Henning Baars

Management Support with Structured and Unstructured Data-An Integrated Business Intelligence Framework

Abstract In the course of the evolution of management support towards corporate wide Business Intelligence infrastructures, the integration of components for handling unstructured data comes into focus. In this paper, three types of approaches for tackling the respective challenges are distinguished. The approaches are mapped to a three layer BI framework and discussed regarding challenges and business potential. The application of the framework is exemplified for the domains of Competitive Intelligence and Customer Relationship Management.

Evaluation of RFID applications for logistics: a framework for identifying, forecasting and assessing benefits

As with all information technologies, there is a necessity to determine the profitability of investments in Radio Frequency Identification (RFID) ex ante. A particularly important aspect is the challenging task of evaluating the multi-faceted benefits of RFID deployments. While a large body of research on RFID benefits exists, our literature review indicates the absence of a comprehensive approach. We introduce a framework that combines the benefit evaluation steps of identification, forecasting and assessment. Based on insights gained in a 3-year research project with case studies in logistics, we refine a process-based IT-benefits classification and subsequently derive six types of RFID benefits that support the systematic identification of benefits, as well as the selection of forecast and assessment methods. We discuss how our framework can facilitate and enhance RFID investment decisions and guide future research activities.

Decision support for partially moving applications to the cloud: the example of business intelligence

Cloud computing services have evolved to a sourcing option that promises a wide range of benefits, such as increased scalability and flexibility at reduced costs. However, many enterprise applications are subject to strict requirements -- e.g. regarding privacy, security and availability -- and are embedded into complex enterprise IT architectures with a multitude of interdependencies. For these reasons, many decision makers have developed a sceptical stance towards cloud computing. A solution might be a hybrid (local/cloud infrastructure) approach where only suited components are migrated to a cloud infrastructure. This, however, has significant architectural consequences that need to be taken into account. This contribution suggests a cloud migration framework that will be implemented as an IT-based decision support system based on modelling the interdependencies between components. The approach is illustrated with the example of Business Intelligence (BI), i.e. integrated approaches to management support. The underlying decision model would particularly consider data transfer volumes, performance, sensitivity of cloud based data repositories, as well as exposure to public networks. The potential of such an approach is illustrated with a selected set of BI scenarios. Based on this, conclusions are derived and generalised for approaches taking into account deployments on both the local premises and cloud infrastructures.

Shaping the Next Incarnation of Business Intelligence

The body of knowledge generated by Business Intelligence (BI) research is constantly extended by a stream of heterogeneous technological and organizational innovations. This paper shows how these can be bundled to a new vision for BI that is aligned with new requirements coming from socio-technical macro trends. The building blocks of the vision come from five research strings that have been extracted from an extensive literature review: BI and Business Process Management, BI across enterprise borders, new approaches of dealing with unstructured data, agile and user-driven BI, and new concepts for BI governance. The macro trend of the diffusion of cyber-physical systems is used to illustrate the argumentation.The realization of this vision comes with an array of open research questions and requires the coordination of research initiatives from a variety of disciplines. Due to the embedded nature of the addressed topics within general research areas of the Information Systems (IS) discipline and the linking pins that come with the underlying Dynamic Capabilities Approach such research provides a contribution to IS.

An Integrated Business Intelligence Framework

Information Technology (IT) support in the manufacturing sector has reached a watershed with digital components beginning to permeate all products and processes. The classical divide between “technical” IT and “business” IT begins to blend more and more. Data from design, manufacturing, product use, service, and support is made available across the complete product lifecycle and supply chain. This goes hand in hand with the diffusion of sensor and identification technology and the availability of relevant information streams on the customer side—leading to unprecedented amounts of data. The challenge is to purposefully apply emerging BI concepts for a comprehensive decision support that integrates product and shop floor design phases, the steering and design of operational industrial processes, as well as big and unstructured data sources. This chapter brings those pieces together in order to derive an integrated framework for management and decision support in the manufacturing sector.

