Overview of data quality challenges in the context of Big Data
The article reviews literature on data quality under a framework with 5 components.
Data Dimensions – what to measure to know how to improve quality
Measurement and Metrics – how to measure and quantify particular dimensions
And then a third layer : Data cleansing, Data profiling, Data quality rules
Why it’s relevant to Nextspace
This article is fairly old (2015), but remains of interest to Nextspace Partners as a relatively comprehensive review. And as the author points out in his Summary
The first general learning from and consensus amongst experts in Big Data quality circle is that many traditional data quality processes [remain] highly relevant
The other useful element is the great list of data quality dimensions.
Not so useful is that they are listed from multiple sources and there are some double ups.
Accessibility
Accuracy
Appropriate Amount of data
Believability
Completeness
Compliance
Confidentiality
Consistency – contextual, temporal, operational
Correctness
Currentness
Ease of manipulation
Efficiency
Free-of-error
Interpretability
Objectivity
Precision
Recoverability
Relevancy
Reputation
Security
Timeliness
Traceability
Understandability
Value-added
Variety
Velocity
Volatility
Volume
