A classification of data quality assessment and improvement methods

Data quality (DQ) assessment and improvement in larger information systems would often not be feasible without using suitable “DQ methods”, which are algorithms that can be automatically executed by computer systems to detect and/or correct problems in datasets. Currently, these methods are already essential, and they will be of even greater importance as the quantity of data in organisational systems grows. This paper provides a review of existing methods for both DQ assessment and improvement and classifies them according to the DQ problem and problem context. Six gaps have been identified in the classification, where no current DQ methods exist, and these show where new methods are required as a guide for future research and DQ tool development.This is the accepted manuscript. It's currently embargoed pending publication by Inderscience

Data Quality Management

Data quality is crucial in measuring and analyzing science, technology and innovation adequately, which allows for the proper monitoring of research efficiency, productivity and even strategic decision making. In this chapter, the concept of data quality will be defined in terms of the different dimensions that together determine the quality of data. Next, methods will be discussed to measure these dimensions using objective and subjective methods. Specific attention will be paid to the management of data quality through the discussion of critical success factors in operational, managerial and governance processes including training that affect data quality. The chapter will be concluded with a section on data quality improvement, which examines data quality issues and provides roadmaps in order to improve and follow-up on data quality, in order to obtain data that can be used as a reliable source for quantitative and qualitative measurements of research

Quality of E-Tax System and its Effect on Tax Compliance (Evidence from Large Taxpayers in Tanzania)

        Abstract        Globally, countries endeavor toward improving tax compliance behavior with the ultimate goal of increasing tax revenue collection. This study examined the quality of the e-tax system and its effect on the tax compliance behavior of large taxpayers in Tanzania. The data were gathered from 313 large taxpayers from three regions in Tanzania, namely Dar es Salaam, Mwanza, and Arusha. The study employed Information System Success Model (The IS model) with constructs service quality, system quality, information quality, user satisfaction, behavioral intention, and tax compliance behavior (actual behavior). A Partial Least Square Structural Equation Modeling (PLS-SEM) with SmartPLS3 was used to evaluate the latent variables and their indicators.  The results showed that behavioral intention to use the e-tax system has the strongest effect on tax compliance behavior. Thus, service and information quality had an incredible effect on creating eagerness to accept and utilize the e-tax system which improves tax compliance behavior. However system quality has not shown a significant effect on tax compliance behavio

Developing an ontological framework for effective data quality assessment and knowledge modelling

Big data has become a major challenge in the 21st century, with research being carried out to classify, mine and extract knowledge from data obtained from disparate sources. Abundant data sources with non-standard structures complicate even more the arduous process of data integration. Currently, the major requirement is to understand the data available and detect data quality issues, with research being conducted to establish data quality assessment methods. Further, the focus is to improve data quality and maturity so that early onset of problems can be predicted and handled effectively. However, the literature highlights that comprehensive analysis, and research of data quality standards and assessment methods are still lacking. To handle these challenges, this paper presents a structured framework to standardise the process of assessing the quality of data and modelling the knowledge obtained from such an assessment by implementing an ontology. The main steps of the framework are: (i) identify user’s requirements; (ii) measure the quality of data considering data quality issues, dimensions and their metrics, and visualise this information into a data quality assessment (DQA) report; and (iii) capture the knowledge from the DQA report using an ontology that models the DQA insights in a standard reusable way. Following the proposed framework, an Excel-based tool to measure the quality of data and identify emerging issues is developed. An ontology, created in Protégé, provides a standard structure to model the data quality insights obtained from the assessment, while it is frequently updated to enrich captured knowledge, reducing time and costs for future projects. An industrial case study in the context of Through life Engineering Services, using operational data of high value engineering assets, is employed to validate the proposed ontological framework and tool; the results show a well-structured guide that can effectively assess data quality and model knowledge

