Big Data Value Chain: Making Sense of the Challenges

In this paper, we highlight how the value of data accumulates through the stages in a value chain. We introduce a Big data value chain where the value adding stages are decoupled from the technological requirements of data processing. We argue that through viewing the stages of value accumulation, it is possible to identify such challenges in dealing with Big Data that cannot be mitigated through technological developments. Our proposed Big Data value chain consists of eight stages that we subsequently cluster into three main phases, namely sourcing, warehousing and analyzing. In scrutinizing these three phases we suggest that the technologically immitigable challenges in sourcing relate to the veracity of data, and the challenges in warehousing concern ownership and power distribution. Finally, in the phase of analyzing the problems are manifold, including the black boxed nature of the algorithms, the problematics of standards of desirability, and the mandatory trade-offs. Our discursive article contributes to the literature discussing the value, utility and implications of Big Data.