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RESEARCH & RESOURCES

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.


IDC FutureScape: Worldwide Big Data and Analytics 2016 Predictions

January 1, 2017

This IDC study offers IDC analysts' collective advice to IT and business decision makers to consider in their planning for big data and analytics (BDA) initiatives.


Three Ways to Get Started with Hadoop

January 1, 2017

Dell and Intel share three solutions that provide end-to-end scalable infrastructure, leveraging open source technologies, to allow you to simultaneously store and process large datasets in a distributed environment for data mining and analysis, on both structured and unstructured data, and to do it all in an affordable manner.


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7 Data Visualizations You're Not Using Enough

January 1, 2017

Download this white paper and discover how to tell better stories with data.


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Big Data Analytics Buyer’s Guide

January 1, 2017

If you have large volumes of diverse data and need to perform sophisticated analysis on this data, you need big data analytics.


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Using Customer Behavior Analytics to Increase Revenue

January 1, 2017

Customer behavior analytics maximizes the value of customer relationships by identifying actionable insights that drive valuable outcomes.


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Getting Value from Hadoop: Build, Bridge, or Buy

January 1, 2017

Hadoop plays an important role in the big data architecture in many organizations. Big data analytics platforms provide a faster path to prove business value and eliminate risk that can upend your big data initiatives.


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Data Preparation for Modern BI: Common Design Patterns

November 16, 2016

If you want to be successful in mining big data for insights that will move the needle for your business, then the data you’re analyzing needs to be clean, organized, detailed, and well-understood.


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