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Data forms the vital asset of many organizations, as the quality of their decisions depends on the quality of their data. Trust in data is, therefore, critical. This paper aims to evaluate different aspects of data quality, examine the existing data veracity characteristics, and propose a methodology to assess the impact of fault-tolerant design patterns on data veracity. Data generated by IoT devices often reveals characteristics such as noise, incompleteness, and imprecision [1], which make it a prime example for data quality assessment. This paper investigates how we can effectively address the attributes and characteristics associated with data veracity by applying fault-tolerant design patterns within the data processing workflow.
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