11 research outputs found

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

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    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

    A pattern based approach for data quality requirements modelling

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    CHROMA model for the information-driven decision-making process

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    The strong, progressive interaction between decision-making processes (DMP) and information technologies has led to breakthroughs in how business is conducted. These developments represent the advent of significant trends for data-driven DMP in terms of increased competitive advantages and business opportunities. However, there is still a gap between technological capabilities and organizational needs due to the fact that the adoption of technology solutions in many companies is faster than their capacity to adapt at the managerial level. Balancing this situation implies a process of self-recognition in which aspects that need to be addressed for the application of better analytical practices must be highlighted. Such evaluation is necessary to embrace more rigorously the use of data and analytics insights within organizations attempting to become information-driven companies. This thesis presents an evaluation methodology that is based on the foundations of maturity models and provides a framework for assessing and ranking the level of organizations' proficiency regarding their information-driven DMP. In this vein, the “Circumplex Hierarchical Representation of Organization Maturity Assessment” (CHROMA) model and its variant, “Simplified Holistic Approach to DMP Evaluation” (SHADE), which is applied to small and medium-sized enterprises (SMEs), provide a novel and holistic approach that embraces the most relevant aspects at the technological and management level to make more objective and better supported decisions. In this respect, the key factors that influence making better-informed decisions are grouped into 5 dimensions: data availability, data quality, data analysis & insights, information use, and decision-making. Both the CHROMA model with its 5X5X5 structure (5 dimensions subdivided into 5 attributes, each classifiable into 5 proficiency levels) and its SHADE variant with a 5X3X5 structure, were conceived to be applied in an organized and systematic way in accordance with this structure in order to characterize the organization’s use of information in DMPs from an uninitiated stage to a completely embedded one. In this sense, its application consists of a methodology that involves interviewing key company personnel plus a brief web questionnaire, and the subsequent evaluation of the dimensions and attributes of the model. Both models were tested in a field study campaign in six family-run SMEs, which were deployed in two blocks. In the first block, three SMEs were analyzed through the application of the CHROMA model. In the second block, the SHADE version of the CHROMA model was applied to the other three SMEs that collaborated with the study. This field study campaign was very significant in terms of reaching a deeper understanding of the extent to which organizations are supporting their decisions with information obtained from data analysis and their willingness to improve accordingly. The findings indicate that, overall, data quality problems are the biggest challenge facing organizations. Moreover, data analysis remains limited, reactive and timid, is mainly focused on senior management and middle managers, and is very scarce at operational levels. Despite this, the findings in the “decision-making” dimension demonstrate that these organizations have, to some extent, been able to leverage their available data to support their decisions. These results confirm that both models are useful for collecting relevant and firsthand information through a close and personalized treatment to consequently identify strengths and weaknesses of specific aspects, thus providing a broader view that leads companies to prioritize improvement actions that could have a meaningful impact on the success and growth of the organization.La fuerte y progresiva interacción existente entre el proceso de toma de decisiones (DMP) y las tecnologías de información (IT) ha conllevado a un gran avance que ha repercutido en la forma en que los negocios son conducidos. Estos avances han representado el advenimiento de tendencias significativas para el DMP impulsado por datos en términos de mayores ventajas competitivas y oportunidades de negocio. Sin embargo, existe aún una brecha entre las capacidades tecnológicas y las necesidades de la organización debido a que la adopción de soluciones tecnológicas conducidas por datos en muchas compañías es más rápida que su capacidad de adaptarse a nivel gerencial. Equilibrar este desbalance implica un proceso de auto-reconocimiento donde sean resaltados los aspectos que requieren ser atendidos para la aplicación de mejores prácticas analíticas. Tal evaluación es necesaria dentro de las organizaciones que intentan dar un uso más riguroso a sus datos y conocimientos analíticos para convertirse en compañías impulsadas por información. Esta tesis presenta una metodología de evaluación que basada en los fundamentos de los modelos de madurez proporciona un marco para evaluar y categorizar el nivel de competencia de las organizaciones en el DMP impulsado por información. En tal sentido, el modelo “Circumplex Hierarchical Representation of OrganizationMaturity Assessment” (CHROMA) y su variante “SimplifiedHolistic Approach to DMP Evaluation” (SHADE) para pequeñas y medianas empresas, ofrecen un enfoque novedoso y holístico que abarca los aspectos más relevantes a nivel tecnológico y de gestión para tomar decisiones más objetivas y mejor soportadas, en orden de hacer frente a esta situación. Al respecto, estos factores que influyen en la toma de decisiones mejor informada son agrupados en 5 dimensiones: disponibilidad de datos, calidad de datos, análisis de datos e insights, uso de la información y toma de decisiones. Tanto el modelo CHROMA con su estructura 5£5£5 (5 dimensiones subdivididas en 5 atributos clasificables en 5 niveles de aptitud) como su variante SHADE de estructura 5£3£5, fueron concebidos para ser aplicados de una forma estructurada y sistemática en concordancia con dicha estructura, en orden de caracterizar el uso de la información en el DMP de la organización desde una etapa no iniciada a una completamente embebida. En este orden de ideas, su aplicación consiste de una metodología que involucra realizar entrevistas a personal clave de la compañía más un breve cuestionario web, y la posterior evaluación de las dimensiones y atributos del modelo. Ambos modelos fueron probados en una campaña de estudios de campo en seis empresas familiares pymes, los cuales fueron desplegados en dos bloques. En el primer bloque, fueron analizadas tres pymes a través de la aplicación del modelo CHROMA. En el segundo bloque, se procedió a aplicar el modelo SHADE de CHROMA a las otras tres pymes que colaboraron con el estudio. Esta campaña de estudios de campo resultó muy significativa en términos de alcanzar una comprensión más profunda del grado en el cual las organizaciones están tomando decisiones impulsadas en la información resultante del análisis de datos y su disposición a mejorar en consecuencia. Los hallazgos señalan que, en términos generales, los problemas de calidad de datos constituyen el mayor desafío al que se enfrentan las organizaciones. Asimismo, el análisis de datos continúa siendo limitado, reactivo y poco audaz, principalmente concentrado en la alta gerencia y mandos intermedios, siendo muy escaso a niveles operativos. A pesar de esto, los hallazgos en la dimensión “toma de decisiones” demuestran que estas organizaciones, en cierta medida, han logrado aprovechar sus datos disponibles para soportar sus decisiones. Los resultados confirman que ambos modelos son útiles para recolectar información relevante y de primera mano a través de un trato cercano y personalizado para consecuentemente identificar fortalezas y debilidades de aspectos específicos, proporcionando así una visión más amplia que conduzca a las compañías a priorizar acciones de mejora, que podrían significar el éxito y crecimiento de la organización

