A method to obtain sustained data quality at Distimo

This thesis attempts to answer the following research question: “how can we determine and improve data quality?”. A method is proposed to systematically analyse the demands and current state of data quality within an organisation. The mission statement and information systems architecture are used to characterise the organisation. A list of data quality characteristics based on literature is used to express the organisation in terms of data quality. Metrics are established to quantify the data quality characteristics. A risk analysis determines which are the most important areas to improve upon. After improvement, the metrics can be used to evaluate the success of the improvements. Distimo is an innovative application store analytics company aiming to solve the challenges created by a widely fragmented application store marketplace filled with equally fragmented information and statistics. As Distimo’s products are very data driven, data quality is very important. The method will be applied to Distimo as a case study. The proposed method provides a way to determine the current state of data quality, and to determine what to improve, and how to evaluate if the improvements provide the desired outcome. The case study of Distimo resulted in an in-depth analysis of Distimo, which in turn yielded a number of data quality improvements that at this very moment are in production and have improved data quality. Because of the generic nature of input data, the proposed method is applicable to any organisation looking to improve data quality. The iterative improvement process allow for fine grained control of changes to organisational processes and systems.