Advanced Search
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 Read Now
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
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 Read Now
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.
Users ignore the modernization of deep warehouse infrastructure at their peril. Without it, they may achieve complete, clean, and beautifully modeled data, but without the ability to scale to big data, iterate data models on the fly, enable flexible self-service access, operate continuously and in real-time (as warehouses must in global businesses), and handle new data types and workflows for advanced analytics.
As organizations collect and analyze increasing amounts of data, they are turning to the data lake as the platform to perform more advanced analytics such as machine learning. This TDWI Checklist Report presents best practices for advanced analytics on a data lake.
Individual, Student, & Team memberships available.
Join Today