Data Warehouse

New data sources and BI delivery modes make it that much harder for EDW initiatives to succeed. Here are eight recommendations for controlling project costs and reducing risks.

via IntelligentEnterprise : Kimball University: Eight Guidelines for Low-Risk Enterprise Data Warehousing (printable version) .

Fact tables are the foundation of the data warehouse. They contain the fundamental measurements of the enterprise, and they are the ultimate target of most data warehouse queries. Perhaps you are wondering why it took me so long to get to fact tables in DM Review? Well, there is no point in hoisting fact tables up the flagpole unless they have been chosen to reflect urgent business priorities, have been carefully quality assured and are surrounded by dimensions that provide a wealth of entry points for constraining and grouping. Now that we have paved the way for fact tables, let’s see how to build them and use them.

via Fact Tables.

IntelligentEnterprise : Kimball University: Data Stewardship 101: First Step to Quality and Consistency

Data stewards are the liaisons between business users and the data warehouse team, and they ensure consistent, accurate, well-documented and timely insight on resources and requirements.

Fact tables are the foundation of the data warehouse. They contain the fundamental measurements of the enterprise, and they are the ultimate target of most data warehouse queries. Perhaps you are wondering why it took me so long to get to fact tables in DM Review? Well, there is no point in hoisting fact tables up the flagpole unless they have been chosen to reflect urgent business priorities, have been carefully quality assured and are surrounded by dimensions that provide a wealth of entry points for constraining and grouping. Now that we have paved the way for fact tables, let’s see how to build them and use them.

Fact Tables.

Dimensions implement the user interface UI in a business intelligence BI tool. In a dimensional data warehouse DW/BI system, the textual descriptors of all the data warehouse entities like customer, product, location and time reside in dimension tables. My two previous columns carefully described three major types of dimensions according to how the DW/BI system responds to their slowly changing characteristics. But why all this fuss about dimensions? They are the smallest tables in the data warehouse, and the real “meat” is actually the set of numeric measurements in the fact tables. But that argument misses the point that the DW/BI system is always accessed through the dimensions. The dimensions are the gatekeepers, the entry points, the labels, the groupings, the drill-down paths and, ultimately, the texture of the DW/BI system. The actual content of the dimensions determines what is shown on the screen of a BI tool and what UI gestures are possible. That is why we say that the dimensions implement the UI.

Judge Your BI Tool through Your Dimensions.

Dimensions are key to navigating the data warehouse / business intelligence system, and hierarchies are the key to navigating dimensions. Any time a business user talks about wanting to drill up, down or into the data, they are implicitly referring to a dimension hierarchy. In order for those drill paths to work properly, and for a large DW/BI system to perform well, those hierarchies must be correctly designed, cleaned, and maintained.

IntelligentEnterprise : Kimball University: Maintaining Dimension Hierarchies.

Behind every success or failure are people. People are the only differentiators. Every data warehousing DW and business intelligence BI project, whether successful or not, teaches us something. It is generally on failures that we base our new success. Having said that, it’s not always necessary that you fail to learn; you can also learn from other’s failures, 10 of which are discussed here.

10 Mistakes to Avoid in a Business Intelligence Delivery.

IntelligentEnterprise : Kimball University: Eight Recommendations for International Data Quality
Language, culture, and country-by-country compliance and privacy requirements are just a few of the tough data quality problems global organizations must solve. Start by addressing data accuracy at the source and adopting an MDM strategy, then follow these six other best-practice approaches.