Resumen de I25k: environment for the evaluation and certification of the quality of data products

Jorge Merino Garcia

• Data is the most important asset of any IT organization. The most successful companies of the world are data-driven businesses. Brave people, such as Julian Assange and Edward Snowden, have demonstrated that even the most important countries store massive amounts of data as their raw material to gain strategic advantages.

  As any other raw material or asset for creating goods and services, data must be good enough for those companies to obtain benefits. When making choices based on data, it is vital that this raw material has the necessary levels of quality. Otherwise, created goods and services using the data might be useless or not appropriate for the intended purposes, and data-based decisions might be worthless or even harmful for the companies.

  Considering that both the industry and public entities have interest in Data Quality, several solutions on Data Quality Assessment are present in the literature. Unfortunately, none of them focus on the certification and the assurance of the levels of quality of this precious asset. Consequently, this research digs deeply in the evaluation and certification of data in terms of its quality.

  The main contribution of this thesis is the creation of an environment called I25K, for the evaluation and certification of Data Quality. I25K is composed of a certification process, an evaluation process, and a Data Quality Model. This environment has been defined to be easily implementable and deployable, including details on the necessary resources, the roles that participate in the evaluation and certification processes, and their responsibilities. I25K was validated through several case studies and it has been implemented and deployed alongside two important Spanish companies.

  The implementation of the Data Quality Model in Big Data scenarios and the application of this model to quantify the levels of quality of Master Data have been published in indexed journals. Future lines of research have been drawn as well, including the improvement of Data Quality and the extension of the results. Furthermore, business opportunities that can be the consequence of this research have been proposed.