This report explains in detail what cloud data management (CDM) is and does so data professionals and their business counterparts can understand what CDM can do for them and how they might organize a successful program.
May 24, 2019
This TDWI Checklist Report provides suggestions when considering modernizing your data warehouse in the cloud.
March 29, 2019
This checklist focuses on how IoT data and analytics can be used to help drive the customer experience.
January 3, 2019
This TDWI Checklist Report presents seven recommendations for successful data hub design and use. It should help you understand the new direction that the data hub has taken as well as what you should demand when evaluating products and deploying a modern data hub.
This Checklist Report discusses six areas that are critical to achieving high-value, business-driven analytics and the role data virtualization plays in realizing success in these areas.
This TDWI Pulse Report discusses some best practices for developing an IoT data strategy. It examines the organizational as well as the data and analytics aspects of such a strategy. This includes organizational alignment, understanding the unique nature of IoT, and other issues at play when managing and analyzing this “new” kind of data.
A number of newly mature trends are making cloud-based data integration platforms, technologies, and user best practices more relevant than ever.
It can be difficult to create an organization that thrives on data and analytics. This TDWI Checklist Report discusses best practices to build a program and an infrastructure for becoming data-driven.
Organizations dependent on big data for a wide range of business decisions need data quality management that can improve the data so it is fit for each desired purpose. This TDWI Checklist Report offers six strategies for improving big data quality.
Machine learning is being used today to solve well-bounded tasks such as classification and clustering. Note that a machine learning algorithm learns from so-called training data during development; it also learns continuously from real-world data during deployment so the algorithm can improve its model with experience. This report will drill into the data, tool, and platform requirements for machine learning with a focus on automating and optimizing ML's development environment, production systems, voracious appetite for data, and actionable output.
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