Analyst insights
Build a business case for continuous data quality
In this report you’ll learn how:
- Organizations must align their business objectives to their data quality program in order to be successful.
- Data quality cannot be considered a one-off IT program, but rather an integral part of an organization's digital transformation.
Read this Gartner report to learn more about the challenges of poor data quality and how to overcome those roadblocks to get the most out of your data investments.
*Gartner, 5 Steps to Build a Business Case for Continuous Data Quality Assurance, Saul Judah, Alan D. Duncan, Melody Chien, Ted Friedman, April 20 2020. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved.
Summary
Data quality is crucial as organizations accelerate their digital business efforts, but poor data quality is preventing organizations from becoming truly data-driven. With poor data quality, data and analytics leaders struggle to show the business value of their data investments. Gartner outlines the five-step approach for creating a business case for data quality improvement.
Preview
Poor data quality destroys business value. Recent research shows organizations estimate the average cost of poor data quality at $10.8 million per annum.1 This number is likely to rise as business environments become increasingly digitalized and complex.
Figure 1 shows that managing data quality issues across the organizational landscape is increasingly cited as a top challenge (by 60% of respondents) to data management practice (see "Survey Analysis: Data Management Struggles to Balance Innovation and Control"). Organizations with multiple business units (BUs) operating in several geographic regions with many customers, employees, suppliers and products will inevitably face more severe data quality issues.
Low levels of data literacy and silo-oriented attitudes, prevalent among senior business leaders, often result in a lack of investment in systemic and sustainable data quality improvement. As a consequence, key business goals, such as financial performance and customer experience, are adversely impacted.