Hans-Georg Kemper

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.

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.

Datentransformation im Data Warehouse

Der folgende Beitrag beschaftigt sich mit der Problematik der Uberufhrung operativer in dispositive Datenbestande, die direkt fur Analytische Informationssysteme nutzbar sind. Die hierfur erforderlichen Transformationsprozesse werden in aufeinander aufbauende Filterungs-, Harmonisierungs-, Verdichtungs- sowie Anreicherungsaktivitaten unterschieden und durch entsprechende Praxisbeispiele illustriert.

A Concept for Extracting and Sharing Technical Data from Digital Product Models for Subsequent Processing

In order to develop new products, design engineers build digital product models leveraged by 3D-Computer-Aided-Design (CAD) systems. During design, they specify various aspects of the product: e.g., Which technical elements like wholes, pockets and materials the product consists of, or which tolerances have to be observed during production. This information is vital to stakeholders in subsequent processes like production planning and control as well as process engineering or quality management. Nowadays, the information is shared via the distribution of technical drawings, which implies various problems (e.g. Change of media, different CAD-systems in use by different organizational units, or unnecessary overhead to retrieve the required information from the model). In order to mitigate these problems, this paper proposes an approach to extract the information that is relevant to the stakeholders from various CAD-systems, and store this information in a vendor-neutral format, which other application systems or employees are able to process.

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.

Business Intelligence — Begriffsabgrenzung und Ordnungsrahmen

Im Mittelpunkt des ersten Kapitels stehen die Abgrenzung des Begriffes Business Intelligence (BI) und die Entwicklung eines BI-Rahmenkonzeptes, das den grundlegenden Ordnungsrahmen fur das vorliegende Werk bildet.

Transformation operativer Daten

Der folgende Beitrag beschaftigt sich mit der Problematik der Uberfuhrung operativer in dispositive Datenbestande, die direkt fur Analytische Informationssysteme nutzbar sind. Die hierfur erforderlichen Transformationsprozesse werden in aufeinander aufbauende Filterungs-, Harmonisierungs-, Aggregations- sowie Anreicherungsaktivitaten unterschieden und durch entsprechende Praxisbeispiele illustriert.

The customer-centric data warehouse - an architectural approach to meet the challenges of customer orientation

This paper presents research-in-progress. An extensive customer-centric data warehouse architecture should enable both complex analytical queries as well as standard reporting queries on customer data without performance restrictions for both requirements. This paper introduces a dichotomic approach, which brings together these contradicting tasks of a data warehouse. On the one hand, it elaborates on the qualities of customer data and their implications on data structures due to their change over time. The authors will present a data concept, that is specialized in gaining a realistic image of the customer, ideally over her entire customer lifecycle. On the other hand, the paper works out the role of the operational data store (ODS) in the light of CRM: it presents how the ODS supports its counterpart, the data warehouse, in the dichotomic approach for the maintenance of high performance and low response times.

Integration von Big Data-Komponenten in die Business Intelligence

Stichwörter Analytics Big Data Business Intelligence Data Warehouse Volume, Variety, Velocity Der betriebliche Einsatz von Big Data wird häufig ohne Bezug zum Themenkomplex Business Intelligence (BI) diskutiert. Der folgende Beitrag arbeitet heraus, wieso für betriebswirtschaftlich orientierte Big Data-Anwendungen eine Integration in einen BI-Kontext sinnvoll ist und in welchen Bereichen sich die beiden Konzepte ergänzen. Die Ausführungen werden anhand von Beispielen aus dem Controlling veranschaulicht.