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TDWI Best Practices Report - Data Management for Advanced Analytics

November 5, 2020

Authored by Philip Russom

Modern enterprises are expanding their analytics programs to improve their ability to make fact-based decisions, plan for an uncertain future, compete on analytics, and grow customer accounts. These high-value business goals require advanced forms of analytics, which in turn demand use-case-appropriate data integration, data platforms, and other data management. Without the right data in the right format on the right platform, critical and expensive 
efforts in advanced analytics have little or no business value.

This report defines data management for advanced analytics (DM for AA), which tailors established and emerging data management best practices and techniques to specific forms of advanced analytics, thereby raising the precision, productivity, and business value of analytics.

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.

Previous Flipbook
TDWI Checklist: Data Management Requirements for Data Science, ML, and AI
TDWI Checklist: Data Management Requirements for Data Science, ML, and AI

Next Flipbook
BARC: The Data Management Survey 2020
BARC: The Data Management Survey 2020