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Data Warehousing

Foundations of Data Warehousing

Data warehousing is the proven architecture for delivering subject-oriented, stable, analytical information for decision making.  Effective data warehouse development provides many business benefits. Introduction A data warehouse is an enviroment that combines an integrated decision support database with software to collect, cleanse, transform, and store data from a variety of operational and external sources. These technologies are combined to support

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Agile Framework for Managing and Measuring Enterprise Business Intelligence

To be agile, business intelligence and analytics systems need frameworks and metrics to enable stable evolution. Introduction Enterprise Business Intelligence and analytics solutions are complex implementation efforts because of the Develop – Support (Growth-Sustain) cycles followed concurrently. Every enterprise wide BI system continuously evolves over a period with new business functionality added at regular intervals, and they need to be in

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Dimensional Data Modeling Fact Qualifier Matrix

Fact Qualifier Matrix capabilities can be expanded to improve the value of conformed dimensions in business intelligence and analytics systems Introduction The venerable Fact Qualifier Matrix (FQM) has been a tool long used for demonstrating for dimensional models how fact and dimension tables intersect to ensure that dimensional models are conformed. A key principle of dimensional modeling is that dimensions must

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Performance Benefits of Surrogate Keys in Dimensional Models

There are many benefits to implementing surrogate keys in a dimensional model design for a data warehouse data model Introduction There are many reasons for implementing surrogate keys in Dimensional Models (enterprise data modeling) such as insulating dimensions from changes to source systems and enabling historical versioning of dimension members. However, query performance in the data warehouse  is another primary

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Security Threats in the Data Warehouse Environment

Data warehouse implementations are vulnerable to internal as well as external security threats.  Follow these mitigating steps to reduce the risks. Introduction Security threats exist against your information resources, whether the systems are accessible through the Internet or buried deep within your internal network, available only to authorized users, including the enterprise data warehouse  and related analytics systems.  The increasing

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Migrating From “Independent” Data Marts

Independent data marts cause many problems in data warehouse architecture; combining to result in numerous issues for business intelligence and analytics solutions. Introduction A severe disease has spread to epidemic proportions throughout our society.  This disease is particularly dangerous as it effects are not readily identifiable at the time of infection.  However if this condition goes untreated it can be

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Enabling High Quality Analytics through a Data Validity Dimension

Using a specialized data validity dimension in a data warehouse design can support data quality and analytics capabilities. Introduction While working on an Enterprise Data Warehouse for a state court system the issue of poor data quality in the source systems became apparent.  Referential integrity was not strictly enforced and there was very little in the way of attribute level constraints.  One

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Data Mining

Data mining is a powerful analytical activity that can be used with data warehouses and with operational systems, yielding valuable insights. Introduction Data mining is often confused with “writing lots of reports and queries,” when in fact data mining activities do not involve any traditional report writing or querying at all.  Data mining is performed through a specialized tool, which

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The Evolution of the Corporate Information Factory

Introduction In the beginning were applications.  And applications served the corporation well until there was a desire for integration of historical information. However, applications supported neither integration nor historical data.  When it was noticed that applications – once built and put into production – were unable to be reshaped, the limitations of trying to get information out of applications became

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Building a Data Warehouse in Iterations

Effective data warehouse development requires an iterative approach that results in a robust, well-defined and usable system for analytics. This article is excerpted from Data Warehouse Project Management (Addison-Wesley, Adelman and Moss, © September 2000). Data Warehouse Iterations Introduction A data warehouse cannot and should not be built in one Big Bang. Instead, a data warehouse is an evolving system

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