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Displaying 690–705 (of 703)

Popularity is determined by a calculation of page views and community comments.

Title Popularity * Date
Introduction to the Government Information Factory (GIF) Summary
The Government Information Factory is a framework for planning the architecture to support the information needs of a government agency.

Topics: Data Warehousing, Best Practices, Information Management, Data Marts, Data Modeling and Design, Architecture

by Bill Inmon | 0 comments
19-07-2004
Dollars and Homogeneity Summary
Rationalized comparisons across time and between companies are more dream than reality.

Topics: Data Governance, Business Intelligence, Compliance

by Bill Inmon | 0 comments
15-07-2004
Data Modeling in the Government Information Factory Summary
The data model is an intellectual roadmap to the contents of the government information factory.

Topics: Data Warehousing, Best Practices, Information Management, Data Modeling and Design, Architecture

by Bill Inmon | 0 comments
08-07-2004
ODS: For Flexibility and Speed Summary
The “virtual” ODS - what you need for flexibility and speed!

Topics: Data Integration, Business Intelligence, Performance Management, Data Acquisition and Delivery, Information Management, Data Marts, Data Modeling and Design, Data Warehousing, OLAP, Query & Reporting, Real-Time Enterprise, Architecture

by Bill Inmon | 0 comments
08-07-2004
Methodology and The Government Information Factory (GIF) Summary
To meet the needs of the GIF there are three methodologies – the heuristic method is the most flexible.

Topics: Information Management, Data Modeling and Design, Architecture

by Bill Inmon | 0 comments
01-07-2004
Information Systems Architecture Summary
The three primary elements of information systems architecture - a plan, a paradigm, and a construction - all rely upon each other. Any one by itself is an exercise in futility. Any two are a curiosity. But all three together form a very solid foundation.

Topics: Data Warehousing, Information Management, Data Marts, Architecture

by Bill Inmon | 0 comments
01-07-2004
Data Quality Summary
An enterprise runs under the assumption that the data contained inside it is accurate and valid. If the data is not valid, then there is no accountability for the decisions based upon it.

Topics: Data Integration, Best Practices, Business Intelligence, Information Management, Data Quality, Data Warehousing, Enterprise Information Integration (EII)

by Bill Inmon | 0 comments
24-06-2004
Making Sense of Analytic Applications Summary
Now that you’ve warehoused the data – Who do you want to analyze it?

Topics: Analytics, Business Intelligence, Performance Management, Data Acquisition and Delivery, OLAP, Query & Reporting

by Bill Inmon | 0 comments
17-06-2004
Iterative Development Summary
Some speakers continue to promulgate the “Big Bang” theory that a data warehouse must be built as a single project. Maybe they are simply misinformed or have their own agenda.

Topics: Data Warehousing, Best Practices, Information Management

by Bill Inmon | 0 comments
17-06-2004
The All in One Data Warehouse Summary
The push to combine real-time on-line processing and OLAP or to combine exploration and data mining with data warehousing will cause problems with transaction guarantees and integrity of data.

Topics: Data Warehousing, Best Practices, Data Mining and Statistical Analysis, OLAP, Real-Time Enterprise

by Bill Inmon | 0 comments
10-06-2004
Who is Going to Use the Data? Summary
Bigger isn't always better! The value of data warehouses is determined by real usage not volume.

Topics: Data Warehousing, Information Management, Data Mining and Statistical Analysis, Query & Reporting

by Bill Inmon | 0 comments
10-06-2004
Capacity Planning for the Data Warehouse Environment Summary
Capacity planning is essential for all data warehouse environments. Data warehouses grow at high rates and must be managed to keep budgets under control.

Topics: Data Warehousing, Best Practices, Storage

by Bill Inmon | 0 comments
01-06-2004
Information Agility Summary
There are many reasons that people choose a data warehouse environment. These may be the relief from the pain of legacy systems, a need for historical and integrated data among others.

Topics: Data Warehousing, Best Practices, Information Management, Data Modeling and Design

by Bill Inmon | 0 comments
27-05-2004

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