Big Data

The term Big Data is a synonym for massive, heterogeneous data sets with individual characteristics that cannot be analyzed by conventional methods due to their complexity. Explicitly, data described by this term are either defined by large volume, variety or velocity. In addition, data value, data variability as well as data veracity are implicitly given by the specific content. The complex problem space of the data analytics domain can be consequently derived with the help of these six “v” of Big Data.

The analysis of the heterogeneous, multi-dimensional data sets requires a thorough strategy for data assimilation and fusion in order to get an in-depth understanding of phenomena, to discover hidden characteristics, and to gain new knowledge. With the advent of digitalization, this becomes even more evident: Physical devices get smarter, produce a multitude of data with high frequency and need to react to specific events in real-time. Digital agriculture is one of the examples that require the combination of various data sources to generate an added value, from manifold types of sensor and cloud data to meteorological forecasts to bioeconomic simulations.