This TDWI Checklist Report discusses the ways in which design thinking can produce more effective BI and analytics solutions and reduce user frustration with ineffective tools.
December 22, 2017
This TDWI Best Practices Report examines how organizations become data-driven, including patterns for building out infrastructure for managing data and driving analytics. It also examines the best practices of those organizations that are data-driven across three areas we believe are important: technology, analytics, and organization.
December 22, 2017
As the importance of self-service solutions for BI, analytics, and data preparation continues to grow, the emphasis is no longer only on centralized, full-time data professionals and their institutional knowledge. We also must find a way to support the business’ needs with better-documented data assets. For many organizations, this is a data catalog.
December 8, 2017
This Checklist Report focuses on helping organizations understand key considerations and best practices for utilizing cloud services for big data.
Geospatial information can be extremely helpful in a variety of analytics ranging from marketing to operations management. This checklist introduces readers to the range of use cases where geospatial analytics is being used today to support analysis.
For organizations that need a modern data warehouse that satisfies new and future requirements, we offer a checklist of our top six recommendations. These can guide your selection of vendor products and your solution design.
This checklist of best practices can help users make sustainable decisions as they plan their first Hadoop deployments.
This Best Practices Report examines how organizations are leveraging their big data assets, the challenges they face, future trends in user practices and vendor tools, and 10 priorities for the years ahead.
This TDWI Checklist Report takes a look at text analytics and how to get started with this new technology that can help you improve and gain new insight.
This TDWI Checklist Report discusses how organizations can achieve greater agility with data quality projects through adjustments to data stewardship, business processes, and technical development methods. The report also looks at critical success factors for agile
data quality, such as tool features, team structures, self service, data-driven documentation, and data services.
Individual, Student, & Team memberships available.