Development and implementation of the data quality assurance subsystem for the MDM platform
Tereshchenko, Elizaveta (2021)
Diplomityö
2021
School of Engineering Science, Laskennallinen tekniikka
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
http://urn.fi/URN:NBN:fi-fe2021062139227
http://urn.fi/URN:NBN:fi-fe2021062139227
Tiivistelmä
Data is the fuel for artificial intelligence systems, the raw material for analytical algorithms, and the basis for business process automation systems. If decision-makers do not have timely, relevant, and reliable information, they have no choice but to rely on their intuition. Data quality becomes a crucial aspect.
The research aims to develop and implement a data quality assurance subsystem designed to improve data quality and user interaction with data. The study was carried out based on the master data management platform used in a state-owned company, which perform state cadastral registration of real estate activities. As a research method, a single case study analysis and literature review were used. The scientific novelty of the research is the development of the subsystem, which prevent enterprises from data quality problems. Considering different aspects of data quality, this research is an excellent asset to the architectures, developers, and business analysts to develop and adopt data quality assurance subsystems with master data management systems. Moreover, the methodology can also be applied to any implemented system.
The research aims to develop and implement a data quality assurance subsystem designed to improve data quality and user interaction with data. The study was carried out based on the master data management platform used in a state-owned company, which perform state cadastral registration of real estate activities. As a research method, a single case study analysis and literature review were used. The scientific novelty of the research is the development of the subsystem, which prevent enterprises from data quality problems. Considering different aspects of data quality, this research is an excellent asset to the architectures, developers, and business analysts to develop and adopt data quality assurance subsystems with master data management systems. Moreover, the methodology can also be applied to any implemented system.