What is Data Warehouse?
- 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
- 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
- Issues 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 takes
- 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
- Relational Database contains information in a series of two-dimensional tables.
- In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows
- Dimension –
A particular attribute of information
- 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 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
- 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
- Occurs during ETL process and second on the information once if is in the data warehouse
- Contract information in an operational system
- Standardizing Customer name from Operational Systems
- Information cleansing activities
- Missing
Records or Attributes
- Redundant
Records
- Missing
Keys or Other Required Data
- Erroneous
Relationships or References
- Inaccurate
Data
- Accurate and complete information
Business Intelligence
- Business Intelligence – refers to applications and technologies that are used to gather, provides access, analyze data and information to support decision making efforts
- 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
No comments:
Post a Comment