Data warehouse: A Global Information Sphere
A data warehouse is a collection of data designed to support management decision making. These warehouses contain a wide variety of data that present a clear picture of business conditions at a single point of time. The term Data Warehousing generally refers to the combination of many different databases across an entire enterprise or enterprise tape library (ETL).
Data warehousing is a technique that aims to help people get a good high-level understanding of what it takes to implement a successful data warehouse project.
Benefits of data warehouse:
A data warehouse maintains a copy of information from the source transaction system and this provides the opportunity to:
• Maintain data history.
• Integrate data from multiple source systems.
• Improve data by providing consistent code and description.
• Present the organization information consistently.
• Add value to operational business applications, notably customer relationship management systems.
Design:
Bill Inmon, he is one of the first authors on the subject of data warehousing, he defined a data warehouse as a central repository for the entire enterprise.
He is one of the leading proponents of the top-down approach to its design, in which the it is designed using an enterprise data model.
Inmon states that data warehouse is-
• 1. Subject oriented: Data of warehouse is organized so that all the data elements relating to the same real world event or object are combined together.
• 2. Non volatile: Data in the warehouse are never over written or deleted when submitted once, data are static, read-only as well as retained for future reporting.
• Integrated: It consists data from most or all of an organization's operational systems and this data are made consistent.
• Time-variant: The top down design process generates highly consistent dimensional views of data across data marts Top-down design has also proven to be robust against business changes.
Applications:
a) Decision support
b) Trend analysis
c) Financial forecasting
d) Churn Prediction for Telecom subscribers, Credit Card users etc.
e) Insurance fraud analysis
f) Call record analysis
g) Logistics and Inventory management
h) Agriculture
Future:
Data warehousing, has a history of inventions that did not receive market acceptance.
A 2009 Gartner Group paper estimated these developments in data warehousing market.
• Due to lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make capable decisions about significant changes in their business and markets.
• By 2012, business units will control at least 40 percent of total budget for that warehousing.
• By 2012, one-third of analytic applications applied to business processes will be delivered with the help of coarse-grained applications.
Data warehousing is a technique that aims to help people get a good high-level understanding of what it takes to implement a successful data warehouse project.
Benefits of data warehouse:
A data warehouse maintains a copy of information from the source transaction system and this provides the opportunity to:
• Maintain data history.
• Integrate data from multiple source systems.
• Improve data by providing consistent code and description.
• Present the organization information consistently.
• Add value to operational business applications, notably customer relationship management systems.
Design:
Bill Inmon, he is one of the first authors on the subject of data warehousing, he defined a data warehouse as a central repository for the entire enterprise.
He is one of the leading proponents of the top-down approach to its design, in which the it is designed using an enterprise data model.
Inmon states that data warehouse is-
• 1. Subject oriented: Data of warehouse is organized so that all the data elements relating to the same real world event or object are combined together.
• 2. Non volatile: Data in the warehouse are never over written or deleted when submitted once, data are static, read-only as well as retained for future reporting.
• Integrated: It consists data from most or all of an organization's operational systems and this data are made consistent.
• Time-variant: The top down design process generates highly consistent dimensional views of data across data marts Top-down design has also proven to be robust against business changes.
Applications:
a) Decision support
b) Trend analysis
c) Financial forecasting
d) Churn Prediction for Telecom subscribers, Credit Card users etc.
e) Insurance fraud analysis
f) Call record analysis
g) Logistics and Inventory management
h) Agriculture
Future:
Data warehousing, has a history of inventions that did not receive market acceptance.
A 2009 Gartner Group paper estimated these developments in data warehousing market.
• Due to lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make capable decisions about significant changes in their business and markets.
• By 2012, business units will control at least 40 percent of total budget for that warehousing.
• By 2012, one-third of analytic applications applied to business processes will be delivered with the help of coarse-grained applications.
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