This TDWI Checklist Report discusses three broad areas of best practices for helping to make machine learning, and those involved with it, successful.
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.
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