An empirical investigation of factors influencing data quality improvement success

While some research has been done to identify the dimensions of data quality and to develop methodologies of improving particular aspects of data quality, the fundamental questions of these methodologies remain vague. This paper tries to fill this gap by empirically analyzing the factors influencing the success of data quality improvements. Hereto, we develop a model for data quality improvement success. This model is evaluated using survey data from 179 respondents. The significance of the model is computed using the maximum likelihood estimation of AMOS. The results show, that organizational implementation success is positively associated with perceived data quality, whereas no significant contribution of data quality projects to perceived data quality could be observed

Understanding data quality issues in dynamic organisational environments – a literature review

Technology has been the catalyst that has facilitated an explosion of organisational data in terms of its velocity, variety, and volume, resulting in a greater depth and breadth of potentially valuable information, previously unutilised. The variety of data accessible to organisations extends beyond traditional structured data to now encompass previously unobtainable and difficult to analyse unstructured data. In addition to exploiting data, organisations are now facing an even greater challenge of assessing data quality and identifying the impacts of lack of quality. The aim of this research is to contribute to data quality literature, focusing on improving a current understanding of business-related Data Quality (DQ) issues facing organisations. This review builds on existing Information Systems literature, and proposes further research in this area. Our findings confirm that the current literature lags in recognising new types of data and imminent DQ impacts facing organisations in today&rsquo;s dynamic environment of the so-called &ldquo;Big Data&rdquo;. Insights clearly identify the need for further research on DQ, in particular in relation to unstructured data. It also raises questions regarding new DQ impacts and implications for organisations, in their quest to leverage the variety of available data types to provide richer insights.<br /

Understanding Data Quality Issues in Dynamic Organisational Environments : A Literature Review

Technology has been the catalyst that has facilitated an explosion of organisational data in terms of its velocity, variety, and volume, resulting in a greater depth and breadth of potentially valuable information, previously unutilised. The variety of data accessible to organisations extends beyond traditional structured data to now encompass previously unobtainable and difficult to analyse unstructured data. In addition to exploiting data, organisations are now facing an even greater challenge of assessing data quality and identifying the impacts of lack of quality. The aim of this research is to contribute to data quality literature, focusing on improving a current understanding of business-related Data Quality (DQ) issues facing organisations. This review builds on existing Information Systems literature, and proposes further research in this area. Our findings confirm that the current literature lags in recognising new types of data and imminent DQ impacts facing organisations in today’s dynamic environment of the so-called “Big Data”. Insights clearly identify the need for further research on DQ, in particular in relation to unstructured data. It also raises questions regarding new DQ impacts and implications for organisations, in their quest to leverage the variety of available data types to provide richer insights

Assessing Quality of Unstructured Data – Insights From a Global Imaging Company

The main objective of this research to understand if previous Data Quality frameworks are still applicable in today’s organisational environment characterised by a wide variety of data types, including the unstructured data. The paper describes a pilot study conducted in a global imaging company with the researchers adopting and re-examining a previously developed data quality framework, used in a number of different research studies for more than a decade. The study focuses on two research questions: Are the existing data quality frameworks developed for highly structured data, still applicable to today’s organisational environment? Do users’ perceptions of data quality change depending on data type? The paper reports on the main findings and offers some suggestions for future research

Achieving data completeness in electronic medical records: A conceptual model and hypotheses development.

This paper aims at proposing a conceptual model of achieving data completeness in electronic medical records (EMR). For this to happen, firstly, we draw on the model of factors influencing data quality management to construct our conceptual model. Secondly, we develop hypotheses of relationships between influencing factors for data completeness and mediators for achieving data completeness in EMR based on the literature. Our conceptual model extends the prior model for factors influencing data quality management by adding a new factor and exploring the relationships between the influencing factors within the context of data completeness in EMR. The proposed conceptual model and the presented hypotheses once empirically validated will be the basis for the development of tools and techniques for achieving data completeness in EMR.N

Empirical Evaluation of the Influence of EMR Alignment to Care Processes on Data Completeness

Data completeness is an important dimension of data quality in electronic medical records (EMR). There are many constructs that influence data completeness in EMR. In this paper, we investigate three of these constructs: Clinical staff participation, EMR integration, and EMR alignment to care processes. We use these constructs from related studies as theoretical support to propose a conceptual model of factors influencing data completeness in EMR. The conceptual model is empirically validated using a survey with clinical staff participants. The results reveal that a high level of clinical staff’s participation influences the data completeness in EMR. Furthermore, the alignment of EMR to the care processes has an impact on the data completeness in EMR

Empirical study of Data Completeness in Electronic Health Records in China

Background: As a dimension of data quality in electronic health records (EHR), data completeness plays an important role in improving quality of care. Although many studies of data management focus on constructing the factors that influence data quality for the purpose of quality improvement, the constructs that are developed for interpreting factors influencing data completeness in the EHR context have received limited attention. Methods: Based on related studies, we constructed the factors influencing EHR data completeness in a conceptual model. We then examined the proposed model by surveying clinical practitioners in China. Results: Our results show that the data quality management literature can serve as a starting point to derive a conceptual model of factors influencing data completeness in the EHR context. This study also demonstrates that “resources” should be added as a factor that influences data completeness in EHR. Conclusion: Our resulting conceptual model shows a substantial explanation of data completeness in EHR assessed in this study. Although the proposed relationships between the included factors were previously supported in the literature, our work provides the beginning empirical evidence that some relationships may not be always significantly supported. The possible explanation of these differences has been discussed in the present research. This study thus benefits decision makers and EHR program managers in implementing EHR as well as EHR vendors in the EHR integration by addressing data completeness issues

The impact of information quality awareness on users\u27 behaviors toward information quality practices

Healthcare organization rely more on electronic information to optimize most of their processes. Additional information sources and more diverse information increase the relevance and importance of information quality (IQ). The quality of information needs to be improved to support a more efficient and reliable utilization of information systems (IS). This improvement can only be achieved through the implementation of initiatives followed by most users across the organization. The purpose of this study is to develop a model related to how awareness of IS users about IQ issues would affect their actual practices toward IQ initiatives. It is posited that users’ motivation is influenced by their awareness on beneficial and problematic situations generated by IQ. The motivation that users may have regarding IQ impact, will influence their behavior regarding IQ practices. Social influences and facilitating conditions are considered as moderators of the interaction between intention and actual users’ behavior