP)
Uses relational DB query tools
Extensions to RDBMS
Multidimensional data schema support
Data access language and query performance
optimized for multidimensional data
Support for very large databases (VLDBs)
MOLAP (Multidimentional OLAP)
OLAP functionality to multidimensional
databases (MDBMS)
Stored data in multidimensional data cube
Affected by how the database system
handles density of data cube called sparsity
Star Schema
Data-modeling technique
Maps multidimensional decision support into
relational database
Four Components:
Facts
Dimensions
Attributes
Attribute hierarchies
Data Mining
Seeks to discover unknown data characteristics
Automatically searches data for anomalies and
relationships
Data mining tools:
Analyze data
Uncover problems or opportunities
Form computer models based on findings
Predict business behavior with models
Require minimal end-user intervention