Data is a critical foundation of modern business. Organizations rely on trustworthy data for diverse applications, including decision support, advanced analytics, machine learning, and artificial intelligence.
In TDWI’s research, data quality management is often at the top of the list of enterprise strategic priorities. Keeping data of high quality is fundamental to unlocking its value. Users require that this precious resource remain fit for every possible use, where fitness refers to its trustworthiness, accuracy, completeness, reliability, and relevance.
Steam this on-demand webinar to hear from TDWI and subject matter experts from Alteryx, Erwin/Quest, Precisely, and SAP as they discuss a variety of topics including:
How mature are enterprises’ data quality management practices?
What is the level of organizational commitment to data quality in today’s business world?
How should organizations staff the technical and business roles necessary to maintain robust data quality?
What are the most important metrics of data quality and how should organizations collect and report them to stakeholders?
What platforms, tools, and infrastructure should organizations implement in order to scale, automate, and govern data quality?
How should organizations incorporate data quality management discussions into their data literacy training programs?
How should organizations address responsibility concerns—such as data privacy, data ethics, and data bias mitigation—in their data quality management practices?
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Recommended Resources
Analyst Report
TDWI State of Data Quality Report
This report examines the current state of data quality management, the top challenges organizations face, and best practices for improving your data quality practices.
Improving Data Quality in the Age of Generative AI
Data fuels AI, shaping the future of your business. The quality, integrity and availability of that data are pivotal in determining the success of generative AI initiatives.
