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
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
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
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.
A number of newly mature trends are making cloud-based data integration platforms, technologies, and user best practices more relevant than ever.
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.
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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