By Ken Collier, Ph.D.
In my work with dozens of BI teams attempting to “go agile,” I’ve
seen lots of mistakes. This Ten Mistakes to Avoid outlines the
most common and recurring errors to help you avoid repeating the
mistakes of others.
By Krish Krishnan
In this Ten Mistakes to Avoid, we will look at the most common mistakes that occur when implementing a big data program to help you enhance your analytical insights and the decision support processes in your enterprise.
By Dave Wells
Data is an essential business resource that is as critical to business success as financial and human resources are. The disciplines of data resource management include strategy, architecture, and governance. Mistakes to avoid in data resource management span all three disciplines.
By John O'Brien
These 10 mistakes to avoid center around three key themes: identifying transformational or disruptive technologies, minimizing and managing risk to the overall program, and maintaining a continuing balance in business intelligence (BI) programs.
By Marc Demarest
Marc Demarest takes a look at the 10 most common missteps and fatal actions he’s seeing now as the big data revolution gets under way.
By Jonathan G. Geiger
Jonathan Geiger describes 10 common errors to avoid as you validate your current business intelligence or data warehousing direction.
By Christopher Adamson
To attain the full potential of your dimensional model, it is necessary to master a broad range of principles, understand how and what to model, and avoid lapsing into habits from other modeling disciplines. A dimensional model of your business is an important asset of your BI program. Maximize its value by avoiding these 10 mistakes.
By Shawn Rogers
The upsides to cloud business intelligence are easy to identify, but like any other solution, you need a strategy before you begin your research to identify the right solution for your company. Skipping the due-diligence phase with cloud solutions can be just as costly as with traditional on-premises solutions.
By Ralph Hughes
To identify where many agile BI/DW practitioners and managers have erred, we will examine the 10 items found in the Agile Manifesto and consider ways to rethink or rebalance each concept as a path to an even more effective second decade of accelerated BI/DW programs.
By Laura L. Reeves
Gathering requirements is the most critical step in any project, and certainly for a data warehouse project. A data warehouse should deliver an environment that empowers the business community rather than an application system that accurately performs specific business functions. This difference in approach will change the way you go about gathering requirements.
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