Strategic data management

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 the so-called data organization. The strategic positioning of the data organization allows the sustainable alignment of data domains, data roles and data applications.
 

At Fraunhofer ISST, the components of strategic data management that ensure success for data-driven innovations are developed. The goal of strategic data management is the introduction and optimization of a company-internal data organization to realize data democratization. Establishing a data organization increases data quality and usability of AI applications, reduces data search processes, and improves the adoption of data applications. Within its framework, the necessary data capabilities are developed, sustainably established and continuously measurable. The basis for the data organization is the establishment of a data strategy that defines long-term specifications, for example, the prerequisite for participation in data ecosystems or the type of data storage. The data organization is based on these specifications and integrates them into the data governance approaches, which is ensured by means of decentralized and/or centralized corporate 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's range of services includes data strategy positioning, conducting data assessments and reviews, selecting suitable data governance approaches, developing role and process models, and supporting a proof-of-concept for tools.

Data maturity measurement for strategic data management

• Holistic determination of the ACTUAL state within a company in the area of data management on the basis of six central building blocks with a total of 26 different characteristics
• Data maturity measurement with the help of expert interviews with selected stakeholders of the respective company as a possible basis for the development of a data governance organization model

Data governance

• Development and selection of a suitable data governance organizational model to determine centralized and decentralized responsibilities.
• Development and introduction of suitable data roles according to tasks, competencies and responsibilities (AKV principle) in the existing organization
• Process model development based on the relevant data capabilities

Data strategy and data culture

• Strategic positioning of data management in the internal and external corporate environment
• Derivation of data capabilities, structured by technology, organization and people (TOP principle)
• Dovetailing with business strategy by means of data-related target systems, development plan and key performance indicators
• Transformation to a data culture by means of data awareness workshops, data principles and data competence building 

Tool Landscape

• Assessment and support of the proof-of-concept for the implementation of a data catalog 
• Assessment for the selection of suitable data quality software

Industries

Strategic data management contributes to solving demanding challenges in various industries. Whether as a toolbox in automotive manufacturing, a framework in medical technology, or an organizational model in transportation, data management as a strategic cornerstone has a positive impact on the introduction of new applications.

Here you will find a selection of released application examples from the competence field "Strategic Data Management" of the past years. Are you looking for further information? Simply get in touch with us - our contact persons will be happy to answer your questions and talk to you.

Example 1:

Data management at Dräger

The company-wide use of data is becoming a critical success factor for product manufacturers. For data to develop its full value for companies, it must be available, of high quality and easy to understand. A data strategy guarantees a stable process towards a future-oriented and data-driven organization. Together with Dräger, a data framework was developed and put into organizational use. 

Internal project page

Example 2:

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.

Example 3:

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

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.