Evaluation of data quality tools

Evaluation of data quality tools Abstract Data plays an important role in our day-to-day activities and its importance cannot be over emphasized. For organizations, it has become a valuable asset that drives strategy and informed decision making but the benefits of data are compromised if the data is of bad quality. With the large amount of data generated daily, it is common within datasets to find anomalies such as inconsistency, outdated values, missing values, duplicate values, wrong formats or representations of data, etc. Such anomalies negatively impact the quality of data significantly and degrade the quality of information, insights or decisions derived from such datasets. For this reason, it is necessary to assess and ensure the quality of data is reliable and up to standards. The goal of this thesis is to provide an understanding of data quality, the dimensions of data quality and the standards in place to assess and measure data quality. Exploration of available tools which can be used to assess and improve data quality, and finally a comparative analysis is then carried out on these tools to understand their capabilities.