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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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.
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.
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.
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.
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.
May 10, 2019
This Ten Mistakes to Avoid focuses on helping organizations sidestep QA problems that many DW projects experienced.
March 8, 2019
This Ten Mistakes to Avoid focuses on key issues facing organizations as they determine strategies for generating value from IoT data.
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.
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.
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.