Why Good Data Goes Bad

Why Good Data Goes Bad

There is a disconnect between workers held accountable for data quality and those that are responsible for its capture and use.

98% of survey respondents expect the volume of data within their organization to increase in the coming year, and nearly half say it will increase by at least 50%.

Increased revenue: 51%, Reduced costs: 49%, Decrease in time spent reconciling data: 47%, Boosted confidence in analytical systems: 46%, Improved customer satisfaction: 45%

Just 40% of survey respondents are “very” confident in their organization’s data quality management (DQM).

62% have formal Master Data Management (MDM) program technology, and 61% use DQM software on premise and 53% use a DQM cloud service.

94% believe that business value is lost as a result of poor data quality, and nearly three out of 10 say 50% or more of business value is lost due to data quality issues.

71% say their data’s integrity must be addressed, and 68% say the same about its accuracy.

58% say their organization must improve the consistency of its data, and 54% say the same about the data validity.

Data entry by employees: 58%, Data migration or conversion projects: 47%, Mixed entries by multiple users: 44%, Changes to source systems: 44%, Systems errors: 43%

44% say they will either initiate an Internet of things (IoT) program for the first time this year, or they will expand an existing IoT program.

Predictive analytics: 67%, Recommender systems: 67%, Cluster analysis and segmentation: 59%