A Software Engineering View of Data Quality

Procedia Computer Science, 2017

The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object's values and procedures for data object's analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in cases when software architecture and/or data models are changed; (3) to gather data from different data sources and to transfer them to data warehouse.

Information & Management, 1980

Until recently, data quality was poor'.y understood and seldom achieved, yet it is essential to tlihe effective use of information systems. This paper discusses/ the nature and importance of data quality. The role of dataquality is placed in the life cycle framework. Many new concepts, tools and i techniques from both programming lang,uages and database management systems are presented and rhiated to data quality. In particular, the coqcept of a databrlse constraint is considered in detail. Some current limitation/s and research directions are proposed.

Journal on Advances in Theoretical and Applied Informatics

Users need trusting in data managed by software applications that are part of Information Systems (IS), which supposes that organizations should assuring adequate levels of quality in data that are managed in their IS. Therefore, the fact that an IS can manage data with an adequate level of quality should be a basic requirement for all organizations. In order to reach this basic requirement some aspects and elements related with data quality (DQ) should be taken in account from the earliest stages of development of software applications, i.e. “data quality by design”. Since DQ is considered a multidimensional and largely context-dependent concept, managing all specific requirements is a complex task. The main goal of this paper is to introduce a specific methodology, which is aimed to identifying and eliciting DQ requirements coming from different viewpoints of users. These specific requirements will be used as normal requirements (both functional and non-functional) during the deve...

2018

The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object’s values and procedures for data object’s analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in cases when software architecture and/or data models are changed; (3) to gather data from different data sources and to transfer them to data warehouse. © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the scientific committee of the internat...

2002

Abstract: La qualità dei dati è una tematica affrontata in ambito statistico, gestionale, informatico, insieme a molti altri settori scientifici. Il presente articolo considera il problema della definizione della qualità dei dati dal punto di vista informatico. Sono comparate alcune proposte di dimensioni (o caratteristiche) che contribuiscono alla definizione della qualità dei dati e viene introdotta una definizione “base” di tale concetto.

JUCS - Journal of Universal Computer Science, 2020

The paper proposes a new data object-driven approach to data quality evaluation. It consists of three main components: (1) a data object, (2) data quality requirements, and (3) data quality evaluation process. As data quality is of relative nature, the data object and quality requirements are (a) use-case dependent and (b) defined by the user in accordance with his needs. All three components of the presented data quality model are described using graphical Domain Specific Languages (DSLs). In accordance with Model-Driven Architecture (MDA), the data quality model is built in two steps: (1) creating a platform-independent model (PIM), and (2) converting the created PIM into a platform-specific model (PSM). The PIM comprises informal specifications of data quality. The PSM describes the implementation of a data quality model, thus making it executable, enabling data object scanning and detecting data quality defects and anomalies. The proposed approach was applied to open data sets, ...

2005

The exploration of data to extract information or knowledge to support decision making is a critical success factor for an organization in today's society. However, several problems can affect data quality. These problems have a negative effect in the results extracted from data, affecting their usefulness and correctness. In this context, it is quite important to know and understand the data problems. This paper presents a taxonomy of data quality problems, organizing them by granularity levels of occurrence. A formal definition is presented for each problem included. The taxonomy provides rigorous definitions, which are information-richer than the textual definitions used in previous works. These definitions are useful to the development of a data quality tool that automatically detects the identified problems.

Journal of Systems and Software, 2021

The most successful organizations in the world are data-driven businesses. Data is at the core of the business of many organizations as one of the most important assets, since the decisions they make cannot be better than the data on which they are based. Due to this reason, organizations need to be able to trust their data. One important activity that helps to achieve data reliability is the evaluation and certification of the quality level of organizational data repositories. This paper describes the results of the application of a data quality evaluation and certification process to the repositories of three European organizations belonging to different sectors. We present findings from the point of view of both the data quality evaluation team and the organizations that underwent the evaluation process. In this respect, several benefits have been explicitly recognised by the involved organizations after achieving the data quality certification for their repositories (e.g., long-term organizational sustainability, better internal knowledge of data, and a more efficient management of data quality). As a result of this experience, we have also identified a set of best practices aimed to enhance the data quality evaluation process.

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, 2017

The research discusses the issue how to describe data quality and what should be taken into account when developing an universal data quality management solution. The proposed approach is to create quality specifications for each kind of data objects and to make them executable. The specification can be executed step-by-step according to business process descriptions, ensuring the gradual accumulation of data in the database and data quality checking according to the specific use case. The described approach can be applied to check the completeness, accuracy, timeliness and consistency of accumulated data.