Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 11

Data Quality Related Abstracts

11 A Framework for Information Quality in Accounting Information Systems Adoption

Authors: Wongsim Manirath

Abstract:

In order to implement AIS adoption successfully, it is important to consider the quality of information management and understand Information Quality (IQ) factors influencing AIS adoption. This research aims to explore ways of managing AIS adoption to investigate the adoption of accounting information systems within organisations. The study has led to the development of a framework for understanding the AIS adoption process in an organisation. This research used qualitative, interpretive evidence. This framework was developed from case studies and by collecting qualitative data (interviews). This research has conducted 10 case studies to study how IQ is managed through the accounting information system adoption process. A special focus is placed on determining how organisation size influences the information quality practices. The finding is especially useful to SMEs as many SMEs have the desire to grow bigger. By better dealing with IQ issues, there could be a successful future.

Keywords: Information Management, Data Quality, Accounting Information System, Information Quality

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10 The Impact of System and Data Quality on Organizational Success in the Kingdom of Bahrain

Authors: Amal M. Alrayes

Abstract:

Data and system quality play a central role in organizational success, and the quality of any existing information system has a major influence on the effectiveness of overall system performance.Given the importance of system and data quality to an organization, it is relevant to highlight their importance on organizational performance in the Kingdom of Bahrain. This research aims to discover whether system quality and data quality are related, and to study the impact of system and data quality on organizational success. A theoretical model based on previous research is used to show the relationship between data and system quality, and organizational impact. We hypothesize, first, that system quality is positively associated with organizational impact, secondly that system quality is positively associated with data quality, and finally that data quality is positively associated with organizational impact. A questionnaire was conducted among public and private organizations in the Kingdom of Bahrain. The results show that there is a strong association between data and system quality, that affects organizational success.

Keywords: Performance, system quality, Data Quality, Kingdom of Bahrain

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9 The Methodology of Out-Migration in Georgia

Authors: Shorena Tsiklauri

Abstract:

Out-migration is an important issue for Georgia as well as since independence has loosed due to emigration one fifth of its population. During Soviet time out-migration from USSR was almost impossible and one of the most important instruments in regulating population movement within the Soviet Union was the system of compulsory residential registrations, so-called “propiska”. Since independent here was not any regulation for migration from Georgia. The majorities of Georgian migrants go abroad by tourist visa and then overstay, becoming the irregular labor migrants. The official statistics on migration published for this period was based on the administrative system of population registration, were insignificant in terms of numbers and did not represent the real scope of these migration movements. This paper discusses the data quality and methodology of migration statistics in Georgia and we are going to answer the questions: what is the real reason of increasing immigration flows according to the official numbers since 2000s?

Keywords: Migration, Methodology, Data Quality, Georgia

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8 Introduction of Electronic Health Records to Improve Data Quality in Emergency Department Operations

Authors: Anuruddha Jagoda, Samiddhi Samarakoon, Anil Jasinghe

Abstract:

In its simplest form, data quality can be defined as 'fitness for use' and it is a concept with multi-dimensions. Emergency Departments(ED) require information to treat patients and on the other hand it is the primary source of information regarding accidents, injuries, emergencies etc. Also, it is the starting point of various patient registries, databases and surveillance systems. This interventional study was carried out to improve data quality at the ED of the National Hospital of Sri Lanka (NHSL) by introducing an e health solution to improve data quality. The NHSL is the premier trauma care centre in Sri Lanka. The study consisted of three components. A research study was conducted to assess the quality of data in relation to selected five dimensions of data quality namely accuracy, completeness, timeliness, legibility and reliability. The intervention was to develop and deploy an electronic emergency department information system (eEDIS). Post assessment of the intervention confirmed that all five dimensions of data quality had improved. The most significant improvements are noticed in accuracy and timeliness dimensions.

Keywords: electronic health records, Emergency Department, Data Quality, electronic emergency department information system

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7 Data Quality Enhancement with String Length Distribution

Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

Abstract:

Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: Feature selection, Data Quality, probability distribution, string classification, string length

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6 An Automated Approach to Consolidate Galileo System Availability

Authors: Marie Bieber, Fabrice Cosson, Olivier Schmitt

Abstract:

Europe's Global Navigation Satellite System, Galileo, provides worldwide positioning and navigation services. The satellites in space are only one part of the Galileo system. An extensive ground infrastructure is essential to oversee the satellites and ensure accurate navigation signals. High reliability and availability of the entire Galileo system are crucial to continuously provide positioning information of high quality to users. Outages are tracked, and operational availability is regularly assessed. A highly flexible and adaptive tool has been developed to automate the Galileo system availability analysis. Not only does it enable a quick availability consolidation, but it also provides first steps towards improving the data quality of maintenance tickets used for the analysis. This includes data import and data preparation, with a focus on processing strings used for classification and identifying faulty data. Furthermore, the tool allows to handle a low amount of data, which is a major constraint when the aim is to provide accurate statistics.

Keywords: Aerospace, Availability, Data Quality, system performance, Galileo

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5 Identifying Critical Success Factors for Data Quality Management through a Delphi Study

Authors: Maria Paula Santos, Ana Lucas

Abstract:

Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: Data Quality, critical success factors, Delphi, data quality management, Q-Sort

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4 Dataset Quality Index:Development of Composite Indicator Based on Standard Data Quality Indicators

Authors: Preecha Vichitthamaros, Sakda Loetpiparwanich

Abstract:

Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: Principal Component Analysis, Data Quality, Factor Analysis, composite indicator, data quality management, dataset quality

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3 The Quality Assessment of Seismic Reflection Survey Data Using Statistical Analysis: A Case Study of Fort Abbas Area, Cholistan Desert, Pakistan

Authors: M. A. Rashid, U. Waqas, M. F. Ahmed, A. Mehmood

Abstract:

In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data Quality, Null hypothesis, Seismic lines, Seismic reflection survey

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2 An Information Quality Framework for College and University Websites

Authors: Joseph Elliot, Daniel Berleant

Abstract:

Information Quality (IQ) of a college or university website plays a major role in the decision process for prospective students when selecting an institution for their higher education. Furthermore, current students and others rely on these websites for many other purposes. In this paper, we identify information quality dimensions for college and university website that are relevant to prospective and current students and other site users. We discuss the rationales for identifying these IQ dimensions and propose a University Website Information Quality (UWebIQ) framework to quantify the individual IQ dimensions as well as a strategy for defining the composite IQ for such a website. The outcome of this research is expected to provide insight for colleges and universities that wish to maximize the fitness for use of their websites.

Keywords: world wide web, Data Quality, Information Quality, dimensional IQ, composite IQ

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1 Improving the Statistics Nature in Research Information System

Authors: Rajbir Cheema

Abstract:

In order to introduce an integrated research information system, this will provide scientific institutions with the necessary information on research activities and research results in assured quality. Since data collection, duplication, missing values, incorrect formatting, inconsistencies, etc. can arise in the collection of research data in different research information systems, which can have a wide range of negative effects on data quality, the subject of data quality should be treated with better results. This paper examines the data quality problems in research information systems and presents the new techniques that enable organizations to improve their quality of research information.

Keywords: standardization, Data Quality, Data cleansing, Research information systems (RIS), research information, heterogeneous sources, science system

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