Intelligent Enterprise Contributing Editor: THE KIMBALL GROUP The Kimball Group is the definitive source for education and consulting on the Kimball dimensional data warehousing and business intelligence (DW/BI) methods. Each Kimball Group member has focused on DW/BI for a minimum of 15 years. They collectively authored The Data Warehouse Lifecycle Toolkit, 2nd Edition (Wiley 2008). Visit www.kimballgroup.com to learn more about the Kimball Group and Kimball University.
Ralph Kimball
Founder of the Kimball Group, Ralph has been the DW/BI industry's thought leader on the dimensional approach since the mid 1980s. He has trained more than 10,000 IT professionals and sold more than 250,000 copies of his Toolkit books. ralph@kimballgroup.com
Warren Thornthwaite
Warren began his DW/BI career in 1980. He teaches DW/BI project lifecycle and Microsoft data warehousing classes for Kimball University, and co-authored The Microsoft Data Warehouse Toolkit (Wiley 2006). warren@kimballgroup.com . 
Joy Mundy
Joy has worked in the DW/BI industry since 1992, including a stint at Microsoft's SQL Server product development organization. She co-authored The Microsoft Data Warehouse Toolkit (Wiley 2006). joy@kimballgroup.com |
Margy Ross
Margy is President of the Kimball Group and teaches dimensional modeling and DW/BI project lifecycle classes through Kimball University. She co-authored The Data Warehouse Toolkit, 2nd Edition (Wiley 2002). margy@kimballgroup.com
Bob Becker
Bob has focused on DW/BI solutions since 1989, including extensive work with health care industry clients recently. He co-teaches Kimball University's ETL Architecture class. bob@kimballgroup.com |
ARTICLES BY THE KIMBALL GROUP
RECENT ARTICLES
Kimball University: Six Key Decisions for ETL Architectures
Kimball University: Five Alternatives for Better Employee Dimension Modeling
Kimball University: The 10 Essential Rules of Dimensional Modeling
Kimball University: A Data Warehousing Fitness Program for Lean Times
Kimball University: Using Regular Expressions for Data Cleansing and Standardization
Kimball University: Practical Steps for Designing a Dimensional Model
Keep to the Grain in Dimensional Modeling
Kimball University: Eight Recommendations for International Data Quality
Kimball University: Better Business Skills for BI and Data Warehouse Professionals
Kimball University: Microsoft SQL Server Comes of Age for Data Warehousing
DIMENSIONAL MODELING/DATA ARCHITECTURE
Kimball University: Five Alternatives for Better Employee Dimension Modeling
The employee dimension presents one of the trickier challenges in data warehouse modeling. These five approaches ease the complication of designing and maintaining a 'Reports To' hierarchy for ever-changing reporting relationships and organizational structures.
Kimball University: Practical Steps for Designing a Dimensional Model
What does it take to develop a robust dimensional model? Here's how to get from requirements-gathering to final approval in a process that will ferret out the good, bad and ugly realities of your source data and help you avoid surprises, delays and cost overruns.
Keep to the Grain in Dimensional Modeling
When developing fact tables, aggregated data is NOT the place to start. To avoid "mixed granularity" woes including bad and overlapping data, stick to rich, expressive, atomic-level data that's closely connected to the original source and collection process.
Think Critically When Applying Best Practices
Best practices are precision tools that should be wielded precisely and skillfully. This article describes five best practices drawn from the Kimball Method that often are described incorrectly.
Pick the Right Approach to MDM
It's time to migrate master data management upstream to an integration hub or, ideally, an enterprise MDM system. And if you have yet to do anything about data consistency, take these four steps toward integration and stewardship.
ETL/DATA QUALITY
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.
Kimball University: Should You Use An ETL Tool?
You can still hand-code an extract, transform and load system, but in most cases the self-documentation, structured development path and extensibility of an ETL tool is well worth the cost. Here's a close look at the pros and cons of buying rather than building..
Subsystems of ETL Revisited
These 34 subsystems cover the crucial extract, transform and load architecture components required in almost every dimensional data warehouse environment. Understanding the breadth of requirements is the first step to putting an effective architecture in place.
Data Warehouse Dining Experience
Managing a data warehouse is similar to running a restaurant.
Surrounding the ETL Requirements
Before designing an ETL system, you must first understand all of your business needs.
BI/ANALYTICS
Building a Foundation for Smart Applications
Off-the-shelf apps may offer built-in analytics, but the best approach to supporting operational decisions is to rely on a solid data warehouse that cleans, integrates.
Building and Delivering BI Reports
Here's how to build, test and deploy standard reports for key business processes.
