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.
February 18, 2014
This Checklist Report focuses on helping organizations understand key considerations and best practices for utilizing cloud services for big data.
December 11, 2013
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.
November 21, 2013
This Checklist Report will help healthcare provider organizations develop strategies for employing BI, data discovery, and analytics tools and technologies.
November 18, 2013
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.
October 29, 2013
This checklist of best practices can help users make sustainable decisions as they plan their first Hadoop deployments.
September 17, 2013
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.
July 22, 2013
This TDWI Checklist Report drills into the many technologies and capabilities needed to make operational intelligence possible for a technology team and successful for a business.
July 22, 2013
This TDWI Checklist Report discusses adjustments to DW architectures that real-world organizations are making today, so that Hadoop can help the DW environment satisfy new business requirements for big data management and big data analytics.
June 17, 2013
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.