Paper Details

Artificial intelligence (AI) has revolutionized a number of industries and profoundly affected our day-to-day existence. The success of AI is largely dependent on high-quality data. The progression of data quality (DQ) awareness from conventional data management systems to contemporary data-driven AI systems—which are essential to data science—is thoroughly reviewed in this work. We summarize the body of research, emphasizing the quality issues and methods that have developed from traditional data management to data science, which includes big data and machine learning. Although data science solutions facilitate a variety of tasks, in this study we specifically address the analytics component powered by machine learning. To provide a more comprehensive knowledge of growing DQ difficulties and the associated quality awareness strategies in data science systems, we leverage the cause-effect relationship between the quality challenges of big data and machine learning. We believe this work is the first to review DQ awareness across both classic and emerging data science platforms. We hope that this exploration of the development of data quality awareness will be useful and enlightening to readers.

Data Quality (DQ), Big Data, Data Science, Machine Learning(ML)