CC Corporate Data Quality (CC CDQ)

Data and information1 of high quality is not just a hygiene factor for business, but has turned into an asset for competitive advantage. In line with this, data must be carefully managed, thoughtfully governed, strategically used, and sensibly controlled. Excellent organizations recognize the importance of timely, accurate, and reliable data and accordingly treat data as an asset the same way they treat all other corporate assets (such as employees, patents, or manufacturing equipment, for example). The opposite, however, is also true; enterprises using only ad hoc data management practices find that important information gets locked in silos, reports are untrustworthy or practically useless, and vital processes depending on data often run incorrectly. Today's companies are establishing enterprise-wide data quality management as a corporate function in order to ensure smooth business operations provisioned withthe right data at the right time at a sufficient quality level. To support enterprises in their efforts, the Framework for Corporate Data Quality Management (CDQM) describes structures and activities that need to be built up and implemented for efficient and effective management of enterprise-wide data. The Framework has been published as a standard for master data and data quality management by the Competence Center Corporate Data Quality (CC CDQ) of the University of St. Gallen and the European Foundation of Quality Management (EFQM, see http://www.efqm.org). The Framework focuses on raising awareness of the topic and on giving guidance for establishing CDQM in organizations. What the Framework does not do, however, is providing guidelines or recommendations as to how corporate data quality management is supposed to be implemented from a technical point of view.This white paper aims at filling this gap, as it describes how the Framework for CDQM can be implemented using solutions and products which are part of SAP Solutions for Information Management. The white paper addresses both experienced practitioners (who need to expand their skills regarding SAP's Information Management domain) and practitioners who are new to managing, governing, and optimizing the use of data that has an impact on enterprise operations. This white paper can be used in several ways:- as a reference regarding practices and methods for establishing corporatewide data quality management,- as a guide to quickly identify specific products of SAP's Information Management portfolio and how these products support the implementation of the Framework for CDQM,- as a reference regarding a common terminology to be used by business and IT professionals.

The digitization of the economy and society requires enterprises from all industries to revisit their business models and prepare their organizations for the digital age. The design of "smart" products and services, the involvement of prosumers, and the intensifying interconnection of supply chains are signs of this transformation. Each of these scenarios builds on improved availability and interchangeability of data. In order to successfully transform their business and be able to develop valuable new services, companies require methodological help. To address this need, this paper proposes a service-capability design framework for digital businesses. The framework is developed theoretically based on the literature and earlier research and consists of a meta-model and a high-level reference model. The framework is retroactively applied to a real-world digital use case to demonstrate its validity.

Retaining customers is a relevant topic throughout all service industries. However, only limited attention has been directed towards studying the antecedents of subscription renewal in the context of operational cloud enterprise systems. Cloud services have historically been offered as subscription-based services with the (theoretical) possibility of seamless service cancellation, in contrast to classical IT-Outsourcing contracts or license-based software installations of on-premise enterprise systems. In this work, we investigate the central concept of subscription renewal by focusing on different facets of IS success and their relevance for distinct employee cohorts. Analyzing inter-cohort differences has strong practical implications, as it helps IT vendors to focus on specific IT-related factors when trying to retain customers. Therefore an empirical study was undertaken. The hypotheses were developed on an individual level and tested using survey responses of IT decision makers within companies which adopted cloud enterprise systems. Gathered data was then analyzed using PLS. The results show that subscription renewal intention of the strategic cohort is mainly based on perceived system quality, whereas information quality explains most of the variance of subscription renewal in the management cohort. Beneath the cloud enterprise systems specific contributions, the work adds to the theoretical body of research related to IS success and IS continuation, as well as stakeholder perspectives.

The Internet of Things, the continuing globalization of logistics networks, decreasing product life cycles and increasing numbers of product variants are examples of current developments that pose, in general, new requirements both on networked industries and on data in logistical systems. Volume, heterogeneity and importance of data for businesses are growing. In order to be able to manage data under these complexity constraints, networked industries need a current, comprehensive and consistent understanding of the data architecture, i.e. of the key data entities, their relationships, their sources of origin, trustworthiness, frequency of occurrence, quality and ownership. As current approaches for data architecture management in particular and data resource management in general fall short in providing support for this endeavor, the paper at hand proposes a morphology of the data resource in networked industries. The morphology is the result of a taxonomic analysis aiming at providing structure to complex data environments. The paper uses four case studies to identify and describe the dimensions of the morphology and its characteristics. Furthermore, the paper develops the baseline of a method for guiding the application of the morphology.

The management of master data (MDM) plays an important role for companies in responding to a number of business drivers such as regulatory compliance and efficient reporting. With the understanding of MDM's impact on the business drivers companies are today in the process of organizing MDM on corporate level. While managing master data is an organizational task that cannot be encountered by simply implementing a software system, business processes are necessary to meet the challenges efficiently. This paper describes the design process of a reference process model for MDM. The model design process spanned several iterations comprising multiple design and evaluation cycles, including the model's application in three participative case studies. Practitioners may use the reference model as an instrument for the analysis and design of MDM processes. From a scientific perspective, the reference model is a design artifact that represents an abstraction of processes in the field of MDM.

Buzzwords like big data, the Internet of Things, mobile computing, or Industry 4.0 all build on the conviction that the importance of data and information will keep growing both for businesses and for society as a whole. Data management departments need to revise their existing architectures and processes to get ready for the new require-ments, for example regarding data availability, data integration, and data credibility. The report builds on insights collected from the CC CDQ workshops and bilateral pro-jects taking place in 2014. It aims at providing data managers of medium and large enterprises from all industries with useful background information and practical guid-ance for their journey towards the digital economy. More precisely, the report - contributes to a common understanding of the major technological, economic and social drivers behind the evolution of the "digital economy", - specifies the implications the digital economy has on data management re-quirements, - shows how companies react to these new requirements in five short exemplary cases, - presents a business and data management capability framework for companies operating in the digital economy, and - describes a possible roadmap for data managers to follow on their company's journey towards digitization.