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.
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.
September 28, 2018
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.
September 17, 2018
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.
July 25, 2018
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.
June 29, 2018
A number of newly mature trends are making cloud-based data integration platforms, technologies, and user best practices more relevant than ever.
June 12, 2018
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.
May 18, 2018
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.
May 8, 2018
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.
March 30, 2018
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.