Market Research Report
Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach
|Published by||Info-Tech Research Group||Product code||603327|
|Published||Content info||130 Pages
Delivery time: 1-2 business days
|Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach|
|Published: April 21, 2017||Content info: 130 Pages||
Get ahead of the data curve by conquering data quality challenges.
Regardless of the driving business strategy or focus, organizations are turning to data to leverage key insights and help improve the organization's ability to realize its vision, key goals, and objectives.
Poor quality data, however, can negatively affect time-to-insight and can undermine an organization's customer experience efforts, product or service innovation, operational efficiency, or risk and compliance management. If you are looking to draw insights from your data for decision making, the quality of those insights is only as good as the quality of the data feeding or fueling them.
Improving data quality means having a data quality management practice that is sustainably successful and appropriate to the use of the data, while evolving to keep pace with or get ahead of changing business and data landscapes. It is not a matter of fixing one data set at a time, which is resource and time intensive, but instead identifying where data quality consistently goes off the rails and creating a program to improve the data processes at the source.
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With the business demand for useful data and the rate of data proliferation showing no signs of slowing down, users are struggling with getting quality data to meet their business needs and to support timely decision making. Even when the data gets to users, they don't trust the data and complain about getting different answers while running the same report.
Our four-step, practical approach helps you to improve the organization's enterprise data quality practices while systematically addressing specific data quality improvement initiatives: