scholarly journals Influence of Information Quality via Implemented German RCD Standard in Research Information Systems

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 30
Author(s):  
Otmane Azeroual ◽  
Joachim Schöpfel ◽  
Dragan Ivanovic
Keyword(s):  
Information Systems ◽  
Data Quality ◽  
Information Quality ◽  
Research Performance ◽  
Data Sources ◽  
Steady Increase ◽  
Research Information ◽  
External Data ◽  
Core Dataset ◽  
The Impact

With the steady increase in the number of data sources to be stored and processed by higher education and research institutions, it has become necessary to develop Research Information Systems, which will store this research information in the long term and make it accessible for further use, such as reporting and evaluation processes, institutional decision making and the presentation of research performance. In order to retain control while integrating research information from heterogeneous internal and external data sources and disparate interfaces into RIS and to maximize the benefits of the research information, ensuring data quality in RIS is critical. To facilitate a common understanding of the research information collected and to harmonize data collection processes, various standardization initiatives have emerged in recent decades. These standards support the use of research information in RIS and enable compatibility and interoperability between different information systems. This paper examines the process of securing data quality in RIS and the impact of research information standards on data quality in RIS. We focus on the recently developed German Research Core Dataset standard as a case of application.

scholarly journals ETL Best Practices for Data Quality Checks in RIS Databases

Informatics ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 10 ◽  
Author(s):  
Otmane Azeroual ◽  
Gunter Saake ◽  
Mohammad Abuosba
Keyword(s):  
Data Integration ◽  
Data Quality ◽  
Information Quality ◽  
Database Systems ◽  
Data Sources ◽  
Research Information ◽  
External Data ◽  
Quality Checks ◽  
Quality Issues ◽  
It Departments

The topic of data integration from external data sources or independent IT-systems has received increasing attention recently in IT departments as well as at management level, in particular concerning data integration in federated database systems. An example of the latter are commercial research information systems (RIS), which regularly import, cleanse, transform and prepare the analysis research information of the institutions of a variety of databases. In addition, all these so-called steps must be provided in a secured quality. As several internal and external data sources are loaded for integration into the RIS, ensuring information quality is becoming increasingly challenging for the research institutions. Before the research information is transferred to a RIS, it must be checked and cleaned up. An important factor for successful or competent data integration is therefore always the data quality. The removal of data errors (such as duplicates and harmonization of the data structure, inconsistent data and outdated data, etc.) are essential tasks of data integration using extract, transform, and load (ETL) processes. Data is extracted from the source systems, transformed and loaded into the RIS. At this point conflicts between different data sources are controlled and solved, as well as data quality issues during data integration are eliminated. Against this background, our paper presents the process of data transformation in the context of RIS which gains an overview of the quality of research information in an institution’s internal and external data sources during its integration into RIS. In addition, the question of how to control and improve the quality issues during the integration process in RIS will be addressed.

scholarly journals Implementation and user acceptance of research information systems

2019 ◽  
Vol 54 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Joachim Schöpfel ◽  
Otmane Azeroual ◽  
Gunter Saake
Keyword(s):  
Information Systems ◽  
Data Quality ◽  
Technology Acceptance ◽  
User Acceptance ◽  
Small Sample ◽  
Perceived Usefulness ◽  
Academic Institutions ◽  
Research Information ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is to present empirical evidence on the implementation, acceptance and quality-related aspects of research information systems (RIS) in academic institutions. Design/methodology/approach The study is based on a 2018 survey with 160 German universities and research institutions. Findings The paper presents recent figures about the implementation of RIS in German academic institutions, including results on the satisfaction, perceived usefulness and ease of use. It contains also information about the perceived data quality and the preferred quality management. RIS acceptance can be achieved only if the highest possible quality of the data is to be ensured. For this reason, the impact of data quality on the technology acceptance model (TAM) is examined, and the relation between the level of data quality and user acceptance of the associated institutional RIS is addressed. Research limitations/implications The data provide empirical elements for a better understanding of the role of the data quality for the acceptance of RIS, in the framework of a TAM. The study puts the focus on commercial and open-source solutions while in-house developments have been excluded. Also, mainly because of the small sample size, the data analysis was limited to descriptive statistics. Practical implications The results are helpful for the management of RIS projects, to increase acceptance and satisfaction with the system, and for the further development of RIS functionalities. Originality/value The number of empirical studies on the implementation and acceptance of RIS is low, and very few address in this context the question of data quality. The study tries to fill the gap.

scholarly journals Data Quality as a Critical Success Factor for User Acceptance of Research Information Systems

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 35
Author(s):  
Otmane Azeroual ◽  
Gunter Saake ◽  
Mohammad Abuosba ◽  
Joachim Schöpfel
Keyword(s):  
Information Systems ◽  
Data Quality ◽  
User Acceptance ◽  
Success Factor ◽  
Critical Success Factor ◽  
Research Information ◽  
Text Data ◽  
Critical Success ◽  
Data Profiling ◽  
The Impact

In our present paper, the influence of data quality on the success of the user acceptance of research information systems (RIS) is investigated and determined. Until today, only a little research has been done on this topic and no studies have been carried out. So far, just the importance of data quality in RIS, the investigation of its dimensions and techniques for measuring, improving, and increasing data quality in RIS (such as data profiling, data cleansing, data wrangling, and text data mining) has been focused. With this work, we try to derive an answer to the question of the impact of data quality on the success of RIS user acceptance. An acceptance of RIS users is achieved when the research institutions decide to replace the RIS and replace it with a new one. The result is a statement about the extent to which data quality influences the success of users’ acceptance of RIS.

scholarly journals Information Systems Quality Level and Its Impact on the Strategic Flexibility: A Field Study on Tourism and Travel Companies in the Jordanian Capital Amman

2017 ◽  
Vol 7 (3) ◽  
pp. 164
Author(s):  
Abood Saleh Ahmad AL-Adwan
Keyword(s):  
Information Systems ◽  
Information Quality ◽  
Statistical Significance ◽  
Systems Design ◽  
Strategic Flexibility ◽  
Quality Level ◽  
The People ◽  
Systems Quality ◽  
The Impact ◽  
Information Systems Quality

This study aimed to analyze the impact on the Information Systems Quality on the Strategic Flexibility in Jordanian tourism and travel companies in capital Amman. To achieve the goals of this study, the questionnaire has been developed to collect data which has been distributed over (130) individual through the survey population, (100) individuals had been studied, which represents 77%. The study reached to a group of results: 1. the perceptions of the people in question were fluctuating between high and moderate toward the level of the availability of Information Systems Quality and all of its dimensions in the Jordanian tourism and travel companies in capital Amman. Whereas their perceptions of the Strategic Flexibility were all moderate. 2. There is a statistical significance impact on the Information Systems Quality dimensions (Usability, Availability, Response Time) on the Strategic Flexibility for Jordanian tourism and travel companies in capital Amman. The study recommends the questioned companies’ administrations to draw attention to the perspectives of the Information Systems users when updating the systems design to improve the dimension of the Systems adaptation, also to bring the researchers attention to do more researches concern the Information Systems Services Quality, Information Quality and the Strategic Flexibility to complete the elements of Information Systems efficiency.

scholarly journals Big Data’s Disruptive Effect on Job Profiles: Management Accountants’ Case Study

2021 ◽  
Vol 14 (8) ◽  
pp. 376
Author(s):  
Adriana Tiron-Tudor ◽  
Delia Deliu
Keyword(s):  
Big Data ◽  
Data Sources ◽  
Disruptive Effect ◽  
Positive Effects ◽  
External Data ◽  
Management Accountants ◽  
Flexible Framework ◽  
Potential Risks ◽  
The Impact ◽  
Job Profile

The abundance of new innovative data sources creates opportunities and challenges for all professions and professionals working with information. One of these professionals is the management accountant (MA). Although their tasks have expanded over time and especially recently, MAs have not fully employed all the available internal and external data sources to describe, diagnose, visualize, predict and prescribe possible solutions that enable smart decisions with positive effects on businesses. Thus, the paper investigates the impact of Big Data, including Data Analytics, on MA’s job profile. Through a review of the most recent academic and professional publications, the paper contributes to the debate surrounding the redefinition of the role of MAs in organizations in a novel informational perspective of Abbott’s theory. The results could serve as a research agenda and incentive for further studies, as well as provide MAs with a guide on the topic of the enlargement of their role(s), respectively, the augmentation of their tasks and responsibilities regarding the analysis of Big Data. Furthermore, the research may provide both a rich and flexible framework to help practitioners in their analysis of potential risks, opportunities and challenges when handling Big Data, and a lens for professional accounting associations and bodies by helping them to prioritize the holding and seizing of jurisdictions as an imperative part of safety and security.