    Process-Oriented Information Logistics: Aligning Process Information with Business Processes

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    During the last decade, research in the field of business process management (BPM) has focused on the design, modeling, execution, monitoring, and optimization of business processes. What has been neglected, however, is the provision of knowledge workers and decision makers with needed information when performing knowledge-intensive business processes such as product engineering, customer support, or strategic management. Today, knowledge workers and decision makers are confronted with a massive load of data, making it difficult for them to discover the information relevant for performing their tasks. Particularly challenging in this context is the alignment of process-related information (process information for short), such as e-mails, office files, forms, checklists, guidelines, and best practices, with business processes and their tasks. In practice, process information is not only stored in large, distributed and heterogeneous sources, but usually managed separately from business processes. For example, shared drives, databases, enterprise portals, and enterprise information systems are used to store process information. In turn, business processes are managed using advanced process management technology. As a consequence, process information and business processes often need to be manually linked; i.e., process information is hard-wired to business processes, e.g., in enterprise portals associating specific process information with process tasks. This approach often fails due to high maintenance efforts and missing support for the individual demands of knowledge workers and decision makers. In response to this problem, this thesis introduces process-oriented information logistics(POIL) as new paradigm for delivering the right process information, in the right format and quality, at the right place and the right point in time, to the right people. In particular, POIL allows for the process-oriented, context-aware (i.e., personalized) delivery of process information to process participants. The goal is to no longer manually hard-wire process information to business processes, but to automatically identify and deliver relevant process information to knowledge workers and decision makers. The core component of POIL is a semantic information network (SIN), which comprises homogeneous information objects (e.g., e-mails, offce files, guidelines), process objects (e.g., tasks, events, roles), and relationships between them. In particular, a SIN allows discovering objects linked with each other in different ways, e.g., objects addressing the same topic or needed when performing a particular process task. The SIN not only enables an integrated formal representation of process information and business processes, but also allows determining the relevance of process information for a given work context based on novel techniques and algorithms. Note that this becomes crucial in order to achieve the aforementioned overall goal of this thesis

