Assessing Data Quality - A Probability-based Metric for Semantic Consistency

We present a probability-based metric for semantic consistency using a set of uncertain rules. As opposed to existing metrics for semantic consistency, our metric allows to consider rules that are expected to be fulfilled with specific probabilities. The resulting metric values represent the probability that the assessed dataset is free of internal contradictions with regard to the uncertain rules and thus have a clear interpretation. The theoretical basis for determining the metric values are statistical tests and the concept of the p-value, allowing the interpretation of the metric value as a probability. We demonstrate the practical applicability and effectiveness of the metric in a real-world setting by analyzing a customer dataset of an insurance company. Here, the metric was applied to identify semantic consistency problems in the data and to support decision-making, for instance, when offering individual products to customers

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided

A data-driven decision-making model for the third-party logistics industry in Africa

Third-party logistics (3PL) providers have continued to be key players in the supply chain network and have witnessed a growth in the usage of information technology. This growth has enhanced the volume of structured and unstructured data that is collected at a high velocity, and is of rich variety, sometimes described as “Big Data”. Leaders in the 3PL industry are constantly seeking to effectively and efficiently mature their abilities to exploit this data to gain business value through data-driven decision-making (DDDM). DDDM helps the leaders to reduce the reliance they place on observations and intuition to make crucial business decisions in a volatile business environment. The aim of this research was to develop a prescriptive model for DDDM in 3PLs. The model consists of iterative elements that prescribe guidelines to decision-makers in the 3PL industry on how to adopt DDDM. A literature review of existing theoretical frameworks and models for DDDM was conducted to determine the extent to which they contribute towards DDDM for 3PLs. The Design-Science Research Methodology (DSRM) was followed to address the aim of the research and applied to pragmatically and iteratively develop and evaluate the artefact (the model for DDDM) in the real-world context of a 3PL. The literature findings revealed that the challenges with DDDM in organisations include three main categories of challenges related to data quality, data management, vision and capabilities. Once the challenges with DDDM were established, a prescriptive model was designed and developed for DDDM in 3PLs. Qualitative data was collected from semi-structured interviews to gain an understanding of the problems and possible solutions in the real-world context of 3PLs. An As-Is Analysis in the real-world case 3PL company confirmed the challenges identified in literature, and that data is still used in the 3PL company for descriptive and diagnostic analytics to aid with the decision-making processes. This highlights that there is still room for maturity into using data for predictive and prescriptive analytics that will, in turn, improve the decision-making process. An improved second version of the model was demonstrated to the participants (the targeted users), who had the opportunity to evaluate the model. The findings revealed that the model provided clear guidelines on how to make data-driven decisions and that the feedback loop and the data culture aspects highlighted in the design were some of the important features of the model. Some improvements were suggested by participants. A field study of three data analytics tools was conducted to identify the advantages and disadvantages of each as well as to highlight the status of DDDM at the real-world case 3PL. The limitations of the second version of the model, together with the recommendations from the participants were used to inform the improved and revised third version of the model.Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

A data-driven decision-making model for the third-party logistics industry in Africa

Third-party logistics (3PL) providers have continued to be key players in the supply chain network and have witnessed a growth in the usage of information technology. This growth has enhanced the volume of structured and unstructured data that is collected at a high velocity, and is of rich variety, sometimes described as “Big Data”. Leaders in the 3PL industry are constantly seeking to effectively and efficiently mature their abilities to exploit this data to gain business value through data-driven decision-making (DDDM). DDDM helps the leaders to reduce the reliance they place on observations and intuition to make crucial business decisions in a volatile business environment. The aim of this research was to develop a prescriptive model for DDDM in 3PLs. The model consists of iterative elements that prescribe guidelines to decision-makers in the 3PL industry on how to adopt DDDM. A literature review of existing theoretical frameworks and models for DDDM was conducted to determine the extent to which they contribute towards DDDM for 3PLs. The Design-Science Research Methodology (DSRM) was followed to address the aim of the research and applied to pragmatically and iteratively develop and evaluate the artefact (the model for DDDM) in the real-world context of a 3PL. The literature findings revealed that the challenges with DDDM in organisations include three main categories of challenges related to data quality, data management, vision and capabilities. Once the challenges with DDDM were established, a prescriptive model was designed and developed for DDDM in 3PLs. Qualitative data was collected from semi-structured interviews to gain an understanding of the problems and possible solutions in the real-world context of 3PLs. An As-Is Analysis in the real-world case 3PL company confirmed the challenges identified in literature, and that data is still used in the 3PL company for descriptive and diagnostic analytics to aid with the decision-making processes. This highlights that there is still room for maturity into using data for predictive and prescriptive analytics that will, in turn, improve the decision-making process. An improved second version of the model was demonstrated to the participants (the targeted users), who had the opportunity to evaluate the model. The findings revealed that the model provided clear guidelines on how to make data-driven decisions and that the feedback loop and the data culture aspects highlighted in the design were some of the important features of the model. Some improvements were suggested by participants. A field study of three data analytics tools was conducted to identify the advantages and disadvantages of each as well as to highlight the status of DDDM at the real-world case 3PL. The limitations of the second version of the model, together with the recommendations from the participants were used to inform the improved and revised third version of the model.Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202