TDWI Checklist Reports
TDWI Checklist Reports provide an overview of success factors for a specific project in business intelligence, data warehousing, or a related data management discipline. Companies may use this overview to get organized before beginning a project or to identify goals and areas of improvement for current projects.
January 3, 2019
This checklist focuses on how IoT data and analytics can be used to help drive the customer experience.
December 20, 2018
This TDWI Checklist report drills down into the adjustments and optimizations in data quality practices required for big data.
December 14, 2018
This TDWI Checklist focuses on how organizations can develop strategies for improving operational intelligence, particularly by realizing value from IoT data.
December 14, 2018
This TDWI Checklist Report discusses three broad areas of best practices for helping to make machine learning, and those involved with it, successful.
December 10, 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.
December 7, 2018
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.
October 31, 2018
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.
October 26, 2018
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.
September 29, 2018
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.