Data Management Publications

Data Strategy Canvas

Canvas for Defining the Building Blocks of a Data Strategy

Document type: Template | Published: 2019
Author: Prof. Dr. Christine Legner, Tobias Pentek

The data strategy canvas helps you to define the key elements of your data strategy. The document is printable and created in DIN A0 format. You can use the data strategy canvas in workshops together with data managers and business experts to develop the key elements of your data strategy.

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The Data Value Formula

Document type: E-Book | Published: 2020
Authors: Prof. Dr. Christine Legner, Martin Fadler

Today, access to valuable data is a superpower. But just having a lot of data is not enough. In their recent e-book, CDQ data experts from the Competence Center Corporate Data Quality (CC CDQ) explain how to turn data into business value using a simple model – the Data Value Formula.

The formula has three elements: Data Volume, Data Quality and Data Use. To showcase the data value formula and its practical application, we invite you to read the complementary e-book.

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In 4 Schritten zu mehr Wert aus Daten [GERMAN]

Document type: E-Book | Published: 2021
Authors: Dr. Tobias Pentek, Prof. Dr. Christine Legner, Martin Fadler

Wie jede Reise beginnt auch der Weg zu mehr Wert aus Daten mit ersten Schritten. In diesem Leitfaden erläutern wir zunächst die Motivation für und die Herausforderungen auf diesem Weg.

Mithilfe der Daten-Wertschöpfungskette stellen wir einen Ord­nungsrahmen vor. Darauf aufbauend empfehlen wir vier Schritte, die ein unternehmensweites Verständnis für den Wert und die Potenziale von Daten erzeugen, erste Erfolge ermöglichen und die Grundlagen für einen langfristig erfolgreichen Weg schaffen.

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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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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.

 Request publication now at SAP Get publication at the CC Wiki

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.

 Purchase at Fraunhofer-Bookstore Get publication at the CC Wiki

Data Documentation for Data Catalogs: Metadata model and attributes

Document type: Working report | Published: 2020
Author: Prof. Dr. Christine Legner, Dr. Markus Eurich, Clément Labadie

Enterprises that are engaging in digital transformation need to empower an increasing number of data consumers to work with data. A prerequisite is data documentation – data assets should be inventoried and well-described to facilitate data selection by non-data experts, who need to both find and understand them. This research paper proposes a reference model for data documentation in the enterprise context.

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FAIR Enough? Enhancing the Usage of Enterprise Data with Data Catalogs

Document type: Case study | Published: 2020
Author: Prof. Dr. Christine Legner, Dr. Markus Eurich, Clément Labadie, Martin Fadler

With increasing relevance of data as a strategic asset, companies strive to make data FAIR, i.e. findable, accessible, interoperable and reusable. Data catalogs are considered an important means to realize these aspirations.

We propose a taxonomy of data catalog initiatives and present 3 detailed case studies that illustrate typical approaches to data catalogs. Our findings contribute to the ongoing discourse on the FAIR principles by elaborating on their significance in the enterprise context and analyzing their operationalization by means of data catalogs.

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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 Data Excellence Model.

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Data Excellence Model Template

Document type: Template | Published: 2019
Author: Tobias Pentek

The template contains the Data Excellence Model (DXM) and a description of the corresponding goals, enablers and results.

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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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Konsortialforschung zur Entwicklung von Referenzmodellen für die Digitalisierung von Unternehmen - Erfahrungen aus dem Datenmanagement [GERMAN]

Document type: Study | Published: 2020
Author: Prof. Dr. Christine Legner, Tobias Pentek

This article illustrates how consortium research - as a multilateral, institutionalized collaboration between researchers and practitioners - facilitates knowledge transfer and the rigorous development of reference models.

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

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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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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EFQM Framework for Corporate Data Quality Management

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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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.

 Get publication now Get hardcover at Amazon

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 [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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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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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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Framework for the Generation and Documentation of Open Data Use Cases

Document type: Working report | Published: 2019
Authors: Prof. Dr. Christine Legner, Pavel Krasikov, Matthieu Harbich, Markus Eurich

This report is based on the research activities of Innosuisse project "Open Data App Store" and documents the research results related to the discovery of open data. It clarifies the definition of "open data", develops a framework for the generation and documentation of open data use cases, and uses it to identify promising open data use cases for the business context.

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Open Data Use Cases Overview

Document type: Presentation | Published: 2019
Author: Pavel Krasikov

This document shows open data use cases in the business environment. It includes seven business scenarios applicable in scopes of marketing & sales, supply chain management, business partner risk management, and finally, data management. Open data can support existing business processes and result in such benefits as cost savings, additional revenues, and risk mitigation.

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