Data quality is a research area strongly investigated during the 90’s. However, few companies in Argentina apply data quality methodologies or tools during the analysis, design or implementation phases of software development process. Developers generally use techniques to design systems such as UML without considering mechanisms for future data quality problems. In this work we propose a methodology in which the data quality is an essential part of the whole software development process. Early design decisions on data quality strongly impact on the system. Our methodology defines a set of practices to be applied on the software life cycle. In addition these practices act as a means to evaluate if systems already running fulfill with minimal ...
Data is the most important asset of any IT organization. The most successful companies of the world ...
By its nature, the term “data quality” with its generic meaning “fitness for use” has both subjectiv...
Context: We revisit our review of data quality within the context of empirical software engineering ...
Data quality is a research area strongly investigated during the 90’s. However, few companies in Arg...
Data quality is a research area strongly investigated during the 90’s. However, few companies in Arg...
Information systems development is a very important activity that is performed continuously in Infor...
Software quality is explicitvproperty which determines what sort of standards software ought to have...
Information systems have been rapidly evolving from monolithic/ transactional to network/service bas...
The data asset is increasingly becoming one of the top factors in securing organization success. Rec...
Existing methodologies for identifying dataquality problems are typically user-centric, where dataqu...
Data quality is crucial in measuring and analyzing science, technology and innovation adequately, wh...
It is now assumed that poor quality data is costing large amounts of money to corporations all over ...
The importance of data quality has been considered for many years and is well recognized among pract...
OBJECTIVE - to assess the extent and types of techniques used to manage quality within software engi...
The growing relevance of data quality has revealed the need for adequate measurement since quantifyi...
Data is the most important asset of any IT organization. The most successful companies of the world ...
By its nature, the term “data quality” with its generic meaning “fitness for use” has both subjectiv...
Context: We revisit our review of data quality within the context of empirical software engineering ...
Data quality is a research area strongly investigated during the 90’s. However, few companies in Arg...
Data quality is a research area strongly investigated during the 90’s. However, few companies in Arg...
Information systems development is a very important activity that is performed continuously in Infor...
Software quality is explicitvproperty which determines what sort of standards software ought to have...
Information systems have been rapidly evolving from monolithic/ transactional to network/service bas...
The data asset is increasingly becoming one of the top factors in securing organization success. Rec...
Existing methodologies for identifying dataquality problems are typically user-centric, where dataqu...
Data quality is crucial in measuring and analyzing science, technology and innovation adequately, wh...
It is now assumed that poor quality data is costing large amounts of money to corporations all over ...
The importance of data quality has been considered for many years and is well recognized among pract...
OBJECTIVE - to assess the extent and types of techniques used to manage quality within software engi...
The growing relevance of data quality has revealed the need for adequate measurement since quantifyi...
Data is the most important asset of any IT organization. The most successful companies of the world ...
By its nature, the term “data quality” with its generic meaning “fitness for use” has both subjectiv...
Context: We revisit our review of data quality within the context of empirical software engineering ...