By Fern Halper
This Ten Mistakes to Avoid covers what works and doesn’t work when it comes to big data analytics projects.
By Lyndsay Wise
This Ten Mistakes to Avoid addresses how to overcome communication gaps and use successful collaboration to help ensure successful BI implementations.
By Mark Madsen
Data strategy focuses on how data can be used as a resource to further the goals of a business strategy. This means building capabilities: treating data as an asset, organizing to make better use of it, and building the necessary management and technology infrastructure. There are many ways to build capabilities. Choices impose constraints and trade-offs, which are the essence of crafting a set of policies, procedures, and plans that make up a data strategy. This Ten Mistakes to Avoid focuses on many common mistakes we make when crafting a data strategy.
By Laura Reeves
The most successful BI solutions are those whose design and subsequent use are driven by the business itself. This is much easier said than done. Too often, what is delivered is not well received by the business community, or worse, met with disappointment or resistance. The most common mistakes, and tips for avoiding them, are explained here.
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.
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