In the last few years, the growth in market adoption of big data technologies for the purpose of data sources analytics has grown significantly. There are many reasons why companies are doing
this. However the main reason is because of the potential value that great data amounts from internal and external sources can generate. For example, on-line click-stream and social media data can
be analyzed to enrich existing customer insight significantly. By automatically analyzing sensor data in real-time, problems in manufacturing production lines and distribution chains can be
detected or predicted and action taken to continually keep them optimized. Today we can see a significant change in how companies are satisfying these new data driven requirements. They are
increasingly shifting from using traditional, IT-centric platforms to more decentralized data discovery deploy-ments that are now spreading across the enterprise. Therefore, following aspects
should be dealt within the master thesis:
1. For which typical processes and tasks in the logistics sector big data analytics can be used win memory processing and online click The investigations may be conducted seamlessly? What effects
thus can be achieved for business?
2. How Big data analytics can be a value driver for a company?
3. Challenges, opportunities & practical implications – What challenges and needs are linked with the use of big data analytics with regard to hardware and software systems of the companies?
4. Brief description of industry wide used Big data tools (eg: SAP HANA, IBM Watson, etc.,) and modules that are used in companies and their purposes and what tasks can be done and also what
conventional tasks can be replaced by them.