Big Data in Austria
On the one hand, these data are frequently complex and unstructured in a wide variety of formats and require time-sensitive computation. On the other hand, it is essential to ensure trustworthiness in data and inferred knowledge. These challenges are commonly known as the four V:
- Variety -> Value.
This study analyses the extraordinary, innovative potential of Big Data technologies for the Austrian market ranging from managing the data deluge to semantic and cognitive systems. Moreover, the study identifies emerging opportunities arising from the utilization of publicly available data, such as Open Government Data, and company internal data by covering multiple domains.
A holistic approach comprising research principles, development of new methods/tools/technologies, requirements, the implementation in innovative products by industry and especially Austrian SMEs, and knowledge transfer at tertiary education establishments served to achieve these objectives.
This study depicts the state-of-the-art of Big Data, defines the Big Data stack for Big Data technologies (Utilization, Analytics, Platform, and Management Technologies) and gives an overview of existing methods in all the areas of the stack. The preparation of the state-of-the-art includes a comprehensive analysis of the Austrian market players, of the publicly accessible data sources as well as the current situation in tertiary education and research.
Further, the study develops a market and potential analysis of the Austrian market, which gives an overview of the development of the Big Data market, a detailed evaluation regarding requirements, potentials, and business cases broken down by industries. It closes with cross-cutting issues. Furthermore, this study includes guidelines for the implementation and application of Big Data technologies in order to reach the goal of successfully handling and implementing Big Data in organizations.
For this purpose, best practice models will be discussed in detail based on specific Big Data key projects. On this basis, specific guidelines for the realization of Big Data projects in organizations will be presented. Within this scope, a Big Data maturity model, a procedure model, a competence analysis (of the job description of a Data Scientist, among others) as well as reference architecture for an efficient implementation of Big Data will be presented.
Finally, this study will present conclusions and recommendations with the goal of strengthening the Austrian research and economic landscape based on a holistic analysis of the area “Big Data” in Austria.
Knowledge acquisition within this study has been based on the following three main building blocks: investigations, interviews, and surveys. The current market (research, industry, and tertiary education) situation as well as domain-specific and multidisciplinary requirements will be analyzed based on
selected national and international studies (e.g. not freely available IDC Big Data studies, Big Data study from Germany, F&E Dienstleistung Österreichische Technologie-Roadmap study),
recently held events and available technology platforms,
currently funded and conducted Austrian research projects,
specialized knowledge transfer at tertiary education establishments.
The achieved results will be complemented and reinforced by means of domain-specific interviews, discussions in workshops, and surveys.