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TDWI White Paper Library

TDWI maintains this library of white papers as a resource for in-depth research and commentary about the big data, business intelligence, data warehousing, and analytics industry. The content in this repository is crafted by TDWI's software and consulting partners. To find out how your company can promote its content in this library, click here.

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3 Shifts in the Modern Data Environment

July 11, 2019

This paper seeks to outline three major thought shifts concerning the modern data environment that Information Technology leaders need to understand in order to support data-driven decision making within their organizations.

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Evaluation Guide: How to Choose the Right Modern BI and Analytics Platform

July 11, 2019

This evaluation guide aims to support IT organizations as they evaluate and select a modern BI and analytics platform suitable for a broad, enterprisewide deployment.

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BI Evaluation Guide

July 1, 2019

Not all BI platforms are created equal—and to succeed in the digital era, you need an analytics solution that gives your business every possible route to transformation. In this guide, you’ll learn which capabilities to look for, which features to prioritize, and all the right questions to ask.

IBM white paper

Optimize Customer Experience Metrics with 12 AI-Enabled Use Cases

July 1, 2019

Research from Forrester shows 12 AI-enabled use cases to help you prioritize CX investments for increased business impact.

IBM white paper

A Business Guide to Modern Predictive Analytics

July 1, 2019

In business, foresight is everything. This guide will help you navigate the modern predictive analytics landscape.

IBM white paper

Technical Validation: Hybrid Multi-cloud Artificial Intelligence: IBM Watson Studio and Watson Machine Learning

July 1, 2019

ESG recently completed testing of IBM Watson Studio and Watson Machine Learning, which are designed to enable organizations to accelerate the value they can extract from AI more easily.

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Deep Dive: Data Warehouse Automation

June 28, 2019

The invention of data warehouse automation (DWA) as a technology category was inevitable. And it was invented in a real sense: the first DWA tools evolved to address gaps in the development, deployment, and maintenance of data warehouse systems. But just as the role of the data warehouse has changed, so, too, has that of the DWA tool sets that evolved to support it.

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