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The term data quality refers to measuring the fitness of data regarding the intended usage. Poor data quality leads to inadequate, inconsistent, and erroneous decisions that could escalate the computational cost, cause a decline in profits, and cause customer churn. Thus, data quality is crucial for researchers and industry practitioners.

Different factors drive the assessment of data quality. Data context is deemed one of the key factors due to the contextual diversity of real-world use cases of various entities such as people and organizations. Data used in a specific context (e.g., an organization policy) may need to be more efficacious for another context. Hence, implementing a data quality assessment solution in different contexts is challenging.

Traditional technologies for data quality assessment reached the pinnacle of maturity. Existing solutions can solve most of the quality issues. The data context in these solutions …