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Data Preparation for Data Analytics (DPDA)

Duration

since 2009

Mission

The objective of the DPDA project is to utilize the potential offered by modern data analysis technology without a great deal of initial effort. For many companies, data preparation in particular still represents a significant initial hurdle to be overcome and one that hinders the implementation of data analytics projects. A lack of appropriate procedures and persons responsible for targeted data preparation within companies in particular are considered the biggest obstacles to implementation when it comes to data analytics projects. The project group has therefore set itself the goal of reducing these obstacles and developing appropriate recommendations to facilitate the implementation of data analytics projects. This includes the development of a role model for DPDA projects. What sets this role model apart is its close correlation with the product lifecycle, which will enable a cross-domain mindset to be established in companies.

Milestonese 2019

In 2019, the role model shown in Figure 1 was developed and specified. It provides an overview of the roles to be filled to ensure successful implementation of DPDA projects within companies. By specifying the interaction between the roles involved and the skills needed, companies can use the initial recommendations for action as the basis for filling the corresponding roles.

Orchestrierer (Projekteigner) -> Orchestrator (project owner)
Domänen- u. Fachexperten -> Domain/Technical Experts
(Prozess-, Daten- und Systemeigner) -> (process, data and system owner)

Outlook 2020

The objective in 2010 will be to specify the role model that has been developed in greater detail and test its applicability. This will involve applying the role model to the use cases already developed. It is intended that the development of a comprehensive reference model be driven forward in order to provide manufacturing companies with support for the future implementation of data analytics projects. Establishing a close link between this reference structure and the usual product lifecycle models should make it possible to derive specific courses of action that give due consideration to data analytics as early as in the planning stage.

Chair

Prof. Dr.-Ing. Rainer Stark, Fraunhofer IPK, Berlin
Prof. Dr. Jochen Deuse, Institute for Production Systems, Dortmund Technical University

Contact

Thorsten Reckelkamm
E-Mail: thorsten.reckelkamm(at)ips.tu-dortmund.de
Tel.: +49 (0)231 755 2631

Thomas Damerau
E-Mail: thomas.damerau(at)ipk.fraunhofer.de
Tel.: +49 (0)30 39006 216

Project Partner

Continental
CONWEAVER
Fraunhofer IGCV
Fraunhofer IPK
NEXT Data Service
PROSTEP
PTC
Schaeffler
SER Solutions
Siemens
ZF

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