Learn the definition of data quality and discover best practices for maintaining accurate and reliable data. Data quality refers to the reliability, accuracy, consistency, and validity of your data.

While companies may share common ground when it comes to their data quality problems, data quality tools and strategies are not one-size-fits-all solutions to the problem. Each company should approach ...

Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. Increasingly more corporate boards and executives understand the importance of data for ...

In healthcare, the costs are not merely financial. Incomplete, inconsistent or delayed electronic health records can lead to ...

Poor data quality costs organizations an average of $12.9 million a year. Organizations are beginning to recognize that not only does it have a direct impact on revenue over the long term, but poor ...

Market intelligence is all about valuable data that is readily available to businesses. That data helps evaluate your market position, understand your audience, identify risks and growth opportunities ...

Randy Barrett is a freelance writer and editor based in Washington, D.C. A large part of his portfolio career includes teaching banjo and fiddle as well as performing professionally. An organization ...

AI, machine learning, and edge com­puting may be all around us, and these technology endeavors all have one important thing in common: Their suc­cess depends on the quality of the data fed into them.

Improved data quality offers enormous financial benefits and can even increase work satisfaction, says Dutch shipbuilding software firm Shipbuilder. “Nevertheless, we see that many maritime companies ...

Fab operations have wrestled with big data management issues for decades. Standards help, but only if sufficient attention to detail is taken during collection. Semiconductor wafer manufacturing ...