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Conquering data in austria technology roadmap

A comprehensive roadmap study on Intelligent Data Analytics technologies is provided.

Bibliographic Data

Dr. Helmut berger, Dr. michael Dittenbach, Dr. Marita Haas, Dr. Ralf Bierig, Dr. Allan Hanbury, Dr. Mihai Lupu, Dr. Florina Piroi
Publisher: bmvit
English

Content Description

This study, commissioned by the Austrian Research Promotion Agency (Österreichische Forschungsförderungsgesellschaft, FFG) and the Austrian Federal Ministry for Transport, Innovation and Technology (Bundesministerium für Verkehr, Innovation und Technologie, BMVIT), provides objectives for the short-, medium- and long-term focus (year 2025) of the FFG funding programme ICT of the Future: Conquering Data - Intelligent Systems (IKT der Zukunft: Daten durchdringen - Intelligente Systeme).

The results presented in this work arose out of a mix of approaches that included an exhaustive literature review, interactions with stakeholders through an online survey, workshop discussions, and structured expert interviews.

This technology roadmap brings the technology perspective and the perspective of the area's stakeholders (public, research, industry) together. It identifies the requirements for new ICT in this area and presents a selection of expected developments, requirements, and guidelines in the ICT field.

Surveying the Intelligent Data Analytics field, we have analysed the relevant methods and techniques and categorised them into four (interacting) groups: Search and Analysis, Semantic Processing, Cognitive Systems and Prediction, and Visualisation and Interaction.

The coverage of Data Analytics applications, on which Austrian companies, research institutes, and universities focus, has a rather wide range. These application areas were reviewed with respect to how they currently handle data and how they make use of Intelligent Data Analytics. Healthcare, Energy and Utilities, eScience as well as Manufacturing and Logistics were identified to be the most important application domains in Austria.

The most important challenges in Intelligent Data Analytics were summarised by aggregating the different stakeholders' viewpoints on data. These challenges range from Privacy, Security and Data Ownership over algorithmic and technological shortcomings to shortages in the supply of qualified personnel.

During this study, a comprehensive landscape of Austrian competences in Intelligent Data Analytics was compiled. This competence landscape covers Austrian research institutes, universities, universities of applied sciences as well as commercial service providers operating in Austria. Austrian strengths are in the areas of statistics, algorithmic efficiency, machine learning, computer vision and Semantic Web.

Based on the analysis, nine roadmap objectives that span over the short, medium and long term are made. These objectives cover three primary areas: Technology, Coordination, and Human Resources.

The first four objectives cover technological topics that aim at

  • the advance of the current data integration and data fusion capabilities,
  • at the increase in algorithm efficiency,
  • at turning raw data into actionable information, and
  • at automating the knowledge workers' processes.

These engineering-focused objectives require dedicated R&D funding, which will, on the mid to long term future, result in novel, Austrian-made lead technologies in the area of Intelligent Data Analytics.

The next three objectives focus on measures supporting the stakeholders' capabilities to innovate and extend their competitive position. These measures aim at improving Austria's visibility, integration, and attractivity in the international ICT research and development context. They are coordination-oriented objectives that require investment in order to build an Austrian Data-Services Ecosystem.

The Ecosystem will make data accessible and interoperable in order to generate greater economic value. Further objectives involve the elaboration of a legal framework for dealing with data, and the launch of various initiatives - including a dedicated "Austrian Data Technologies Institute" - which will strengthen the networking of and know-how exchange between Austrian and international stakeholders in the field.

The remaining two objectives cover the area of Human Resources and aim at addressing the urgent need for highly qualified personnel in data technologies. They advocate investment in novel education programmes that assist in creating polymath thinkers capable to cope with the requirements emerging from (Big) Data Analytics. The second of these two objectives presents actions to improve the gender and diversity awareness in the field of Intelligent Data Analytics.

Potential lighthouse projects are presented as a route to achieving some of these objectives. These include a broad impact lighthouse, the Data-Services Ecosystem, which allows crossfertilisation of technologies between application domains. Furthermore, application-specific lighthouses, which channel the development work towards solving challenges in a specific domain of application, are described. The suggested application domains for applicationspecific lighthouses are manufacturing, energy, healthcare and digital humanities.

In summary, Intelligent Data Analytics has the potential to greatly benefit the Austrian society and economy. It is essential for a successful innovation economy to provide the ecosystem in which data-centred innovation and technology transfer can take place. There are still many challenges to overcome from both a technological and societal point of view before Austria is ready to take full advantage of this opportunity.

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