Information quality assessment and effects on inventory decision-making

Information quality has become a critical concern to the success of organisations. Numerous business initiatives have been delayed or even cancelled, citing poor-quality information as the main reason. Previous research indicated that under-standing the effects of information quality is critical to the success of organisations. However, little research has been done to analyse the effects of information quality in an organisational context. In order to address this drawback, the objective of this thesis is to systematically analyse the effects of information quality on decision-making. In order to achieve this objective, we propose a practical assessment framework that allows us to measure information quality dimensions and categories. This framework was validated using a real-world database. With the help of this assessment framework we gained in-depth insights into the effects of information quality on decision quality. Our results showed that the categories of intrinsic and contextual information quality are positively related to decision quality. However decision quality is not significantly affected by representational information quality. It is also found that in contrast to consistency, increasing information accuracy and completeness can significantly improve decision quality. From our results we concluded that not all the aspects of information quality are equally effective for the improvement of decision quality. Decision-makers could decide to pay little or no attention to the improvement of representational information quality and information consistency. This finding will directly reduce the cost of information quality improvement. For practical implementations, our results concluded a validated framework that allows software engineers to implement assessment. Comparing our framework with other common frameworks that require soft-ware engineers to understand information quality theory, our framework helps soft-ware engineers to follow a step-by-step procedure to build an application of information quality assessment. It will directly increase software engineers' work efficiency

Challenges of Teaching Information Quality: Demonstrating an Adaptation of a Popular Management Game in Teaching Information Quality

Over the last years information quality has gained increasingly importance in practice as well as academia. Recently aspects of information quality are included in many Information Systems’ curricula. However, teaching aspects of information quality to students is challenging and often emphasizes merely theoretical aspects. As a consequence many graduates have a limited understanding of information quality issues and management practice. In order to help to raise the awareness of information quality aspects, we developed a teaching tool that can demonstrate the impact of poor information quality and the importance of information quality management. The tool is based on a popular management game and can show the effects of information quality on organizational decision-making

INFORMATION QUALITY ASSESSMENT: VALIDATING MEASUREMENT DIMENSIONS AND PROCESSES

Over the last two decades information quality has emerged as a critical concern for most organisations. Foremost research provides several approaches to measure information quality and many case studies constantly illustrate the difficulties in assessing information quality. In this paper, we tackle the problem of assessing information quality and we propose a framework to implement information quality assessment in practice. Our framework incorporates two major components: a set of valid measurement dimensions and a measurement process. We have tested the validity, reliability and usefulness of the dimensions and applied the measurement process to an example dataset. In addition, our study demonstrates typical information quality problems in the example dataset and their potential impact to organisations

Data quality problems in TPC-DI based data integration processes

Many data driven organisations need to integrate data from multiple, distributed and heterogeneous resources for advanced data analysis. A data integration system is an essential component to collect data into a data warehouse or other data analytics systems. There are various alternatives of data integration systems which are created in-house or provided by vendors. Hence, it is necessary for an organisation to compare and benchmark them when choosing a suitable one to meet its requirements. Recently, the TPC-DI is proposed as the first industrial benchmark for evaluating data integration systems. When using this benchmark, we find some typical data quality problems in the TPC-DI data source such as multi-meaning attributes and inconsistent data schemas, which could delay or even fail the data integration process. This paper explains processes of this benchmark and summarises typical data quality problems identified in the TPC-DI data source. Furthermore, in order to prevent data quality problems and proactively manage data quality, we propose a set of practical guidelines for researchers and practitioners to conduct data quality management when using the TPC-DI benchmark

Predicting Data Quality Success - The Bullwhip Effect in Data Quality

Over the last years many data quality initiatives and suggestions report how to improve and sustain data quality. However, almost all data quality projects and suggestions focus on the assessment and one-time quality improvement, especially, suggestions rarely include how to sustain the continuous data quality improvement. Inspired by the work related to variability in supply chains, also known as the Bullwhip effect, this paper aims to suggest how to sustain data quality improvements and investigate the effects of delays in reporting data quality indicators. Furthermore, we propose that a data quality prediction model can be used as one of countermeasures to reduce the Data Quality Bullwhip Effect. Based on a real-world case study, this paper makes an attempt to show how to reduce this effect. Our results indicate that data quality success is a critical practice, and predicting data quality improvements can be used to decrease the variability of the data quality index in a long run

Classification Methodology for Architectures in Information Systems: A Statistical Converging Technique

Architectures are critical to the Information System (IS) domain because they represent funda- mental structures and interactions of systems. Since analysing architecture similarities is chal- lenging and time-consuming even in one domain, IS architecture classifications are paramount to understanding architectural complexity. However, classification approaches used in existing research commonly rely on manual interventions, and thus architectural classification reliability is hampered. We propose a novel methodology based on component modelling and applica- tion of a statistical converging technique, which ensures reliable IS architectural classification and minimises subjective interventions. We demonstrate the methodology by classifying data warehouse architectures

Limitations of Weighted Sum Measures for Information Quality

In an age dominated by information, information quality (IQ) is one of the most important factors to consider for obtaining competitive advantages. The general approach to the study of IQ has relied heavily on management approaches, IQ frameworks and dimensions. There are many IQ measures proposed, however dimensions in most frameworks are analyzed and assessed independently. Approaches to aggregate values have been discussed, by which foremost research mostly suggests to estimate the overall quality of information by total all weighted dimension scores. In this paper, we review the suitability of this assessment approach. In our research we focus on IQ dependencies and trade-offs and we aim at demonstrating by means of an experiment that IQ dimensions are dependent. Based on our result of dependent IQ dimensions, we discuss implications for IQ improvement. Further research studies can build on our observations

Exploiting food choice biases for healthier recipe recommendation

By incorporating healthiness into the food recommendation / ranking process we have the potential to improve the eating habits of a growing number of people who use the Internet as a source of food inspiration. In this paper, using insights gained from various data sources, we explore the feasibility of substituting meals that would typically be recommended to users with similar, healthier dishes. First, by analysing a recipe collection sourced from Allrecipes.com, we quantify the potential for finding replacement recipes, which are comparable but have different nutritional characteristics and are nevertheless highly rated by users. Building on this, we present two controlled user studies (n=107, n=111) investigating how people perceive and select recipes. We show participants are unable to reliably identify which recipe contains most fat due to their answers being biased by lack of information, misleading cues and limited nutritional knowledge on their part. By applying machine learning techniques to predict the preferred recipes, good performance can be achieved using low-level image features and recipe meta-data as predictors. Despite not being able to consciously determine which of two recipes contains most fat, on average, participants select the recipe with the most fat as their preference. The importance of image features reveals that recipe choices are often visually driven. A final user study (n=138) investigates to what extent the predictive models can be used to select recipe replacements such that users can be "nudged'' towards choosing healthier recipes. Our findings have important implications for online food systems

Industrial Involvement in Information System Education: Lessons Learned from a Software Quality Course

As Information System (IS) development is closely related to industry and real-world applications, industrial involvement is a critical element in IS education. This paper studies one typical IS course - a Software Quality course, and reflects our experience with involving a mix of industrial experts in building a practical IS course that would increase students’ competences in critical thinking about the consequences of the design and quality engineering decisions that they are making during software development. In the course design, the industrial experts are involved in lecturing, hands-on-exercise seminars and final student evaluation. We find that students are showing active course participation with our designed industrial involvement. Furthermore, we summarize lessons learned from the industry involvement, as well as the reflections on the value perceived by the industrial experts involved in the IS education