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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.


IB CDO white paper cover

Four Actions for a Chief Data Officer's First Year

March 7, 2019

Download this white paper to learn four key ways a CDO can demonstrate value to company leaders, as well as prove to stakeholders that data is an asset worthy of time and money investments.


IB customer-facing BI WP cover

Customer-Facing BI and Analytics

March 7, 2019

Download this white paper to learn how customer-facing technologies help companies increase efficiency, reduce costs, boost customer loyalty, and even increase revenue.


IB IoT Analytics WP cover

IoT Analytics: From Data Integration to Actionable Intelligence

March 7, 2019

Download this white paper for an exploration of the challenges associated with implementing actionable Internet of Things analytics and the critical role integration plays in the process.


IB Real Cost of Bad Data WP cover

The Real Cost of Bad Data: Six Simple Steps to Address Data Quality Issues

March 7, 2019

Download this white paper for six simple yet highly effective steps for preventing and fixing bad data.


IB Dirty Data WP cover

Dirty Data, Broken Trust

March 7, 2019

Download this white paper for an examination of how losing trust in your data destroys economic value.


IB wp Five Things to Focus on

Five Things to Focus On For Faster MDM Results

March 7, 2019

Download this white paper to find out what every successful master data management initiative needs.


IB wp Busting 10 Myths

Busting 10 Myths About Data Quality Management

March 7, 2019

This white paper deconstructs 10 myths about data quality management and offers practical alternatives that can help your organization design and implement an effective data quality management strategy.


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