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Data Warehousing/Mining

Comp 150

Additional Information

The Need for Data Analysis

 Constant pressure from external and internal forces

requires prompt tactical and strategic decisions.

 The decision-making cycle time is reduced, while

problems are increasingly complex with a growing number of internal and external variables.

 Managers need support systems for facilitating quick

decision making in a complex environment.

 Decision support systems (DSS).

Decision Support Systems

 Decision Support is a methodology (or a series of methodologies) designed to extract information from data and to use such information as a basis for decision making.

 A decision support system (DSS) is an arrangement of computerized tools used to assist managerial decision making within a business.

• A DSS usually requires extensive data “massaging” to produce information.
• The DSS is used at all levels within an organization and is often tailored to focus on specific business areas or problems.
• The DSS is interactive and provides ad hoc query tools to retrieve data and to display data in different formats.

Decision Support Systems

 Four Components of a DSS

• The data store component is basically a DSS database.
• The data extraction and filtering component is used to extract and validate the data taken from the operational database and the external data sources.
• The end user query tool is used by the data analyst to create the queries that access the database.
• The end user presentation tool is used by the data analyst to organize and present the data.

Decision Support Systems

 Operational Data vs. Decision Support Data

• Most operational data are stored in a relational database in which the structures tend to be highly normalized.
• The operational data storage is optimized to support transactions that represent daily operations.
• Whereas operational data capture daily business transactions, DSS data give tactical and strategic business meaning to the operational data.

Decision Support Systems

 Three Main Areas in Which DSS Data Differ from Operational Data

• Time span  Operational data represent current (atomic) transactions.  DSS data tend to cover a longer time frame.
• Granularity  Operational data represent specific transactions that occur at a given time.  DSS data must be presented at different levels of aggregation.
• Dimensionality  Operational data focus on representing atomic transactions.  DSS data can be analyzed from multiple dimensions. Docsity.com

Table 13.2 Contrasting Operational And DSS Data Characteristics

Decision Support Systems

 The DSS Database Requirements

• Database Schema  The DSS database schema must support complex (non- normalized) data representations.  The queries must be able to extract multidimensional time slices.

Yearly Sales Summaries, Two Stores and Two Departments Per Store, Millions Of Dollars

Table 13.

Decision Support Systems

• Data Extraction and Loading  The DBMS must support advanced data extracting and filtering tools.  The data extraction capabilities should support different data sources and multiple vendors.  Data filtering capabilities must include the ability to check for inconsistent data or data validation rules.  The DBMS must support advanced data integration, aggregation, and classification capabilities.

Decision Support Systems

• End-User Analytical Interface  The DSS DBMS must support advanced data modeling and data presentation tools, data analysis tools, and query generation and optimization components.  The end user analytical interface is one of the most critical components.
• Database Size Requirements  DSS databases tend to be very large.  The DBMS must be capable of supporting very large databases (VLDB).  The DBMS may be required to use advanced hardware, such as multiple disk arrays and multiple-processor technologies. Docsity.com

The Data Warehouse

 The Data Warehouse is an integrated, subject-

oriented, time-variant, non-volatile database

that provides support for decision making.

• Integrated  The Data Warehouse is a centralized, consolidated database that integrates data retrieved from the entire organization.
• Subject-Oriented  The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company.

Table 13.6A Comparison Of Data Warehouse And Operational Database Characteristics

Creating A Data Warehouse

Figure 13.