Big Data in the transportation and logistics industry : transforming Big Data into meaningful customer information: the case of DB Schenker - Austria

Abstract

Abstract Data and information overloading is one of two the main challenges for organizations today. The amount and speed of data has rapidly grown over this last decade. 90% of all worldwide data has been generated in the last 2 years and this exponential trend seems to be the same for the future. Moreover, data is being generated from new sources like social networks, smart watches, wristbands or other sensor products. Albeit, enterprise systems are an important source of information, they are not the only one. All the factors above, including enterprise systems describe the term -Big Data-. This phenomenon is defined by big data volume, velocity, variety and veracity and it is seen both as a new potential for getting the right information and, as the next frontier for innovation. Particularly when looking at customer information; Big Data plays an important role. The master thesis is focused on this phenomenon in the transportation and logistics industry, and how Big Data can yield excellent customer information. This paper describes why Big Data analytics shouldn-t be underestimated; which data sources for the analytics provide important information about customers and their behaviors; and gives a surface overview how big data analytics works. Included are highlighted specifics of the transportation and logistics industry in relation to Big Data. The practical part is based on the case study of the company DB Schenker Austria. This includes 3 practical use cases of possible application of Big Data analytics in the DB Schenker Austria environment and their influence on customer information. The cases describe solutions for analyzing internal data for land transportation, internal data from emails and other documents, results visualization, and analysis of external data from social networks. The results of the cases lead to important factors and key drivers for Big Data analytics in this kind of industry. I also describe internal and external factors which impact the viability of Big Data analytics in general.