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.
By Ralph Kimball—August 1, 2008
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.
By Warren Thornthwaite—June 23, 2008
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.
By Warren Thornthwaite—May 11, 2008
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.
By Joy Mundy—April 06, 2008
What satisfies, or doesn't satisfy, the customer? Use one of these three powerful data warehouse design approaches to gauge satisfaction and help marketers tease out the customer experience behind various behaviors.
By Ralph Kimball—February 4, 2008
How do you cope with an executive's request to "bring back a time series of activity for all subscribers who were in platinum status as of X date," or "show me a time series of orders by sales region according to the sales organization as of Y"? Here's how data warehouse pros can cope with the common requirement to look back in time.
By Joy Mundy—December 9, 2007
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.
by Bob Becker—October 21, 2007
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.
by Warren Thornthwaite—August 27, 2007
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.
by Ralph Kimball—July 30, 2007
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?
by Margy Ross—June 1, 2007
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.
by Ralph Kimball—April 27, 2007
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.
by Bob Becker and Ralph Kimball—March 26, 2007
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.
by Warren Thornthwaite—February 7, 2007
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.
by Joy Mundy—December 1, 2006
DW/BI professionals are often tasked with making evolutionary upgrades and improvements to minimize cost and upheaval in the current analytic environment. We explore four upgrades that can breathe new life into legacy data warehouses.
by Margy Ross—October 1, 2006
These step-by-step guidelines will help dimension managers and users drill across disparate databases.
by Ralph Kimball—August 1, 2006
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.
by Bob Becker—June 1, 2006
How to build, test and deploy standard reports for key business processes.
by Warren Thornthwaite—April 1, 2006
How to plan, prioritize and design the primary vehicle for delivering business intelligence.
by Warren Thornthwaite & Joy Mundy—February 1, 2006
This Swiss Army knife for BI and data warehousing supports planning, integration and stewardship
by Margy Ross—December 1, 2005
Are you missing potential business opportunities because you're not exploring your data?
by Warren Thornthwaite—October 1, 2005
Is that sales pitch flying in the face of conventional wisdom? Start asking questions now.
by Ralph Kimball—September 1, 2005
Be sure to develop a DW/BI operations plan before deployment
by Joy Mundy—July 1, 2005
Good listening and conversational skills will uncover hidden needs and 'shadow functions.'
by Margy Ross and Ralph Kimball—May 1, 2005
The three fundamental techniques for changing dimension attributes are just the beginning
by Margy Ross and Ralph Kimball—March 1, 2005
Three little letters—E,T, and L—obscure the reality of 38 subsystems vital to successful data warehousing.
by Ralph Kimball—December 4, 2004
Before designing an ETL system, you must first understand all of your business needs.
By Margy Ross, Ralph Kimball— November 13, 2004
Do you know the difference between dimensional modeling truth and fiction?
By Margy Ross & Ralph Kimball—October 16, 2004
Techniques for realigning your DW/BI environment to deliver better business value.
By Bob Becker, edited by Margy Ross—August 7, 2004
By Ralph Kimball & Margy Ross—June 12, 2004
Successful warehouses grow—get ready for the opportunities and obstacles
By Joy Mundy, edited by Margy Ross—May 1, 2004
Reaching past the dashboard hype for some clarity
by Stephen Few, edited by Margy Ross—March 20, 2004
Fundamental differences between the Bus Architecture and Corporate Information Factory
by Margy Ross & Ralph Kimball—March 6, 2004
Managing a data warehouse is similar to running a restaurant
by Margy Ross & Ralph Kimball—January 1, 2004
As with construction, you must plan before building the ETL.
by Warren Thornthwaite, Edited by Margy Ross—December 10, 2003
Use the five-stage analytic framework to deliver more from the data warehouse
by Bill Schmarzo, Edited By Margy Ross—November 18, 2003
Although there's no substitute for atomic details, look into complementary consolidations
by Margy Ross & Ralph Kimball—October 30, 2003
Use this checklist to review your dimensional models
by Margy Ross & Ralph Kimball—October 10, 2003
Our data-warehousing approach is sometimes referred to as bottom-up, but it's far from it
by Margy Ross & Ralph Kimball—September 17, 2003
Business sponsors can make or break a data warehouse program. What habits make or break a business sponsor?
by Margy Ross edited by Ralph Kimball—September 1, 2003
Dimensional design techniques bind events into stories
by Jim Stagnitto edited by Ralph Kimball—August 10, 2003
RFID tagging will create not just a tidal wave of data, but lifetime employment for data warehouse designers
by Ralph Kimball—July 18, 2003
Start by discarding your current concepts of ETL
by Neil Raden edited by Ralph Kimball—June 30, 2003
Take the idea of a real-time data warehouse with a grain of salt, then realize the possibilities
by Neil Raden edited by Ralph Kimball—June 17, 2003
Absolutely yes, or absolutely no, depending...
by Gary Nissen edited by Ralph Kimball—May 31, 2003
The conventional view of data warehouse total cost of ownership is myopic and wrong
by Ralph Kimball—May 13, 2003
The data warehouse takes a pledge to preserve history
by Ralph Kimball—April 22, 2003
Drilling across means asking for the same row headers from another fact table
by Ralph Kimball—April 5, 2003
Drilling down just means 'show me more detail'
by Ralph Kimball—March 20, 2003
It's the most important dimensional design step after identifying data sources
by Ralph Kimball—March 1, 2003
Contrary to conventional data warehouse wisdom, physical centralization is not the question
by Margy Ross—February 1, 2003
The logical foundation of dimensional modeling
by Ralph Kimball—January 1, 2003
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