Data Strategy Canvas

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 canvas in workshops together with data managers and business experts to develop the key elements of your data strategy.

The Data Value Formula

Prof. Dr. Christine Legner, Martin Fadler
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.

In 4 Schritten zu mehr Wert aus Daten [GERMAN]

Dr. Tobias Pentek, Prof. Dr. Christine Legner, Martin Fadler
Wie Sie ein unternehmensweites Verständnis für den Wert und die Potenziale von Daten erzeugen, erste Erfolge ermöglichen und die Grundlagen für eine erfolgreiche Nutzung schaffen.

PMI's Journey Towards a Data-Driven Enterprise

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.

Managing Data as an Asset with the Help of Artificial Intelligence

Prof. Dr. Christine Legner, Martin Fadler
What it means for companies to manage data as an asset, and how artificial intelligence (AI) will fundamentally impact and change the way data is managed.
Exclusive for CC CDQ members

Data Catalogs: Integrated platforms for matching data supply and demand

Prof. Dr. Christine Legner, Prof. Dr. Boris Otto, Tobias Korte, Markus Spiekermann, Martin Fadler
The reference model and a market study help companies in assessing Data Catalog solutions available on the market and choosing the one most suitable to meet their specific needs.
Exclusive for CC CDQ members

Data Documentation for Data Catalogs: Metadata model and attributes

Prof. Dr. Christine Legner, Dr. Markus Eurich, Clément Labadie
This research paper proposes a reference model for data documentation in the enterprise context to facilitate data selection also by non-data experts.
Exclusive for CC CDQ members

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

Prof. Dr. Christine Legner, Dr. Markus Eurich, Clément Labadie, Martin Fadler
Our publication proposes a taxonomy of data catalog initiatives and presents 3 detailed case studies that illustrate typical approaches to data catalogs.

Data Excellence Model: Short Description and Basic Terminology

Prof. Dr. Christine Legner, Tobias Pentek
Short description and basic terminology of the research-based reference model for managing corporate data assets.

Data Excellence Model Template

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

Towards a Reference Model for Data Management in the Digital Economy

Prof. Dr. Boris Otto, Prof. Dr. Christine Legner, Tobias Pentek
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.
Exclusive for CC CDQ members

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

Prof. Dr. Christine Legner, Tobias Pentek
This data management publication illustrates how consortium research facilitates knowledge transfer and the rigorous development of reference models.

Understanding Data Protection Regulations from a Data Management Perspective

Prof. Dr. Christine Legner, Clément Labadie
The paper advances the regulatory compliance management literature by translating legal data protection concepts for the information systems community.

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

Prof. Dr. Christine Legner, Clément Labadie
The GDPR Capability Model provides an action-oriented view on the capabilities that need to be built in order to comply with GDPR’s complete set of requirements.

Assessing the Economic Value of Data Assets

Andreas Zechmann
The work report presents the conceptual design of two data management valuation methods and provides guidance for their application.

EFQM Framework for Corporate Data Quality Management

EFQM, Competence Center Corporate Data Quality
Framework for the assessment and analysis of remedies for missed opportunities and unexploited potentials in Corporate Data Quality Management.

Corporate Data Quality: Prerequisite for Successful Business Models

Prof. Dr. Boris Otto, Prof. Dr. Huber Österle
A holistic approach to the management of master data in a high quality manner for both practitioners and academics.

Corporate Data Quality: Voraussetzung erfolgreicher Geschäftsmodelle [GERMAN]

Prof. Dr. Boris Otto, Prof. Dr. Huber Österle
Dieses Buch zeigt einen ganzheitlichen Ansatz zum qualitätsbewussten Management von Stammdaten auf und richtet sich damit sowohl an Praktiker als auch an die Wissenschaft.

Master Data erfolgreich managen [GERMAN]

Prof. Dr. Boris Otto, Prof. Dr. Christine Legner
In Zeiten digitaler Geschäftsmodelle und Industrie 4.0 steigt die Bedeutung von Stammdaten für den Geschäftserfolg. Aus den Erfahrungen von drei Unternehmen lassen sich wesentliche Erfolgsfaktoren für ein professionelles Stammdaten-Management ableiten.

Business and Data Management Capabilities for the Digital Economy

Prof. Dr. Boris Otto, Dr. Dimitrios Gizanis, Rieke Bärenfänger
The report provides data managers from all industries with useful background information and practical guidance for their journey towards the digital economy.

CDQ Trend Study: Where data management is heading

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.

Framework for the Generation and Documentation of Open Data Use Cases

Prof. Dr. Christine Legner, Pavel Krasikov, Matthieu Harbich, Markus Eurich
The report clarifies the definition of "open data" and develops a framework for the generation and documentation of open data use cases for the business context.

Open Data Use Cases Overview

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.