Strategic data management

From a product-centric to a data-centric organization.

Our scope of services

For data utilization across companies, a company first requires strategic data management. This data management should bundle mission critical decisions for data topics and formulate these across companies. New types of organization development (such as uniform data governance or data architecture) or new trends such as data ecosystems will have a lasting effect on a company. The strategic positioning of data management enables the sustainable alignment of data domains, data roles and data applications..

At Fraunhofer ISST, the components of strategic data management are researched, in which relevant data capabilities of a defensive and offensive company orientation are addressed. The aim is to establish and optimize data organizations while taking strategic premises and options into account. Developing a data strategy, for example, determines the prerequisite for participation in the data ecosystem or decides the nature of the data lifecycle alignments across the organization. A data organization is established using data governance approaches, which are guaranteed by means of decentralized and/or central company units and suitable data roles such as data owners and data stewards. In this context, data registration processes are implemented to enable business-compliant data usage. The implementation of a data organization increases the data quality and the usability of AI applications, reduces data search processes and improves the introduction of data applications.
 

Integrated approach to developing strategic data management with the Fraunhofer ISST toolbox
Figure 1: Integrated approach to developing strategic data management with the Fraunhofer ISST toolbox

The services offered by Fraunhofer ISST include data strategy positioning, performing data assessments, selecting suitable data governance approaches, developing role and process models and extend to the implementation of data strategy concepts.

 

Data strategy

  • Analysis of relevant data trends (such as data spaces, data productization)
  • Strategic positioning of data management in the internal and external corporate environment
  • Derivation of data capabilities of the organization
  • Development of defensive and offensive attributes to align the data strategy
  • Strategic integration with the business strategy using a target system, development plan and key performance indicators

 

Data governance

  • Development and selection of a suitable data governance organizational model to determine central and decentralized responsibilities
  • Analysis and determination of suitable data roles according to tasks, competencies and responsibilities (AKV principle)
  • Process model development based on the relevant data capabilities

 

Industries

Strategic data management contributes to solving demanding challenges in various industries. Whether as a toolbox in automobile production, as a framework in medical technology or as an organizational model in the transport sector, data management as a strategic cornerstone has positive effects on the time to launch a product on the market and the introduction of new applications.

 

 

Hier finden Sie eine Auswahl von freigegebenen Anwendungsbeispielen aus dem Kompetenzfeld »Strategisches Datenmanagement« der vergangenen Jahre. Sie suchen weitergehende Informationen? Nehmen Sie einfach Kontakt mit uns auf – unsere Ansprechpartner stehen Ihnen gerne für Fragen und Gespräche zur Verfügung.

Example 1:

Industrial data management at VW

Fraunhofer ISST is developing an integrated and stringent concept for industrial data management within the project framework. Here we first record relevant customer processes on site. This bottom-up approach allows concrete needs for action to be derived. Subsequently, a data strategy is developed through the interplay of workshops and conceptual work. This framework describes the objectives for architecture rules and the data governance model. The guidelines for the structured handling of data are thereby described from the organizational perspective. At this juncture, we work out what roles are entrusted with what activities in working with data.

Internal project page

 

Example 2:

Diagnosis data management at Thales

Based on the big picture of a data strategy that defines the essential principles of how data is identified and managed in an entrepreneurial manner, the responsibilities of data-related roles and the responsibilities for existing corporate roles are to be defined. A data governance model takes into account whether there is a need for a physical or virtual data governance organization. As part of the project, Fraunhofer ISST is developing a concept for data governance in collaboration with Thales in workshops and additional work.

Internal project page

 

List of scientific publications

GÜR, I., M. SPIEKERMANN, M. ARBTER. und B.OTTO, 2021. Data Strategy Development: A Taxonomy for Data Strategy Tools and Methodologies in the Economy. 16th International Conference on Wirtschaftsinformatik, Essen-Duisburg

HUPPERZ, M., I. GÜR, F. MÖLLER und B. OTTO, 2021. What is a Data-Driven Organization? In: Proceedings of Americas Conference on Information Systems. Montreal

GÜR, I., T. GUGGENBERGER und M. ALTENDEITERING, 2021. Towards a Data Management Capability Model. In: Proceedings of Americas Conference on Information Systems. Montreal

LIS, D. and B. OTTO, 2020. Data Governance in Data Ecosystems – Insights from Organizations. In: Proceedings of Americas’ Conference on Information

Systems, Salt Lake City

LIS, D. and B. OTTO, 2021. Towards a Taxonomy of Ecosystem Data Governance. In: Proceedings of the 54th Hawaii International Conference on System Sciences. Hawaii