BigDataFr recommends: Introduction to data quality

How many times have you heard managers and colleagues complain about the quality of the data in a particular report, system or database? People often describe poor quality data as unreliable or not trustworthy. Defining exactly what high or low quality data is, why it is a certain quality level and how to manage and improve it is often a trickier task.

Within the Data Quality Management community there is a generally held view that the quality of a dataset is dependent on whether it meets defined requirements. Managers often define these requirements as outcomes such as higher sales, lower costs or less defects. Whilst this is important however it doesn’t help practitioners at the coalface to codify rules and other tests designed to measure the quality of a dataset. For this a more specific definition of requirements such as completeness or uniqueness levels is required. An example requirement statement for example could be “All clients should have a name and address populated in our CRM system”. […]

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By Zygimantas Jacikevicius
Source: datasciencecentral.com