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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January 24, 2018
By Lynn Winterboer
With so many teams “going agile,” it’s important for your BI team to keep a few things in mind that will help your agile transformation go more smoothly.
These 10 mistakes to avoid are the primary problems I’ve seen in BI agile transformations. Fortunately, agile approaches provide solutions that prevent you from making these mistakes.
November 29, 2017
By Troy Hiltbrand
Advanced analytics is consistently ranked as a high-priority focus of CIOs across the industry. With applications from cybersecurity to financial management to human resources to sales and marketing, almost all sectors regard advanced analytics as a mechanism to solve their most challenging business problems.
This publication explains 10 mistakes to avoid when adopting advanced analytics.
August 16, 2017
By Ted Cuzzillo
The "smart city" buzz is loud, but smart cities themselves are still young. In this Ten Mistakes to Avoid, Ted Cuzzillo identifies the most common mistakes vendors, leaders, and administrators are making as they create and manage smart cities.
April 21, 2017
By William McKnight
Companies everywhere are realizing that data is a key asset that can directly impact business goals. Yet, in some enterprises, awareness of data’s value doesn’t translate into increased data maturity and modernization. In this Ten Mistakes to Avoid, William McKnight identifies the misguided practices that cause the most friction in modernization efforts and the journey to higher data maturity.
January 31, 2017
By Fern Halper and Martin Pacino
Organizations are moving past traditional structured data and are embracing text data from emails, machine-generated data, and streaming data to enhance their analyses. Data science is an outgrowth of this of this evolution, but many companies are still in the early stages of their data science efforts. In this Ten Mistakes to Avoid, TDWI’s Fern Halper and Martin Pacino identify the mistakes most detrimental to building out a successful data science program.
November 3, 2016
By Nancy Williams and Steve Williams
The benefits of business intelligence—and now big data analytics—may be couched in different business terms, but there has to be a connection to increased profits if there is to be a return on a BI investment. This Ten Mistakes to Avoid examines some of the key, commonly encountered BI strategy challenges faced by major companies across a wide range of industries.
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.
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.
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.
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.