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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 Q4 2020

Ten Mistakes to Avoid in BI Projects
TDWI Member Exclusive

October 19, 2020

Why, in a world where data skills have never been in greater demand, do so many businesses fail to capitalize on data's potential?


10 mistakes to avoid when querying data lakes

Ten Mistakes to Avoid When Querying Your Data Lake
TDWI Member Exclusive

August 14, 2020

We will explain the ten mistakes to avoid when querying data lakes, focusing on effective best practices for keeping data in data lake storage and querying it directly, thereby raising productivity and efficiency and lowering costs and complexity.


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Ten Mistakes to Avoid in Operationalizing Machine Learning
TDWI Member Exclusive

May 12, 2020

Although many companies are excited about machine learning, they often overlook some key success factors, especially when it comes to deploying and operationalizing ML models into production.


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Ten Mistakes to Avoid in Analytics Initiatives
TDWI Member Exclusive

January 17, 2020

Business intelligence (BI) has long been a top CIO priority driving technology investment for organizations. Although it is good to see the continued focus on the data and analytics domains, it is also quite disturbing that this seems to imply many of our past investments may not be hitting the mark in terms of success. To help you map out your strategy, we present 10 fatal mistakes you should avoid at all costs based on our experience.


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Ten Mistakes to Avoid When Modernizing Your Business Intelligence Programs
TDWI Member Exclusive

October 21, 2019

As you embark on your BI modernization effort, you would do well to learn from companies that have successfully completed their own BI modernization projects—and even from companies that have failed in that regard. If you can avoid their pitfalls, you can ensure a successful BI modernization initiative.


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Ten Mistakes to Avoid In Migrating Data and Analytics Platforms to Cloud Infrastructure
TDWI Member Exclusive

September 13, 2019

This Ten Mistakes to Avoid focuses on helping organizations make the transition from on-premises data and analytics platforms to cloud-based deployments more efficiently and thoughtfully.


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Ten Mistakes to Avoid in Test-Driven Data Warehouse Development
TDWI Member Exclusive

May 10, 2019

This Ten Mistakes to Avoid focuses on helping organizations sidestep QA problems that many DW projects experienced.


Ten Mistakes to Avoid

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

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
TDWI Member Exclusive

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
TDWI Member Exclusive

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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