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Databases, Data Warehousing, and Data Analytics

In: Handbook of Media and Communication Economics

Author

Listed:
  • Michael Eble

    (mm1 Consulting and Management PartG)

  • Julian M. Hoch

    (mm1 Consulting and Management PartG)

Abstract

The ongoing and rapid transformation of their markets presents media companies with the challenge of further digitizing and sustainably adapting business models. For this purpose, a broad technology stack for aggregating, storing, and analyzing big data is gaining importance: By means of the big data value chain, polystructured and multimodal data are combined with each other, and analytical insights are obtained in batch and real-time processing. For this purpose, different database systems as well as methods for data analysis (text mining, audio mining, and video mining) are combined in a generic lambda architecture. This makes it possible to leverage potential benefits in the production and distribution of media content, as the chapter outlines using the examples of the Deutsche Presse-Agentur (dpa)/Deutsche Welle (DW) and Netflix.

Suggested Citation

  • Michael Eble & Julian M. Hoch, 2024. "Databases, Data Warehousing, and Data Analytics," Springer Books, in: Jan Krone & Tassilo Pellegrini (ed.), Handbook of Media and Communication Economics, pages 343-356, Springer.
    • Handle: RePEc:spr:sprchp:978-3-658-39909-2_16
    DOI: 10.1007/978-3-658-39909-2_16
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