redundant data - Global Intelligence Blog

Data Quality’s Three C’s Go Beyond Accuracy

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Quality | data records, data rules, dirty data, domain constraints, high quality data, inconsistent data, Melissa Data, redundant data

By Elliot King

In many cases, quality, like beauty, is in the eyes of the beholder. The exact characteristics that define quality can be hard to describe. For example, a news report recently described a new, synthetic method for producing diamonds. Would those diamonds be of the same quality? And would they be as desirable, as diamonds mined and refined in the regular way?

Reducing Data Quality Risks

Blog Administrator | Analyzing Data Quality, Data Management, Data Quality | address data, data decay, data quality issues, people move, poor data entry, redundant data

By Elliot King

As Donald Rumsfeld, the former secretary of the defense once famously said, “there are known unknowns and there are unknown unknowns.” In other words, somethings we know we don’t know and consequently we can do the research to learn what we need to know. But other times, we don’t even know what we don’t know. Unknown unknowns present real risks, as Rumsfeld sadly learned.

Four Pillars of Data Quality Improvement

Blog Administrator | Analyzing Data Quality, Data Integration, Data Management, Data Profiling, Data Quality | data augmentation, data flows, data improvement, data profiling, redundant data

By Elliot King

Almost all data quality management programs have four key elements that serve as the foundations for success–data profiling, data improvement, integration and data augmentation. Put in other words, data quality programs must determine what is broken; fix what can be fixed; consolidate what can be consolidated and enhance what needs to be enhanced. Sounds easy, right? If only.