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This first in a new series of reports offers focused research and analysis of trending analytics, business intelligence, and data management issues facing organizations. TDWI Pulse Reports are designed to educate technical and business professionals and aid them in developing strategies for improvement. March 29, 2018 Read Now
This TDWI Best Practices Report explores the new opportunities for AI, machine learning, and natural language processing presented by innovations in computing power and algorithmic efficiency. September 28, 2017 Read Now
One of the more popular subjects in data modernization today is the addition of data lakes to many different ecosystems. This report defines data lake types and discusses emerging best practices, enabling technologies, and real-world use cases. March 29, 2017 Read Now
In 2018, the average salary dropped for the first time since 2015, but nearly all individual employees saw their salaries rise from the previous year. Read more in the 2019 TDWI Salary, Roles, and Responsibilities Report.
In this checklist, we will examine best practices for data management that support ongoing compliance with a variety of regulations.
Any organization can become data-centric by taking advantage of some of the emerging trends discussed in this checklist. This report reviews some of the practices adopted by data-centric organizations and suggests ways to learn from their experiences.
This checklist focuses on how IoT data and analytics can be used to help drive the customer experience.
This TDWI Best Practices Report focuses on the emerging world of BI and analytics in the age of AI and big data. The research explores organizations’ satisfaction with current technologies and the issues driving interest in newer solutions that enable organizations to exploit advances in AI and big data.
This TDWI Checklist report drills down into the adjustments and optimizations in data quality practices required for big data.
This TDWI Checklist Report discusses three broad areas of best practices for helping to make machine learning, and those involved with it, successful.
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