In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions
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
- Issue of quality information
- Without information history, it is difficult to tell how and why things change over time.
Data Warehouse Fundamentals
Data warehouse – a logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making tasks
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
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.
Data warehouse then send subsets of the information to data mart.
Data mart – contains a subset of data warehouse information
Multidimensional Analysis and Data Mining
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.
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 find 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 find patterns and relationships in large volumes of information. Eg: retailers can 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
An organization must maintain high-quality data in the data warehouse
Information cleansing or scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
Occur during ETL process and second on the information once if is in the data warehouse
Contact information in an operational system
Standardizing Customer name from Operational Systems
Information cleansing activities
Accurate and complete information
Business Intelligence
Business intelligence – refers to applications and technologies that are used to gather, provide access, analyze data, and information to support decision making effort.
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
Eg: Excel, Access
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