Chapter 8 – Accessing Organizational Information – Data Warehouse

What is Data Warehouse?




 Ø  Defined in many different ways, but not rigorously
-          A decision support database that is maintained separately from the organization’s operational database.
-          A consistent database source that bring together information from multiple sources for decision support queries.
-          Support information processing by providing a solid platform of consolidated, historical data for analysis.
History of Data Warehousing
Ø  In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions
Ø  The data warehouse provided the ability to support decision making without disrupting the day-to-day operations, because;
-          Operational information is mainly current – does not include the history for better decision making
-          Issues of quality information
-          Without information history, it is difficult to tell how and why things change over time
Data warehouse fundamentals
Ø  Data warehouse – A logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making takes
Ø  The primary purpose of a data warehouse is to combined information throughout an organization into a single repository for decision-making purposes – data warehouse support only analytical processing
Data warehouse model
Ø  Extraction, transformation and loading (ETL) – A process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.
Ø  Data warehouse then send subsets of the information to data mart.
Ø  Data mart – contains a subset of data warehouse information.



 Multidimensional Analysis and Data Mining 
Ø  Relational Database contains information in a series of two-dimensional tables.
Ø  In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows
   -   Dimension – A particular attribute of information

Ø  Cube – common term for the representation of multidimensional information


-  Once a cube of information is created, users can begin to slice and dice the cube to drill down into the information.
- Users can analyze information in a number of different ways and with number of different dimensions.
Ø  Data Mining – the process of analyzing data to extract information not offered by the raw data alone. Also known as “knowledge discovery” – computer-assisted tools and techniques for sifting through and analyzing vast data stores in order to finds trends, patterns and correlations that can guide decision making and increase understanding
       -  To perform data mining users need data-mining tools
-        Data-mining tool – uses a variety of techniques to finds patterns and relationships in large volumes of information. Eg: retailers and use knowledge of these patterns to improve the placement of items in the layout of a mail-order catalog page or Web page.

 Information Cleansing or Scrubbing
Ø  An organization must maintain high-quality data in the data warehouse
Ø  Information cleansing or scrubbing – A process that weeds out and fixes or discards inconsistent, incorrect or incomplete information
Ø  Occurs during ETL process and second on the information once if is in the data warehouse
Ø  Contract information in an operational system
Ø  Standardizing Customer  name from Operational Systems
Ø  Information cleansing activities
-          Missing Records or Attributes
-          Redundant Records
-          Missing Keys or Other Required Data
-          Erroneous Relationships or References
-          Inaccurate Data

Ø  Accurate and complete information

Business Intelligence 
Ø  Business Intelligence – refers to applications and technologies that are used to gather, provides access, analyze data and information to support decision making efforts
Ø  These systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis to name a few
Ø  Eg; Excel, Access

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