Semantics of Big Data in Corporate Management Systems

The modern development of engineering, telecommunications, information and computer technologies allows for collecting, processing and storing huge volumes of data today. Among the first applications of Big Data there was the creation of corporate repositories that use gathered information for analysis and strategic decision-making. However, an unsystematic collection of information leads to the storage and processing of a large amount of non-essential data, while important information falls out of the analysts’ view. An important point is the analysis of the semantics and purpose of data collection, which define both the collection technology and infrastructure and the direction of subsequent processing and use of Big Data with the help of metrics that reduce data volume, leaving only essential information to process. As a first step towards this goal, we present a formalization approach of corporate Big Data using a partially observable Markov decision process (POMDP), and we show that it naturally aligns itself with the corporate governance system.