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
The gender gap in wages saw a dramatic narrowing in 2015, as women’s average salary rose by 1 percent and men’s dropped by 4 percent. Read more in the 2016 TDWI Salary, Roles, and Responsibilities Report.
Download this report for recommendations and best practices for successfully operationalizing analytics in your organization to derive business value.
This Checklist discusses seven best practices for implementing data warehouse automation within an agile development framework.
This Checklist discusses seven key considerations to help organizations focus their evaluation and develop a strategy for gaining value from open source technologies to support faster, more powerful, and more flexible analytics.
This TDWI Checklist Report drills into seven recommendations and discusses many of the new vendor product types, their functionality, and user best practices that can contribute to data integration modernization. It also presents the business case and technology strengths of each recommendation.
To help the reader understand the ongoing evolution of data management—in response to new requirements around big data and analytics—this report provides a checklist of data management practice areas that are most affected. In addition, the report relates new practices to existing ones, indicating when to use which one.
This Checklist Report discusses what your enterprise should consider before diving into a data lake project, no matter if it’s your first or second or even third major data lake project. Presumably, adherence to these principles will become second nature to the data lake team and they will even improve upon them at some point.
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