scholarly journals Canadian Approaches to Optimizing Quality of Administrative Data for Health System Use, Research, and Linkage

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Catherine Eastwood ◽  
Keith Denny ◽  
Maureen Kelly ◽  
Hude Quan
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Quality Assessment ◽  
Administrative Data ◽  
Information Quality ◽  
Health Data ◽  
Data Sources ◽  
University Of Calgary ◽  
Quality Assessment And Improvement

Theme: Data and Linkage QualityObjectives: To define health data quality from clinical, data science, and health system perspectives To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data. To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives) To highlight how data linkage can be used to provide new insights into the quality of original data sources To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards. Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture. Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11. Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality. Intended Outcomes:“What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data. Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost. This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use. CIHI content will include: Methods for optimizing the value of clinical-administrative data CIHI Information Quality Framework Reabstraction studies (e.g. physician documentation/coders’ experiences) Linkage analytics for data quality University of Calgary content will include: Defining/measuring health data quality Automated methods for quality assessment and improvement ICD-11 features and coding practices Electronic health record initiatives

Assessing the Impact of System Quality, Information Quality, and Service Quality on Enterprise Resource Planning (ERP) Systems

2021 ◽  
Vol 17 (4) ◽  
pp. 69-84
Author(s):  
Godwin Banafo Akrong ◽  
Yunfei Shao ◽  
Ebenezer Owusu
Keyword(s):  
Information Systems ◽  
Service Quality ◽  
Enterprise Resource Planning ◽  
Information Quality ◽  
Resource Planning ◽  
Equation Modeling ◽  
System Quality ◽  
Erp Systems ◽  
Erp System ◽  
The Impact

Globally, governments are taking steps to help them increase their income generation margin by implementing tax administrative ERP systems. However, the impacts on the internal system users of these ERP system quality features have not drawn the attention needed. This study, therefore, examines the relationship between the information systems' (IS) quality and individual impact using the theoretical foundation of the DeLone and McLean IS success model and, secondly, addresses the interrelationships between the quality constructs of information systems (IS). The authors also used the structural equation modeling technique of partial least squares to evaluate and analyze the data. The results show that system quality, the information quality, and the service quality characteristics of the tax administrative ERP system have a strong positive impact on the success of the IS at the individual level. There is also a positive relationship between the information systems' (IS) quality construction. The results provide additional empirical observations and consequences for management.

scholarly journals Using the Canadian Institute for Health Information’s Information Quality Framework to Support Integration and Utilization of Complex, Multi-Jurisdictional Data

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Chrissy Willemse
Keyword(s):  
Data Quality ◽  
Information Quality ◽  
Data Linkage ◽  
Care Delivery ◽  
Descriptive Analysis ◽  
Analytical Data ◽  
Data Sources ◽  
Essential Information ◽  
Canadian Institute ◽  
Quality Framework

The Canadian Institute for Health Information (CIHI) provides essential information on Canada’s health systems and the health of Canadians. This presentation discusses information quality’s role in the integration and utilization of CIHI’s complex, multi-sector and multi-jurisdictional data. IntroductionCIHI’s Data and Information Quality Program is recognized internationally for its comprehensiveness and high standards. As the need for linked data research increases, the requirements on quality continue to grow. CIHI’s multi-sector, multi-jurisdictional healthcare system and the varying health policies, care delivery models, and data collection practices that go with it pose challenges for researchers as they try to pull the data together in a comprehensive way. CIHI’s Information Quality Framework forms the foundation for addressing these challenges and ensuring data are fit for integration and are properly utilized. Objectives and ApproachIn 2019, a connected data quality project was initiated to improve the usability of CIHI’s analytical data. Information quality framework concepts were applied across CIHI data sources to better understand data linkage challenges, measure inconsistencies across data sources, identify opportunities to improve data and standards, and develop resources to support users. ResultsFindings from the project identified key connected data quality activities for the organization to operationalize. These focus on quality assessment and reporting; harmonization of data standards; expanded documentation and analytical resources; data classification and profiling tools to support descriptive analysis; and new source of truth and pre-linked datasets. Quality activities were prioritized based on need and complexity, and “connected data teams” were established to carry out the work. Conclusion / ImplicationsExpansion of CIHI’s quality framework across data sources facilitates its data linkage capabilities and “connected data” use. It enables the evolution of CIHI’s analytical environments and information products from being database specific to integrated-data driven, and facilitates the use of CIHI’s analytical data for research.

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