Automating Electronic Health Record Data Quality Assessment

Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies and tools to help ensure that available data are fit for use. However, developing reliable data quality assessment (DQA) tools necessary for guiding and evaluating improvement efforts has remained a fundamental challenge. This review examines the state of research on operationalising EHR DQA, mainly automated tooling, and highlights necessary considerations for future implementations. We reviewed 1841 articles from PubMed, Web of Science, and Scopus published between 2011 and 2021. 23 DQA programs deployed in real-world settings to assess EHR data quality (n = 14), and a few experimental prototypes (n = 9), were identified. Many of these programs investigate completeness (n = 15) and value conformance (n = 12) quality dimensions and are backed by knowledge items gathered from domain experts (n = 9), literature reviews and existing DQ measurements (n = 3). A few DQA programs also explore the feasibility of using data-driven techniques to assess EHR data quality automatically. Overall, the automation of EHR DQA is gaining traction, but current efforts are fragmented and not backed by relevant theory. Existing programs also vary in scope, type of data supported, and how measurements are sourced. There is a need to standardise programs for assessing EHR data quality, as current evidence suggests their quality may be unknown

Data Migration Testing

Infosüsteemide uuendamise ajajärgul puutuvad infosüsteemide arendajad üha enam kokku vajadusega tõsta (migreerida) vana infosüsteemi andmed uue infosüsteemi andmebaasi. Magistritöös selgitatakse migratsiooni testimist ning leitakse võimalusi testide koostamise ja testimise lihtsustamiseks (automatiseerimiseks). Testimise automatiseerimise vajadusest lähtuvalt käsitletakse migratsiooni testi päringute komplektina, kus igasse komplekti kuulub üks isevalideeruv test (päring) ja vähemalt üks vigaseid kirjeid tagastav abipäring. Sellise lähenemisviisiga saab testide käivitamisel kohese ülevaate migreeritud andmete seisust ja vigaste andmete (kirjete) detailvaate saamiseks ei ole vaja kirjutada SQL lauseid. Seega muutub vigade analüüsimine efektiivsemaks, sest väheneb täiendavate päringute koostamise vajadus. Testide koostamise automatiseerimise eelduseks testide sarnasuse alusel testide tüüpidesse jagamine. Töös defineeriti 16 testi tüüpi ja iga testi tüübi jaoks koostati testi päringu mall (template) ja abipäringute mallid (näidispäringud) ning selgitati testi metaandmete vajadust. Lisas toodud testide tüüpide kirjeldused on näidiseks migratsiooni testijatele. Testide kirjelduste alusel saab arendada ka testide päringute koostamise generaatori.In the era of information system upgrades, information system developers are increasingly confronted with the need to upgrade (migrate) the old information system data into the database of the new information system. Master's thesis explains migration testing and finds ways to simplify (automate) test design and testing. Based on the need for automation testing, the migration test is considered as a set of queries where every element include one self-validating test (query) and at least one auxiliary query that returns invalid entries. With this approach, one can instantly view the status of migratory data when one runs the tests, and no need to write new SQL statements to get a detailed view of the incorrect data (records). Thus, the analysis of errors becomes more efficient as the need for new additional queries is reduced.The prerequisite for automation of test preparation is the division of tests into test types based on similarity of tests. In this work, 16 test types were defined, and a template for the test query and auxiliary query templates (pattern queries) were prepared for each type of test and the need for test metadata was explained. The descriptions of the types of tests in the Appendix are an example of migration testing. A test generator can also be developed based on test descriptions

Addressing Needs of Intimate Partner Violence Survivors in the Emergency Department

Intimate partner violence is a global epidemic and public health concern, including in the United States. The purpose of this descriptive, exploratory, nonexperimental, quantitative study was to determine to what extent intimate partner violence survivors avail themselves of offered resources and interventions in health care settings. The general systems foundation was used for the study\u27s theoretical foundation. The research questions ascertained the proportion of intimate partner violence survivors who accepted mental health, law enforcement, and community outreach resources; the level of comprehensive intervention they received; and the associations, if any, between types of services. Retrospective data were collected from121 medical records from an emergency department in the Midwest United States. Descriptive statistics were performed on collected medical record data and chi-square analyses were performed in an exploratory manner to determine associations between types and numbers of other services accepted. The outcomes indicated that the majority of participants accepted comprehensive intervention, social work or mental health intervention was the most frequently accepted service, and the majority of patients who accepted social work accepted other services. Anticipated social implications may include survivors receiving multi-disciplinary interventions sooner, increased efforts by health care providers to work collaboratively with community agencies, continued development of hospital policy and protocols, and opportunities for further research. Society may ultimately benefit from a decreased economic cost to society and a positive impact in growth and development of witnessing children