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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.
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.
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.
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