Multidimensional Analysis of RFID Data in Logistics

In the domain of logistics, Radio Frequency Identification (RFID) promises a plethora of benefits due to an enhanced efficiency, accuracy, and preciseness of object identification. Many of the far-reaching solutions proposed in the literature are based on data exchange, integration, and analysis. However, the respective applications have so far not been thoroughly scrutinized regarding relevance and design. This paper specifically explores options for modeling and utilizing multidimensional data sets for analytical applications as known from the realm of “Business Intelligence” (BI). Based on case studies, analysis scenarios are derived and discussed with respect to characteristic requirements for the purposeful utilization of BI tools. This is the foundation for the design of a multidimensional data model and for the construction of a prototype based on Online Analytical Processing (OLAP). The prototype not only visualizes possible applications and their business potential, its construction also leads to findings regarding the gathering and integration of RFID data.

Business Intelligence and Performance Management: Introduction

Globalisation, volatile markets, legal changes and technical progress have an immense impact on business environments in most industries. More and more IT is deployed to manage the complexity. As a result, companies and organisations have to handle growing volumes of data which have become a valuable asset. The ability to benefit from this asset is increasingly essential for business success. Therefore, fast storage, reliable data access, intelligent information retrieval, and new decision-making mechanisms are required. Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges. Before important aspects of both topics are analysed from different points of view, this chapter gives an introduction to concepts and terms of BI and PM.

Distribution von Business-Intelligence-Wissen

Im folgenden Beitrag wird ein Ansatz zur Verbreitung von Wissen aus Business-Intelligence-Analysen entwickelt und diskutiert. Relevant ist hierbei nicht nur Wissen in Form konkreter Analyseergebnisse sondern auch Wissen um die zielfuhrende Durchfuhrung von Analysen. Zur effizienten Weitergabe derartigen „Best-Practice-Wissens“ wird empfohlen, dieses in „Analysetemplates“ zu hinterlegen, d. h. in Vorlagen fur die Durchfuhrung verschiedener gleich strukturierter Analysen. Zur systematischen Weitergabe von Ergebnisberichten und Templates bieten sich Systeme aus dem Wissensmanagement mit Document- und Content-Management-Funktionalitat an. Fur die notwendige Verzahnung von Business-Intelligence- und Wissensmanagement-Systemen wird eine Middleware-Losung vorgeschlagen, die als Drehscheibe zwischen den unterschiedlichen Losungen fungiert.

A Framework for Identifying and Selecting Measures to Enhance BI-Agility

Fueled by increasingly complex and dynamic business environments, agility has risen to a strategic priority in many industries. The need to efficiently react not only to expected developments but also to unforeseen events requires responsive decision support approaches -- an agile Business Intelligence (BI). A puzzling variety of measures has been proposed for pursuing this aim -- from modified data modeling methods, over process models, up to a tool-based support of end-user development. Based on the results of a series of expert interviews with 25 companies, this paper develops a framework for a) identifying relevant measures for tackling issues of BI agility and for b) classifying them with respect to potential business impact and costs. The framework has been evaluated and fleshed out in workshops with BI experts.

Moving Business Intelligence to cloud environments

Business Intelligence systems use information technology to supply integrated management support with data coming from several sources of structured and unstructured data. The integrated infrastructures of Business Intelligence (BI) are often too complex and hence costly and inflexible. A solution for these issues is to leverage cloud computing services to enhance legacy BI systems and applications with cost-efficient increased scalability and flexibility. However, the migration of BI systems to cloud environments is usually hindered by strict requirements regarding privacy, security, or availability and a multitude of interdependences with other systems. In this paper, we describe the challenges in the adoption of BI within cloud environments and propose a cloud migration framework to assist decision makers in taking into account the consequences of the migration of BI systems to cloud environments as well as the impact of privacy, security, cost, and performance in so doing.