Information resources management

In nahezu allen grossen Organisationen existieren eine Vielzahl verschiedener und haufig sehr heterogener Informationssysteme, die — in der Regel transaktionsorientiert — die Abwicklung der Geschaftsprozesse gewahrleisten; man spricht deshalb von operativen Informationssystemen. Sich verandernde Geschaftsmodelle und neue Aufgabenstellungen im Bereich der Daten-und Informationsversorgung machen es erforderlich, dass vorhandene Datenquellen auch integriert, d.h. unabhangig von ihrem primaren, operativen Einsatzzweck fur dispositive Zwecke genutzt werden konnen. Die Zielvorstellungen reichen von einer einheitlichen Kundensicht bis hin zu schnell verfugbaren Fuhrungsinformationen. Da es aus verschiedenen Grunden (z.B. fehlendes integriertes Datenmodell, Sicherheits/Zugangskonzepte, Laufzeitprobleme) kaum moglich ist, eine nur virtuelle Datenintegration „auf“ den Transaktionssystemen zu realisieren, bietet sich eine dreischichtige Architektur an, in der operative Systeme einerseits und dispositive Systeme andererseits durch eine Zwischenschicht bzw. eine eigens dafur bereitstehende zentrale Datenbasis voneinander entkoppelt sind (vgl. Abb. 1).

Data Warehousing: Nutzungsaspekte, Referenzarchitektur und Vorgehensmodell

Web 2.0 as a contemporary phenomenon receives considerable attention by IS scholars due to its perceived transformational impact on businesses. This paper critically elaborates on the value creation potential of Web 2.0 for small and medium enterprises (SME). By conducting an inductive study we reveal that SMEs can effectively use Web 2.0 as a means to support customer acquisition, alleviate resource limitations and to maintain customer enthusiasm associated with the customer purchasing process. In case that a high customer convenience is required which is based on the involvement of different parties or on personal service support, there is hardly any Web 2.0 value creation potential. This research contributes to the domain of business modeling by assessing the notion of value in a Web 2.0 setting. It also contributes to IS research on Web 2.0 adoption.

Value creation potential of web 2.0 for sme — insights and lessons learnt from a european producer of consumer electronics

E*Trade revolutionized the securities brokerage industry by creating Internet trading. E*Trades original strategy was to deliver cost savings to customers while amortizing fixed costs over a greater number of accounts. In 1997, several competitors established Internet sites and E*Trade was dethroned as the price leader. Its management team introduced a strategic initiative to transform the company into a financial, one-stop shop for investors. The initiative included expanding its information technology, improving its marketing and advertising program, and developing new strategic alliances. By early 1999, E*Trade had established a popular Web site offering the convenience and control of automated stock, options, and mutual fund order placement at low commission rates. E*Trades success pleased management but was challenged by fierce competition and emerging ethical and operational problems.

E*Trade Securities, Inc., Pioneer On-Line Trader, Struggles to Stay on Top

The goal of a data warehouse system is to provide a comprehensive overview of the data available in a company, thereby supporting the management decisions. The integration of data coming from heterogeneous sources is one of the key problems in data warehousing. The technical foundations for the integration have been developed in recent years. However, an efficient technical infrastructure is not sufficient to address the following problems. Firstly, the data in the systems involved have different semantics. Secondly, there are different user requirements regarding the quality of data. Existing systems are unable to solve these problems. The present thesis supports the development of data warehouse systems paying special attention to the problems regarding semantics and data quality. The approach is based on the explicit modelling of meta data of data warehouse systems. In particular, the conceptual context, the quality requirements, and the quality characteristics of the individual system components are represented in a formal model. The main contributions of the present thesis are, firstly, an extended meta model of the architecture and processes of a data warehouse system and, secondly, a quality model for the systematic representation of quality requirements and measurements. Furthermore, a classification of quality dimensions and factors is developed that can be used for an extensive quality management in data warehouse systems. The meta data is applied in a model for quality management as well as in a methodology for quality-oriented data integration. The methodology developed in this work uses the meta data by combining different existing approaches to data integration. The results of the present work are validated in various case studies in industrial contexts and in international research projects.

http://dbis.rwth-aachen.de/cms/staff/quix/quix-diss

Metadatenverwaltung zur qualitätsorientierten Informationslogistik in Data-Warehouse-Systemen

Die Datenqualitat und Flexibilitat einer Data Warehouse-Umgebung sowie die Konsistenz und der Integrationsgrad der in dieser enthaltenen Daten werden allgemein zu den Hauptfaktoren fur ein langfristig erfolgreiches Data Warehousing gezahlt (vgl. Martin 1998, S. 31). Alle diese Erfolgsfaktoren werden massgeblich von der Art und Weise der Versorgung des Data Warehouse mit Daten aus den operativen Systemen beeinflusst. Der Teilprozess des Data Warehousing, der diese Datenversorgung leistet, wird als ETL-Prozess bezeichnet.

Der ETL-Prozess des Data Warehousing

Managers of large data warehousing projects often put project failure down to organizational resistance. Technical requirements are usually not considered to be crucial for project success. However, the majority of the scientific work on data warehousing concentrates on technical aspects. As a consequence, a comprehensive framework or method for the introduction of a data warehouse is still missing. This paper takes this contradiction into account and deals with the management of organizational risks in large scale data warehouse projects. We base our research on information gathered from large organizations which develop and/or run data warehouses.This paper is structured as follows: After a short introduction the planning process of data warehousing is outlined. The first step is the strategic decision for data warehousing which is followed by the definition and evaluation of the initial project which is also called first increment. In the third section we present a business case strategy for both the initial project and subsequent data mart projects. Furthermore, the interdependencies between the initial phase and subsequent projects are discussed.

Deploying the business case to reduce the risk of large scale data warehousing

Ein Data Warehouse ist eine komplexe Anordnung von verschiedenartigen Soft-und Hardwarekomponenten. Diese wiederum bettet sich in ein Gesamtgefuge ein, das u. a. von unterschiedlichen Benutzertypen (Endbenutzer, Entwickler, Administratoren) gepragt ist. Auf konzeptioneller Ebene wird dieses Gesamtgefuge durch Architekturen, Prozess-und Datenmodelle, organisatorische Konzepte u. a. beschrieben (vgl. Abb. 1)

Integriertes Metadatenmanagement — Ein Überblick

Managing the Organizational Risk of Large Scale Data Warehousing

Unter Electronic Commerce (EC) wird allgemein jede Art von Handel verstanden, bei dem der Kaufer eines Produkts oder einer Dienstleistung einen Computer benutzt, um mit dem Computer des Verkaufers dieses Produkts oder dieser Dienstleistung zu interagieren (Ambler 1997, S. 2). Das beginnt schon mit der Suchphase des Kaufers. So fallen auch jene World Wide Web (WWW)-Angebote unter den Begriff EC, die die Interaktion auf die Moglichkeiten zur Abfrage statischer Web-Seiten beschranken.

Stefan Schwarz

Papers

Der ETL-Prozess des Data Warehousing

Deploying the business case to reduce the risk of large scale data warehousing

Integriertes Metadatenmanagement — Ein Überblick

Managing the Organizational Risk of Large Scale Data Warehousing

Die Eignung des Electronic Commerce für Klein- und Mittelunternehmen