Public Broadcaster's Big-data Technology Trajectories: The Case of NHK and BBC

The competitive environment of the broadcasting sector is changing; under this change, public broadcasters have to adapt to keep being relevant to their users. Big-data technologies play an essential part in the technological side of these changes. Our objective is to identify the public broadcasters' big-data technology trajectories in this changing environment. We propose two research questions to narrow down the objective: What are the big-data technology trajectories of public broadcasters? Also, which are the directions of big-data technologies proposed by the public broadcasters? We propose as the method, to analyze scientific paper's keywords and combine it with network analysis. We compare two datasets, big-data, and public broadcasters. The big-data set is borrowed from a previous work done by the authors which detected big-data keywords proxy of knowledge convergence. The public broadcasters' dataset is created from the scientific publications reported by BBC and NHK. We match the big-data converging keywords to the keywords of the BBC and NHK publications and visualize their behavior over the time (2008–2016). We analyze the documents linked to the shared keywords on both datasets to identify the big-data technology trajectories and propose future directions. We identified as big-data technological trajectories for BBC, Linked open data, recommender system, semantic web, and Image processing; For NHK, speech recognition, Generate metadata to index NHK's programs and Augmented reality (AR). Concerning their future, the detected trajectories are expected to be useful for broadcasters an organizations related to their value chain.