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21 Jul, 2017

Challenges of Data Warehouse Re-Architecture

21 July, 2017
Bruce D. Johnson
Data Warehousing

Data warehouse / business intelligence / analytics environments require business and IT cooperation for successful architecture and design Introduction In working with many large corporations across a variety of industries, it is clear that we all face the reality of re-architecting data warehouses, whether we want to admit it or not. Since data warehouses are business driven (rethink your strateg



23 Feb, 2017

Data Warehouse Standards

23 February, 2017
Sid Adelman
Data Warehousing

Standards are different from guidelines.  Standards are firm and must be followed.  Successful data warehouses use standards Introduction Many dog owners give their dogs what they consider to be commands.  They are really more like guidelines. ("Boscoe come!… pause, pause, pause…  Well I guess Boscoe is busy with his chewy toy and doesn't want to come just now.") A number of organizations



1 Oct, 2008

Agile Framework for Managing and Measuring Enterprise Business Intelligence

01 October, 2008
Karthikeyan Sankaran
Data Warehousing

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



1 Jul, 2006

Security Threats in the Data Warehouse Environment

01 July, 2006
Michael F. Jennings

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 



1 Jan, 2006

Enabling High Quality Analytics through a Data Validity Dimension

01 January, 2006
Pete Stiglich
Data Warehousing

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. 



1 Oct, 2002

Data Mining

01 October, 2002
Larissa Moss
Data Warehousing

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 executes



1 Oct, 2001

Building a Data Warehouse in Iterations

01 October, 2001
Larissa Moss
Data Warehousing

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 evol



1 Apr, 2000

Data Warehouse Costs

01 April, 2000
Sid Adelman
Best Practices

Developing a process for cost justifying a data warehouse and metrics for measuring various costs associated with a data warehouse project are essential for success This article is excerpted from a book titled Data Warehouse Project Management (published by Addison Wesley Longman (© 2000), Sid Adelman, Larissa Moss) Introduction Not every data warehouse project will be cost justified initially. 



1 Jan, 2000

Data Warehouse Success Measures

01 January, 2000
Sid Adelman
Best Practices

Knowing how to measure success and failure, and qualifying results for a data warehouse or analytics project is essential for all project managers. This article is excerpted from a book titled Data Warehouse Project Management (published by Addison Wesley Longman (© 2000), Sid Adelman, Larissa Moss) Introduction There has been much heated discussion over the failure rate of data warehouses and de




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