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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

Ten Mistakes to Avoid for Better BI and Analytics with IoT Data

March 8, 2019

This Ten Mistakes to Avoid focuses on key issues facing organizations as they determine strategies for generating value from IoT data.


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Ten Mistakes to Avoid with Data Lakes

October 2, 2018

The data lake came seemingly out of nowhere in 2016 and quickly became a common approach to capturing, managing, and presenting extremely large quantities of highly diverse data. Today, data lakes are in production in several data-driven business use cases, including modern data warehouse environments, analytics programs, omnichannel marketing data ecosystems, and digital supply chains. Though data lakes are still quite new, TDWI has seen enough implementations to know what works and what doesn’t. And The mistakes of data lakes are mostly about mindset.


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Ten Mistakes to Avoid When Implementing a Data Quality Strategy

July 13, 2018

 

By Patty Haines

Data quality is essential to getting more value from your organization’s data assets. Analysts, data scientists, and managers must know and understand the quality of the data they are using to make decisions and to set direction for their organizations if they are to make the best decisions.


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Ten Mistakes to Avoid When Operationalizing Data Governance

April 6, 2018

 

By David Loshin

Despite the reams of material describing how to develop a data governance program, many continue to struggle with implementing a sustainable program that measurably meets the business objectives. Vacuous data policies, ill-defined roles and responsibilities, missing procedures for implementation, and an excessive fascination with tools all impede real progress in fully internalizing a data governance program.


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Ten Mistakes to Avoid In an Agile BI Transformation

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.


Ten Mistakes to Avoid When Adopting Advanced Analytics

Ten Mistakes to Avoid When Adopting Advanced Analytics

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.


Q3 2017 Ten Mistakes to Avoid in Smart Cities, 1.1

Ten Mistakes to Avoid in Smart Cities, 1.1

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.


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Ten Mistakes to Avoid in Data Maturity and Modernization

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.


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Ten Mistakes to Avoid in Building Out a Data Science Program

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.


Ten Mistakes to Avoid When Defining an Enterprise BI and Analytics Strategy

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.


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