Big Data is growing 40% in both volume and complexity each year. Organizations are eager to leverage this data to pursue new business, automate and streamline processes, and reduce risk. But no data analytics program will succeed if it does not prioritize data quality. Yet, in the U.S. alone, poor data quality costs organizations roughly $3.1 trillion a year.
Instead of developing a strategy to improve and maintain data quality, many organizations rely solely on patches or short-term fixes. But without a long-term plan for improving data quality, it will continue to plague your business, which can lead to lost revenue, increased operational costs, regulatory penalties, and more.
In this guide, our experts dissect the root causes of poor data quality and walk through a practical approach to improving data quality at your organization.
Within this white paper, we look at:
- The financial, operational, regulatory, and executive-level effects of poor data quality
- The root causes of data quality issues – from poor enterprise data management design to shadow IT
- A step-by-step approach to improving and maintaining data quality
- A real-world case study demonstrating how data quality can transform a business
To learn more about how you can achieve and maintain high data quality, complete the short form to the right, and your guide will download immediately.
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