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Ten Mistakes to Avoid

The Ten Mistakes to Avoid series, published quarterly, addresses the 10 most common mistakes managers and teams make—from data modeling to building an operational data store—and gives you inside knowledge on how to avoid these common pitfalls. Ten Mistakes to Avoid is exclusively for TDWI Premium Members.

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Ten Mistakes to Avoid in Data Storytelling
TDWI Member Exclusive

May 5, 2016

 

By Dave Wells

Data storytelling takes the next step beyond data visualization by connecting multiple visuals with narrative, offering interpretation, and inviting conversation. Crafting a good data story, however, is challenging. This Ten Mistakes to Avoid offers guidance to advance your storytelling skills and increase the impact of your data stories.


Ten Mistakes to Avoid In Preparing for IoT Analytics
TDWI Member Exclusive

March 7, 2016

 

By Fern Halper and Asim Razvi

The Internet of Things (IoT)—a network of connected devices that can send and receive data over the Internet—is a hot market topic. Although many companies are excited about the possibility of improving operational efficiencies and driving new business models with IoT, this Ten Mistakes to Avoid identifies important factors to consider when getting ready for IoT analytics projects.


Ten Mistakes to Avoid When Deploying Visual BI and Analytics for Business Decisions
TDWI Member Exclusive

November 9, 2015

 

By David Stodder

Data visualization, data discovery, and analytics technologies are rapidly maturing and are becoming easier to use. However, success with these technologies is not as simple as just turning them on. This Ten Mistakes to Avoid identifies important factors in the success or failure of visual BI and analytics deployment and recommends practices for addressing them.


Ten Mistakes to Avoid In Predictive Analytics Efforts
TDWI Member Exclusive

August 5, 2015

 

By Fern Halper

TDWI research indicates that predictive analytics is one of the most popular kinds of advanced analytics. Although many companies are excited about the possibility of utilizing predictive analytics, there are a number of interrelated themes about what not to do when it comes to predictive analytics projects.


Ten Mistakes to Avoid When Growing Your MDM Program
TDWI Member Exclusive

May 11, 2015

 

By Philip Russom

You need a long-term strategy, documented in a governable plan, if MDM is to mature into rich functionality applied over much of your enterprise. This TDWI Ten Mistakes to Avoid booklet drills into the details of such plans.


Ten Mistakes to Avoid in Hadoop Implementations
TDWI Member Exclusive

February 18, 2015

 

By Krish Krishnan

In this Ten Mistakes to Avoid we identify the mistakes with the most negative impact on Hadoop implementations and recommend solutions you can apply to your own environment.


Ten Mistakes to Avoid When Democratizing BI and Analytics
TDWI Member Exclusive

November 10, 2014

 

By David Stodder

This Ten Mistakes to Avoid focuses on helping organizations ensure satisfaction as they democratize BI and analytics. It recommends a balanced approach for meeting user needs while addressing the necessities of data governance and management.


Ten Mistakes to Avoid When Building a Sustainable Agile BI Practice

Ten Mistakes to Avoid When Building a Sustainable Agile BI Practice
TDWI Member Exclusive

August 18, 2014

 

By Mark Giesbrecht

Presented in this issue are the 10 mistakes to avoid as you take your agile BI practice to the next level.


Ten Mistakes to Avoid in Big Data Analytics Projects

Ten Mistakes to Avoid in Big Data Analytics Projects
TDWI Member Exclusive

May 12, 2014

 

By Fern Halper

This Ten Mistakes to Avoid covers what works and doesn’t work when it comes to big data analytics projects.


Ten Mistakes to Avoid When Bridging the Business/IT Divide

Ten Mistakes to Avoid When Bridging the Business/IT Divide
TDWI Member Exclusive

February 18, 2014

 

By Lyndsay Wise

This Ten Mistakes to Avoid addresses how to overcome communication gaps and use successful collaboration to help ensure successful BI implementations.


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