基 于 大 数 据 技 术 的 数 据 质 量 评 估 方 法 研 究 Chapter 1 Introduction In recent years, big data has become an essential part of business and industry operations, leading to a significant increase in the amount of data being generated. The large volume of data poses a challenge to enterprises, making it difficult to ensure data quality. Data quality assessment is thus an important task which helps identify and mitigate data quality issues. This article evaluates the different methods used to assess data quality using big data technology. Chapter 2 Data Quality Assessment and Challenges Data quality assessment is the process of evaluating the accuracy, completeness, and consistency of data. Businesses depend on data to make decisions and optimize their operations. Poor data quality can have serious repercussions. Inaccurate data can lead to incorrect data analysis and subsequent faulty decisions. Data incompleteness and inconsistency can also lead to poor decision making, which can cost the business dearly. The challenge with data quality assessment lies in the large volume of data generated, which makes it difficult to assess the quality of the data manually. As such, automated data quality assessment techniques have evolved with the increased adoption of big data technology. Chapter 3 Data Quality Assessment Techniques Several data quality assessment techniques are currently employed. These techniques include: 3.1 Data Profiling: Data profiling entails analyzing data structure, relationships, and quality metrics. The profiling task is automated, giving insight that identifies data quality issues in the dataset. The automated process makes it possible to assess large volumes of data quickly and ...
