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Managing Data as an Asset with the Help of Artificial Intelligence

Document type: White paper | Published: 2019
Authors: Prof. Dr. Christine Legner, Martin Fadler

This white paper summarizes insights from research on data management conducted by the Competence Center Corporate Data Quality (CC CDQ) in collaboration with SAP SE.

In the first part, the whitepaper explains what it means for companies to manage Data as an Asset. The second part it outlines how artificial intelligence (AI) will fundamentally impact and change the way data is managed. In the third part, the white paper offers guidance on how the digital platform and other solutions from SAP support companies in leveraging AI/ML for data management.

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Research Article: Understanding Data Protection Regulations from a Data Management Perspective: A Capability-Based Approach to EU-GDPR

Understanding Data Protection Regulations from a Data Management Perspective: A Capability-Based Approach to EU-GDPR

Document type: Research article | Published: 2019
Authors: Prof. Dr. Christine Legner, Clément Labadie

The European General Data Protection Regulation has entered into force in May 2018. However, organizations face difficulties when implementing EU-GDPR due to a lack of common ground between legal and data management domains. The paper advances the regulatory compliance management literature by translating legal data protection concepts for the information systems community.

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Working report: Data Catalogs: Integrated platforms for matching data supply and demand

Data Catalogs: Integrated platforms for matching data supply and demand

Document type: Working report | Published: 2018
Authors: Prof. Dr. Christine Legner, Prof. Dr. Boris Otto, Tobias Korte, Markus Spiekermann, Martin Fadler

Data catalogs have been a focus of research activities in the Competence Center Corporate Data Quality since 2018. The reference model and a market study have been developed in collaboration with researchers from Fraunhofer ISST and many data management experts. They help companies in assessing Data Catalog solutions available on the market and choosing the one most suitable to meet their specific needs.

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PMI's Journey Towards a Data-Driven Enterprise

Document type: Case study | Published: 2018
Authors: Prof. Dr. Christine Legner, Tobias Pentek, Martin Fadler

This work report summarizes PMI’s journey towards a data-driven enterprise and illustrates how offensive and defensive aspects of a data strategy work hand-in-hand. The case study provides interesting insights and can inspire other companies that aim at becoming data-driven enterprises.

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Data Protection from a data management perspective: The case of GDRP

Document type: Work report | Published: 2018
Authors: Prof. Dr. Christine Legner, Clément Labadie

The Competence Center Corporate Data Quality developed a GDPR Capability Model to help practitioners identify areas for action and structure projects and efforts. It provides an action-oriented view on the capabilities that need to be built in order to comply with GDPR’s complete set of requirements. It is based on a review and interpretation of legal texts, official guidelines and industry practice reports. The capabilities defined in this GDPR Capability Model cover both organizational processes and technical measures.

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Book: Corporate Data Quality: Prerequisite for Successful Business Models

Corporate Data Quality: Prerequisite for Successful Business Models

Document type: Book (PDF) | Published: 2015
Authors: Prof. Dr. Boris Otto, Prof. Dr. Huber Österle

Data is the strategic resource of the 21st century. Trends such as digitalization, industry 4.0 and social media are also contributing to the fact that data management has become a core capability for successful companies of this time. This book presents a holistic approach to the management of master data in a high quality manner and is aimed at both practitioners and academics.

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CDQ Trend Study: Where data management is heading

CDQ Trend Study: Where data management is heading

Document type: Study | Published: 2017
Authors: Prof. Dr. Christine Legner, Tobias Pentek, Dr. Martin Ofner, Clément Labadie

The CDQ Trend Study aims at providing an understanding of the goals of data management and capturing current as well as future activities of companies. It presents insights gathered from an expert survey conducted with experienced data management professionals reflecting various industry backgrounds.

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Data Excellence Model: Short Description and Basic Terminology

Data Excellence Model: Short Description and Basic Terminology

Document type: Framework | Published: 2017
Authors: Prof. Dr. Christine Legner, Tobias Pentek

In a joint effort, the Competence Center Corporate Data Quality, comprising more than 15 European companies as well as researchers from three European universities, has developed a reference model for data management in the digital economy: The path="/data-excellence-model" title="Data Excellence Model - Data Management Framework" linktext="Data Excellence Model"Data Excellence Model.

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EFQM Framework for Corporate Data Quality

EFQM Framework for Corporate Data Quality

Document type: Framework | Published: 2016
Authors: EFQM, Competence Center Corporate Data Quality

This document describes the Framework for Corporate Data Quality Management. This Framework supports organizations in the assessment and analysis of remedies for missed opportunities and unexploited potentials of Corporate Data Quality Management

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Towards a Reference Model for Data Management in the Digital Economy

Towards a Reference Model for Data Management in the Digital Economy

Document type: Research paper | Published: 2017
Authors: Prof. Dr. Boris Otto, Prof. Dr. Christine Legner, Tobias Pentek

The digital and data-driven economy requires enterprises from all industries to revisit their existing data management approaches. To address the changing and broader scope of data management activities in the digital economy, this research in progress paper proposes a reference model, that describes the design areas of data management.

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Assessing the Economic Value of Data Assets

Assessing the Economic Value of Data Assets

Document type: Work report | Published: 2016
Authors: Andreas Zechmann

Intangible assets have become more and more important for organizations in recent years. Whereas data management in terms of data quality is widely considered by existing DQM reference models, data management regarding the financial dimension of data is still insufficiently addressed. The work report presents the conceptual design of the two valuation methods and pro-vides guidance for their application.

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Business and Data Management Capabilities for the Digital Economy

Business and Data Management Capabilities for the Digital Economy

Document type: White paper | Published: 2015
Authors: Prof. Dr. Boris Otto, Dr. Dimitrios Gizanis, Rieke Bärenfänger

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. The report aims at providing data managers of medium and large enterprises from all industries with useful background information and practical guidance for their journey towards the digital economy.

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Corporate Data Quality: Voraussetzung erfolgreicher Geschäftsmodelle

Corporate Data Quality: Voraussetzung erfolgreicher Geschäftsmodelle [GERMAN]

Document type: Book (PDF) | Published: 2016
Authors: Prof. Dr. Boris Otto, Prof. Dr. Huber Österle

Daten sind die strategische Ressource des 21. Jahrhunderts. Trends wie die Digitalisierung, Industrie 4.0 und Social Media tragen ebenfalls dazu bei, dass Datenmanagement zu einer Kernkompetenz für erfolgreiche Unternehmen dieser Zeit geworden ist. Dieses Buch zeigt einen ganzheitlichen Ansatz zum qualitätsbewussten Management von Stammdaten auf und richtet sich damit sowohl an Praktiker als auch an die Wissenschaft.

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Master Data erfolgreich managen

Master Data erfolgreich managen [GERMAN]

Document type: Article of a specialized magazine | Published: 2016
Authors: Prof. Dr. Boris Otto, Prof. Dr. Christine Legner

In Zeiten digitaler Geschäftsmodelle und Industrie 4.0 steigt die Bedeutung von Stammdaten für den Geschäftserfolg. Unternehmen müssen sie als Kernressource begreifen, die es zu bewirtschaften gilt. Drei Unternehmen gelang dies zuletzt besonders gut. Aus ihren Erfahrungen lassen sich wesentliche Erfolgsfaktoren für ein professionelles Stammdaten-Management ableiten.

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