Strategic data management

Consistent use of data as the key to success in a data-driven world

The increasing company-wide use of data requires a change in strategy when dealing with data. Success-critical decisions and automated processes are based on reliable data and structures. Strategic data management develops the necessary structures for data organization. The strategic positioning of the data organization enables the sustainable alignment of data domains, data roles and data applications.

We want you to be able to make optimum use of your valuable data resources. That is why we at Fraunhofer ISST offer customized solutions for sustainable data management. We strengthen your data competencies and work with you to develop a data strategy that is precisely tailored to your corporate goals.

Our research focus

Fraunhofer ISST develops the components of strategic data management that ensure the success of data-driven innovations. The goal of strategic data management is the introduction and optimization of an internal data organization to realize data democratization. The establishment of a data organization increases data quality and the usability of AI applications, reduces data search processes and improves the introduction of data applications. Within this framework, the necessary data capabilities are developed, sustainably established and continuously measurable. The data organization is based on a data strategy that defines long-term requirements, such as the prerequisites for participation in data ecosystems or the type of data storage. The data organization is based on these requirements and integrates them into the data governance approaches, which are ensured by means of decentralized and/or centralized company units and suitable data roles such as data owners and data stewards. For efficient implementation of the workflows, the concepts are realized in data catalogs and data quality software and rolled out company-wide.

Fraunhofer ISST offers customized solutions for long-term sustainable data management. Our interoperable IT solutions and concepts increase the efficiency of manufacturing and logistics processes.

The focus of our range of services is on the optimized use of existing and future data resources. By strengthening data competencies and data management approaches, we facilitate the use of AI, the introduction of new data applications and increase the efficiency of data searches. Our offering includes the following services:

Data maturity measurement

The analysis of the current state of data management begins with a data maturity assessment. Through expert interviews with selected stakeholders in the company, six central building blocks with a total of 26 different characteristics are identified. This forms the basis for the development of an individual data governance organization model.

Data Governance

The services in the area of data governance include the development and selection of a suitable organizational model. The aim is to develop role and process models with the definition of central and decentralized responsibilities. This is accompanied by the introduction of data roles according to the task-competence-responsibility (TCC) principle, as well as the development of process models based on relevant data capabilities. This creates a solid foundation for effective governance structures within the company.

Data strategy and data culture

The strategic positioning of data management in the internal and external corporate environment takes place in the area of data strategy and data culture. The derivation of data capabilities, structured according to the TOP principle (technology, organization, people), enables dovetailing with the business strategy through data-related target systems, development plans and KPIs. The transformation to a sustainable data culture is achieved through data awareness workshops, the establishment of data principles and the systematic development of data skills. A target strategy with a roadmap is developed on the basis of the benefit analysis.

Tool Landscape

At the implementation level, there is the option of actively implementing a tailored proof of concept for the introduction of a data catalog. In addition, a detailed assessment can be carried out to select suitable data quality software. These steps ensure that the tools are optimally tailored to the specific requirements of data management.

Get started with us!

Would you like to make greater use of data as a strategic resource, ensure quality, enable integration and make data-driven decisions?

Then get in touch with us! 

Ten added values through strategic data management

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

ALTENDEITERING, M. und T. GUGGENBERGER, 2021. Designing Data Quality Tools: Findings from an Action Design Research Project at Boehringer Ingelheim. Twenty-Ninth European Conference on Information Systems (ECIS 2021), Marrakesh

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

Lis et al. (2023). Data Strategy and Policies. In: Caballero, I., Piattini, M. Data Governance: From Fundamentals to Real Cases. Springer International Publishing (Verlag).

Lipovetskaja, A., Haße, H. & Bukowski, D. (2023). Strategisches Datenmanagement: Der Schlüssel zur Digitalen Transformation. ERP Management.

Jahnke, N., Otto, B. Data Catalogs in the Enterprise: Applications and Integration. Datenbank Spektrum 23, 89–96 (2023).

Gür, I., Möller, F., Hupperz, M., Uzun, D., & Otto, B. (2022, June). Requirements for DataOps to foster Dynamic Capabilities in Organizations-A mixed methods approach. In 2022 IEEE 24th Conference on Business Informatics (CBI) (Vol. 1, pp. 166-175). IEEE.

Lis, D., & Arbter, M. (2022). Data Governance als Hebel für datengetriebene Wertschöpfung – Der Weg zu einer datengetriebenen Organisation. ERP Management 3/2022. Gito Verlag.

Altendeitering, M., & Tomczyk, M. (2022). A functional taxonomy of data quality tools: Insights from science and practice.

Lis et al. (2022). An Investigation of Antecedents for Data Governance Adoption in the Rail Industry – Findings from a Case Study at Thales. IEEE Transactions on Engineering Management, vol. 70, no. 7, pp. 2528-2545, July 2023, doi: 10.1109/TEM.2022.3166109.

Weber, K., Otto, B., Lis, D. (2021). Data Governance. In: Hildebrand, K., Gebauer, M., Mielke, M. (eds) Daten- und Informationsqualität. Springer Vieweg, Wiesbaden.