Improving the quality of process reference models: A quality function deployment-based approach

Little academic work exists on managing reference model development and measuring reference model quality, yet there is a clear need for higher quality reference models. We address this gap by developing a quality management and measurement instrument. The foundation for the instrument is the well-known Quality Function Deployment (QFD) approach. The QFD-based approach incorporates prior research on reference model requirements and development approaches. Initial evaluation of the instrument is carried out with a case study of a logistic reference process. The case study reveals that the instrument is a valuable tool for the management and estimation of reference model quality

In order to fully realise the value of open data researchers must first address the quality of the datasets

There has been a phenomenal increase in the availability of data over the last decade. Open data is provided as a means of empowering users with information and in the hope of sparking innovation and increased efficiency in governments and businesses. However, in spite of the many success stories based on the open data paradigm, concerns remain over the quality of such datasets. Marta Indulska and Shazia Sadiq argue that in order to facilitate more effective and efficient realisation of value from open data, research must reach a shared consensus on the definition of data quality dimensions, provide methods and guidelines for assessing the potential usefulness of open datasets using exploratory tools and techniques, and develop rigorous theoretical underpinnings on effective use of open data

Do Process Modelling Techniques Get Better? A Comparative Ontological Analysis of BPMN

Current initiatives in the field of Business Process Management (BPM) strive for the development of a BPM standard notation by pushing the Business Process Modeling Notation (BPMN). However, such a proposed standard notation needs to be carefully examined. Ontological analysis is an established theoretical approach to evaluating modelling techniques. This paper reports on the outcomes of an ontological analysis of BPMN and explores identified issues by reporting on interviews conducted with BPMN users in Australia. Complementing this analysis we consolidate our findings with previous ontological analyses of process modelling notations to deliver a comprehensive assessment of BPMN

Business Process Management: Saving the Planet?

Organisational and government concerns about environmental sustainability (ES) are on the increase. While a significant amount of research from a wide range of domains has addressed various ES challenges, intuitively, Business Process Management (BPM), with its focus on process improvement and process performance measurement, has much to offer the ES field. In this paper we aim to understand the BPM research contribution to ES, including specific Environmental Performance Indicators (EPI), and the BPM concepts that have been utilized in the ES context. To this end we conduct a systematic literature review to capture prior research focused on BPM and ES, coding the articles according to their contribution to EPIs and other ES concepts, while also contrasting their focus with main challenges identified in industry reports. Our study identifies which EPIs have been addressed in prior BPM research and highlights areas of future contribution

Compliance Centric Data Quality Management – The Banking and Financial Industry Perspective

The banking and financial industry is subject to a large number of regulations, including several recent data quality regulations, which makes it difficult for organisations to judge the effort and cost involved in compliance to multiple, often overlapping, regulatory obligations. The aim of this paper is to identify and analyse data quality related regulations, and map them against a common set of data quality dimensions to expose overlaps and inconsistencies to inform compliance efforts. In our study we identify seven global data quality regulations/frameworks applicable to the banking and financial industry and conduct a systematic analysis of data quality stipulations within. Our study explores the breadth and depth of coverage of data quality dimensions in the regulations, and identifies inherent overlaps and inconsistencies. We argue that understanding of data quality requirements within and across the regulations is an essential first step towards the design of new approaches for compliance centric data quality management

Business Process Management and Environmental Sustainability – Insights from the Hospitality Industry

Environmental sustainability (ES) is a source of competitive advantage for organizations. However, from socio-technical systems (STS) perspective, a multitude of complex factors is involved in transforming organizations towards environmental sustainability. To facilitate such transformation, all components of an STS, including people, processes, systems and technology, need to be considered. With processes at the core of STS functionality, organizational transformations towards sustainable practices should consider business process management (BPM) as an enabler. ES in BPM research is an emerging topic. To better understand ES objectives, practices, and challenges from a process-oriented perspective, we look to the hospitality industry given it is one of the largest business sectors world-wide. Specifically, in this paper we report on a qualitative study that aims to identify ES efforts and challenges in the Australian hotel industry, exploring the extent to which a process orientation exists to assist with these efforts. Findings indicate an absence of process-oriented initiatives in relation to ES in this context

Perceptions and Challenges of EHR Clinical Data Quality

Despite the premise of better data, Electronic Health Record (EHR) data quality remains problematic. Traditional approaches for improving data quality through semantic and syntactic controls have not resolved the problems. To use the medical vernacular – “we have addressed the symptoms but not the cause.” This paper reports on an exploratory study undertaken in a large maternity hospital with an aim to expose detractors from high-quality data in EHRs. The study involved a perceptions survey that was completed by Nursing and Midwifery staff; chosen because of known data quality challenges in their area of practice. The study results indicate social, cultural and environmental aspects of information systems (IS) use are equally as problematic as the IS itself. A lack of agreement amongst healthcare practitioners surrounding what data quality means is also evident, with time, culture and lacking formal education on data quality being contributors to lower data quality outcome

Effectiveness of Domain Ontologies to Facilitate Shared Understanding and Cross-Understanding

Shared cognition constructs such as shared understanding and cross-understanding are important factors in team performance. Although research has focused on understanding the effects of these constructs, little emphasis has been placed on improving their development. In Information Systems and related fields shared understanding of a domain is said to be facilitated by the use of a domain ontology, however there is a lack of empirical evidence to support this claim. Accordingly, in this research-in-progress paper, we report our efforts to develop a deep understanding of the benefits of domain ontology use at the group level. Specifically, we propose a model that theorizes the relationships between domain ontology use and the development of shared understanding and cross-understanding of domains. Additionally, we provide details of operationalization and empirical validation of our model, and the current state of this research

Practical Significance Of Key Data Quality Research Areas

The body of knowledge on data and information quality is highly diversified, primarily due to the cross-disciplinary nature of data quality problems, coupled with a strong focus on fitness for use principle in developing data quality solutions. As a result, research and practice in data and information quality is characterized by methodological as well as topical diversity. Although research pluralism is highly warranted, there is evidence that substantial developments in the past have been isolationist. As a first step towards bridging gaps between various communities involved in data quality research and practice, we undertook a literature review of data quality research published in a range of Information System (IS) and Computer Science (CS) publication outlets and identified the key themes of research from last 20 years. In this paper, we utilize the above results to explore the impact of these themes within the data quality professional community. To that end, we developed an initial model of data quality factors (based on the identified key research themes), and conducted a survey of data quality practitioners to test the model. Our study found that the effective implementation of data quality assessment practices, data quality frameworks, and data constraints and rules, has a significant impact on overall data quality levels in organizations, whereas focus on other factors do not appear to significantly affect data quality. Results from this study can assist organizations in prioritising their data quality initiatives to focus on the factors that have the potential to contribute most significantly to overall data and information quality