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.
June 29, 2018
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
Businesses can only seize new data-driven opportunities if they recognize sensitive data and handle it responsibly. This report focuses on how targeted improvements to specific data management best practices and technology can contribute significantly to your success with GDPR compliance, as well as data governance and data-driven programs in general.
March 6, 2018
In this checklist, we explore the concept of hybrid transaction/analytical processing (HTAP), an alternative architecture that enables analytics to be performed in concert with transaction processing. We will present best practices for taking advantage of this alternative architecture to enable real-time analytics.
In addition to automation using AI, next-generation data catalogs often contain new features such as crowdsourcing and collaboration. This TDWI Checklist describes five ways modern data catalogs drive business value.
Existing enterprise infrastructures are engineered in a way that complicates some types of data provisioning. In this checklist, we will consider the benefits of a platform-based approach to DataOps that addresses some of these complexities.
This TDWI Checklist Report discusses best practices for data engineering and management to support machine learning with a focus on collecting, cleansing, transforming, and governing new types of data for analysis.
This TDWI checklist discusses six important issues that organizations should address to start big data projects off right and then manage them to achieve objectives faster and with less difficulty.
This TDWI checklist discusses six best practices for gaining greater value from AI for BI and self-service analytics. Our objective is to help organizations accomplish projects faster and provide relevant and accurate insights that users can trust.
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.
Individual, Student, & Team memberships available.