    Designing Cross-Company Business Intelligence Networks

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    Business Intelligence (BI) ist der allgemein akzeptierte Begriff für Methoden, Konzepte und Werkzeuge zur Sammlung, Aufbereitung, Speicherung, Verteilung und Analyse von Daten für Management- und Geschäftsentscheidungen. Obwohl unternehmensübergreifende Kooperation in den vergangenen Jahrzehnten stets an Einfluss gewonnen hat, existieren nur wenige Forschungsergebnisse im Bereich unternehmensübergreifender BI. Die vorliegende Arbeit stellt eine Arbeitsdefinition des Begriffs Cross-Company BI (CCBI) vor und grenzt diesen von gemeinschaftlicher Entscheidungsfindung ab. Auf Basis eines Referenzmodells, das existierende Arbeiten und Ansätze verwandter Forschungsbereiche berücksichtigt, werden umfangreiche Simulationen und Parametertests unternehmensübergreifender BI-Netzwerke durchgeführt. Es wird gezeigt, dass eine Peer-To-Peer-basierte Gestaltung der Netzwerke leistungsfähig und kompetitiv zu existierenden zentral-fokussierten Ansätzen ist. Zur Quantifizierung der Beobachtungen werden Messgrößen geprüft, die sich aus existierenden Konzepten zur Schemaüberführung multidimensionaler Daten sowie Überlegungen zur Daten- und Informationsqualität ableiten oder entwickeln lassen.Business Intelligence (BI) is a well-established term for methods, concepts and tools to retrieve, store, deliver and analyze data for management and business purposes. Although collaboration across company borders has substantially increased over the past decades, little research has been conducted specifically on Cross-Company BI (CCBI). In this thesis, a working definition and distinction from general collaborative decision making is proposed. Based on a reference model that takes existing research and related approaches of adjacent fields into account a peer-to-peer network design is created. With an extensive simulation and parameter testing it is shown that the design proves valuable and competitive to centralized approaches and that obtaining a critical mass of participants leads to improved usefulness of the network. To quantify the observations, appropriate quality measures rigorously derived from respected concepts on data and information quality and multidimensional data models are introduced and validated

    Preface

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    The experience of stigma in people with inflammatory bowel disease with or without incontinence:a hermeneutic phenomenological study - A thesis submitted in partial fulfilment of the University’s requirements for the Degree of Doctor of Philosophy September, 2014

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    A stigma is ‘an attribute that is deeply discrediting,’ often contravening social norms, and perceived by others as being undesirable. Inflammatory Bowel Disease (IBD) is a chronic illness characterised by symptoms of diarrhoea, urgency, and vomiting occurring in a relapsing and remitting pattern. Regular or temporary loss of bowel control is a prominent feature of the disease and may lead to stigmatisation through infringement of social hygiene rules. Although stigma in IBD has been measured in quantitative studies, there is a dearth of qualitative evidence. This Heideggerian (hermeneutic) phenomenological study explores the lived experience of IBD-related stigma. Using purposive stratified sampling, 40 members of a national IBD charity were recruited. Participants did or did not experience faecal incontinence, and did or did not feel stigmatised. Unstructured individual interviews (digitally recorded and professionally transcribed) took place in consenting participants’ homes between May and November 2012. Data were analysed using Diekelmann’s hermeneutic method. Seven relational themes (present in some transcripts) and three constitutive patterns (present in all transcripts) emerged. IBD-related stigma is a complex experience, mostly of anticipated or perceived stigma, which often decreases over time. Stigma changes according to social settings and relationships, but arises from the challenges the disease presents in maintaining social privacy and hygiene rules. Stigma resilience appears most likely in those with a positive sense of control, a support network (particularly of close and intimate others) which suits their needs, and mastery over life and illness. IBD-related stigma occurs regardless of continence status and can cause emotional distress. Time, experience, and robust social support enhance stigma resilience. Further research is needed to confirm features which enable resilience, and to develop stigma-reduction strategies that will promote resilience in this patient group
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