TDWI Best Practices Reports
TDWI’s Best Practices Reports are designed to educate technical and business professionals about new business intelligence technologies, concepts, or approaches that address a significant problem or issue. Research for the Best Practices Reports is conducted via interviews with industry experts and leading-edge user companies, and is supplemented by a survey of business intelligence professionals.
June 29, 2022
This TDWI Best Practices Report provides new research about how successful companies are organizing to support successful modern analytics.
March 25, 2022
This report illustrates how the unification of DataOps and MLOps platforms, workflows, and methodologies is dovetailing with enterprise data modernization initiatives.
November 17, 2021
This TDWI Best Practices Report offers recommendations that highlight priorities for modernizing data and information integration for business advantage.
September 10, 2021
This TDWI Best Practices Report examines the adoption, use, challenges, architectures, and best practices for unified platforms for modern analytics.
May 28, 2021
This TDWI Best Practices Report examines the convergence of the data warehouse and data lake, including drivers, challenges, and opportunities for the unified DW/DL and best practices for moving forward.
December 18, 2020
This report canvasses current and future data governance strategies and best practices to help organizations understand the new requirements for data governance.
September 25, 2020
This TDWI Best Practices Report offers recommendations for ensuring that the evolution toward modern business analytics is successful.
May 29, 2020
This TDWI Best Practices Report explores data management strategies and best practices, then links combinations of these to the leading forms of advanced analytics to help data management and advanced analytics professionals and their business counterparts achieve greater success and business impact.
December 20, 2019
This TDWI Best Practices Report examines where organizations are facing barriers to getting relevant data into the right condition for analytics, for developing artificial intelligence (AI) programs, and for delivery to users who need insights in time to solve business problems.