Data quality, one particular area of data governance, is a multi-dimensional notion which crosses the classical IT-business divide. Data quality problems emerge through interactions between the following worlds: 1) systems, devices, and interfaces, 2) policies, rules, and best practices, and 3) the behaviour of people. Typical quality problems concerns data that is incomplete, inaccurate, or duplicated. Root-cause analysis can then be conducted by understanding the types of quality problems (e.g. in the form of patterns) and tracing back their origins through these worlds. Understanding these root causes forms the basis of mitigation and prevention. Little work has been conducted on how individual behaviour may impact data quality and this is a question of interest to the behavioural data governance program.
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