The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.

Methodologies for data quality assessment and improvement

Nowadays, the importance of achieving and maintaining a high standard of data quality is widely recognized by both practitioners and researchers. Based on its impact on businesses, the quality of data is commonly viewed as a valuable asset. The literature comprises various techniques for defining, assessing, and improving data quality. However, requirements for data and their quality vary between organizations. Due to this variety, choosing suitable methods that are advantageous for the data quality of an organization or in a particular context can be challenging. This paper surveys data quality frameworks in a comparative way regarding the definition, assessment, and improvement of data quality with a focus on methodologies that are applicable in a wide range of business environments. To aid the decision process concerning the suitability of these methods, we further provide a decision guide to data quality frameworks. This guidance aims to help narrow down possible choices for data quality methodologies based on a number of specified criteria.

An Overview of Data Quality Frameworks

We present a comprehensive framework and test methodology for the evaluation of human-machine interfaces (HMI) and human-robot interactions (HRI) in collaborative manufacturing applications. An overview of the challenges that face current- and next-generation collaborative robot systems is presented, specifically focused on the interactions between man and machine, and a series of objectively quantitative and subjectively qualitative metrics are given to guide the development and assessment of interfaces and interactions. A generalized set of guidelines for the design of HMI is also proposed to address these challenges and thereby enable effective and intuitive diagnostics and error corrections when process failures occur. These guidelines are aimed at aiding researchers in developing effective interface and interaction technologies, maximizing operator situation awareness in human-robot collaborative manufacturing teams, promoting effective process and system diagnostics reporting, and enabling faster responses to equipment or application errors.

https://dl.acm.org/doi/pdf/10.1145/3385009

Towards Effective Interface Designs for Collaborative HRI in Manufacturing: Metrics and Measures

Measuring and improving data quality in an organisation or in a group of interacting organisations is a complex task. Several methodologies have been developed in the past, providing a basis for the definition of a data quality programme that guarantees high data quality levels. Since the main limitation of existing approaches is their specialisation on specific issues or contexts, this paper presents a Comprehensive Data Quality (CDQ) methodology. The main aim of the CDQ methodology is the integration and enhancement of the phases, techniques and tools proposed by previous approaches. In particular, the CDQ methodology is conceived to be at the same time complete, flexible and simple to apply. Completeness is achieved by considering an existing techniques and tools and integrating them in a framework that can work in any organisation. The methodology is flexible, since it supports the user in the selection of the most suitable techniques and tools within each phase and in any context. Finally, CDQ is simple, since it is organised in phases and each phase is characterised by a specific goal and a set of techniques to apply. The methodology is explained by means of a running example and significant cases of its application are reported.

A comprehensive data quality methodology for web and structured data

Topical problems of modern business are: how to cull right information from its abundance, how to decide which information is valuable and which one is useless, finally, how to assess the quality of the usable informa-tion. These problems are relevant and important both globally and locally in Lithuanian business. This paper highlights information quality (IQ) management and assessment peculiarities, reveals the importance of IQ in the modern business. The authors present IQ dimensions that are essen-tial to Lithuanian and foreign business information con-sumers because effective IQ assessment and manage-ment depend upon right evaluation of these IQ dimen-sions. The authors of this paper also evaluated informa-tion maturity levels of Lithuanian business organiza-tions and revealed their attitudes towards the informa-tion. This information maturity was assessed according to IBM Corporation proposed Information Maturity Model. In reference to the accomplished research, authors of this paper identify the crucial problems that influence business IQ and also highlight these root problems: absence of proper technologies, poor information tech-nologies (IT), inappropriate software, difficulties to discover reasons for IQ errors, insufficient information security, lack of time to ensure IQ, lack of reliable in-formation sources, poor knowledge of business informa-tion consumer (employee). The findings revealed that the main business IQ assurance problem is influenced simply by the lack of time. To draw a conclusion, to ensure IQ, information must be presented and produced in accordance with clearly defined IQ dimensions, just like other products (services and goods) are supplied and evaluated ac-cording to specific quality characteristics. Conse-quently, the identification of IQ dimensions that are the most important to information consumers, and the esti-mation of IQ measurement criteria and its methodology are the basis for the better information product’s qual-ity assessment. To assure quality information in the organizations the authors of this paper propose: to adapt IQ Man-agement Model that might service like one of the tools for solving IQ assurance problems, to define and choose right business IQ dimensions, and change organiza-tional attitude towards the value of information.

/pdf/business-information-quality-and-its-assessment-2ie3nwtm8u.pdf

Business Information Quality and its Assessment

A Methodology for Information Quality Assessment in the Designing and Manufacturing Processes of Mechanical Products

Information Quality (IQ) is a critical factor for the success of many activities in the information age, including the development of data warehouses and implementation of data mining. The issue of IQ risk is recognized during the process of data mining; however, there is no formal methodological approach to dealing with such issues. Consequently, it is essential to measure the risk of IQ in a data warehouse to ensure success in implementing data mining. This article presents a methodology to determine three IQ risk characteristics: accuracy, comprehensiveness, and non-membership. The methodology provides a set of quantitative models to examine how the quality risks of source information affect the quality for information outputs produced using the relational algebra operations: Restriction, Projection, and Cubic product. It can be used to determine how quality risks associated with diverse data sources affect the derived data. The study also develops a data cube model and associated algebra to s...

Modeling Information Quality risk for data mining in data warehouses.

This paper describes the simulation modeling for redesigning health service centers at a medical consultation corporation Ltd. Research was performed in an effort to improve health service, in particular to reduce average waiting times as well as their variability. Discrete-event simulation modeling provided valuable input into new facility design decisions and showed the efficacy of pooling sources of variation, particularly patient demand and service times. Initial performance of the redesigned facilities was positive; however, dynamic feedback within the system of service centers eventually resulted in unanticipated performance problems. System dynamics models were developed to help predict these implementation problems and suggest some ways to improve results.

Modeling Health Service Centers with Simulation and System Dynamics

In this paper we outline a framework for assuring information quality (IQ) in an e-science context, using semiotics theory, semantic explanation of resources, and data couplings. Scientists can define the quality characteristics that are of importance in their particular domain by applying an IQ semiotics, which classifies and organizes these domain-specific quality characteristics within this quality assurance framework. Resource description framework (RDF) is used to explain data resources, with reference to IQ indicators defined in the semiotics. Data couplings - again defined in RDF - are used to represent mappings between data elements and the IQ semiotics. As a practical illustration of our approach, we present a case study from the domain of natural science.

Assuring information quality in e-Science

This paper describes the simulation modeling for planning for the efficient transportation of patients following a disaster Research was performed in an effort to predict the consequences of conceivable scenarios Discrete-event simulation modeling provided an effective supplement to regular drill procedures when training emergency medical services personnel Initial performance of the redesigned facilities was positive; however, dynamic feedback within the system of emergency medical services eventually resulted in unanticipated performance problems System dynamics models were developed to help predict these implementation problems and suggest some ways to improve results

Simulation and System Dynamics Models for Transportation of Patients Following a Disaster

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