Standard Reports: Basics for Business Users
How to plan, prioritize and design the primary vehicle for delivering business intelligence.
PROJECT LIFECYCLE
Kimball University: Better Business Skills for BI and Data Warehouse Professionals
To deliver better intelligence, BI and data warehousing teams need business acumen, interpersonal skills and communication competencies. Here are helpful tips and 12 invaluable resources for career development and success.
Educate Management to Sustain DW/BI Success
Data warehousing and business intelligence success cannot be taken for granted. You must create an ongoing education and communication program to maintain your success and extend it across the organization.
Overcoming Obstacles when Gathering Requirements
How do you cope with "abused users, overbooked users, comatose users, clueless users" and "know-it-all users" during the requirements-gathering stage of a data warehouse/BI project? Kimball group offers its advice for proactively working with (or around) the uncooperative, unavailable, uninsightful and irrepressible types who sometimes make it hard to know just what the business needs.
Think Critically when Applying Best Practices
Best practices are precision tools that should be wielded precisely and skillfully. This article describes five best practices drawn from the Kimball Method that often are described incorrectly.
RELATED TECHNOLOGIES
Kimball University: Microsoft SQL Server Comes of Age for Data Warehousing
With new compression, partitioning and star schema optimization features, Microsoft's SQL Server 2008 is catching up with the state of the industry in data warehousing. Here's why these three capabilities are crucial for scalability and performance on any platform.
Dimensional Relational vs. OLAP
The choice between deploying relational tables or OLAP cubes is not a trivial matter. Weigh these 34 pros and cons of each approach early in the design of your extract-transform-load system.
Pick the Right Approach to MDM
It's time to migrate master data management upstream to an integration hub or, ideally, an enterprise MDM system. And if you have yet to do anything about data consistency, take these four steps toward integration and stewardship.
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KIMBALL UNIVERSITY
Feb 23-26, 2010
Kimball University Dimensional Modeling In Depth
Dallas
March 16-19, 2010
Kimball University Data Warehouse Lifecycle In Depth
Sydney, Australia
April 6-9, 2010
Kimball University Microsoft Data Warehousing In Depth
Seattle, WA
April 13-16, 2010
Kimball University Data Warehouse Lifecycle In Depth
White Plains, NY
May 4-7, 2010
Kimball University Dimensional Modeling In Depth
Washington DC
May 10-13, 2010
Kimball University ETL Architecture In Depth
Washington DC
May 17-20, 2010
Kimball University ETL Architecture In Depth
Amsterdam
May 18-21, 2010
Kimball University Dimensional Modeling In Depth
London
July 6-9, 2010
Kimball University Dimensional Modeling In Depth
San Francisco
July 13-16, 2010
Kimball University Microsoft Data Warehousing In Depth
Boston
July 27-30, 2010
Kimball University Data Warehouse Lifecycle In Depth
Chicago
Sept 28-Oct 1, 2010
Kimball University Data Warehouse Lifecycle in Depth
Washington, DC
Sept 28-Oct 1, 2010
Kimball University ETL Architecture In Depth
San Jose, CA
KIMBALL BOOKS
 The Data Warehouse Lifecycle Toolkit: Tools and Techniques for Designing, Developing, and Deploying Data Warehouses.
This new second edition by Ralph Kimball, Margy Ross, Bob Becker, Warren Thornthwaite and Joy Mundy provides a detailed guide to the data warehouse project, with no-nonsense techniques from inception through deployment. A supporting Web site offers a comprehensive set of useful templates and project tools.
 The Microsoft Data Warehouse Toolkit: With SQL Server 2005 and the Microsoft Business Intelligence Toolset
Warren Thornthwaite and Joy Mundy co-authored this guide to building a successful business intelligence system and its underlying data warehouse databases using Microsoft SQL Server 2005. They provide invaluable advice about designing, developing, deploying, and operating your Kimball Method data warehouse system on the Microsoft BI platform.
 The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data
This book, written by Ralph Kimball and Joe Caserta, is a roadmap for planning, designing, building, and running the back room of a data warehouse. We expand the traditional ETL steps of extract, transform, and load into the more actionable steps of extract, clean, conform, and deliver, although we will resist the temptation to change ETL into ECCD. We build on a set of consistent techniques for delivery of dimensional data.
 The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd Edition
Ralph Kimball and Margy Ross co-authored the second edition of Ralph’s classic guide to dimensional modeling. It provides a complete collection of modeling techniques, beginning with fundamentals and gradually progressing through increasingly complex real-world case studies. With more than 60% new content, the book significantly enhances and expands upon the concepts and examples presented in the original Data Warehouse Toolkit.
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