Skip to main content
SpringerLink
Log in

Databases, Data Warehousing, and Data Analytics

  • Living reference work entry
  • First Online:
Handbook of Media and Communication Economics

  • 39 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Abelló, A. (2015). Big data design. https://www.essi.upc.edu/~aabello/publications/15.DOLAP.pdf. Accessed 2 May 2022.

  • Amatriain, X. (2013a). Big & personal: Data and models behind Netflix recommendations. https://xamat.github.io/pubs/BigAndPersonal.pdf. Accessed 2 May 2022.

  • Amatriain, X. (2013b). Mining large streams of user data for personalized recommendations. SIGKDD Explorations, 14(2), 37–48.

    Article  Google Scholar 

  • Amatriain, X. (2014). The recommender problem revisited. In RecSys’14, 6–10 Oct 2014, Foster City/Silicon Valley. https://doi.org/10.1145/2645710.2645775

  • Auer, S. (2014). Introduction to LOD2. In S. Auer, V. Bryl, & S. Tramp (Eds.), Linked open data – Creating knowledge out of interlinked data (pp. 1–17). Springer.

    Chapter  Google Scholar 

  • Bourque, P., & Fairley, R. E. (Eds.). (2014). Guide to the software engineering body of knowledge (SWEBOK). Version 3.0. IEEE Computer Society.

    Google Scholar 

  • Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88–98.

    Article  Google Scholar 

  • Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.

    Article  Google Scholar 

  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

    Article  Google Scholar 

  • Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90, 1–6.

    Google Scholar 

  • Davenport, T. H., Barth, P., & Bean, R. (2012). How “big data” is different. MIT Sloan Management Review, 54, 1–6.

    Google Scholar 

  • Debortoli, S., Müller, O., & vom Brocke, J. (2014). Comparing competency requirements for business intelligence and big data specialists. Wirtschaftsinformatik, 56(5), 315–328.

    Article  Google Scholar 

  • Dierickx, I., & Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12), 1504–1511.

    Article  Google Scholar 

  • Dirschl, C., Pellegrini, T., Nagy, H., Eck, K., Van Nuffelen, B., & Ermilov, I. (2014). LOD2 for media and publishing. In S. Auer, V. Bryl, & S. Tramp (Eds.), Linked open data – Creating knowledge out of interlinked data (pp. 133–154). Springer.

    Chapter  Google Scholar 

  • Eble, M. (2013). Medienmarken im Social Web: Wettbewerbsstrategien und Leistungsindikatoren von Online-Medien aus medienökonomischer Perspektive. LIT.

    Google Scholar 

  • Eble, M., & Kirch, S. (2014). Enterprise Search im Wissensmanagement: Herausforderungen für Suchmaschinen in forschungsbasierten Konzernen. In H. Krah & R. Müller-Terpitz (Eds.), Suchmaschinen (pp. 85–106). Logos.

    Google Scholar 

  • Eble, M., & Stein, D. (2015). Utilisation of audio mining technologies for researching public communication on multimedia platforms. In A. Maireder, J. Ausserhofer, & C. Schumann (Eds.), Digitale Methoden in der Kommunikationswissenschaft (pp. 329–345). Nomos. https://doi.org/10.17174/dcr.v2.14

    Chapter  Google Scholar 

  • Eble, M., & Winkler, T. (2014). Digitale Wertketten für Social Connected TV: Wertbeiträge von Content-Technologies in der Multimedia-Produktion. In H. Rau (Ed.), Digitale Dämmerung. Die Entmaterialisierung der Medienwirtschaft (pp. 229–239). Nomos.

    Google Scholar 

  • Eble, M., Ziegele, M., & Jürgens, P. (2014). Forschung in geschlossenen Plattformen des Social Webs. In M. Welker, M. Taddicken, J.-H. Schmidt, & N. Jackob (Eds.), Handbuch Online-Forschung. Sozialwissenschaftliche Datengewinnung und -auswertung in digitalen Netzen (pp. 128–154). Halem.

    Google Scholar 

  • Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10–11), 1105–1121.

    Article  Google Scholar 

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2015), 137–144.

    Article  Google Scholar 

  • Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652–687.

    Article  Google Scholar 

  • Kamp, G. (2015). Newsstream 3.0 – Big-Data-Infrastruktur für Journalisten. Vortrag auf dem Symposium Big Data am 18.06.15 im Haus des Rundfunks in Berlin. http://de.slideshare.net/gkamp/20150618-ardzdf. Accessed 14 Feb 2016.

  • Kreps, J. (2014). Questioning the lambda architecture. https://www.oreilly.com/ideas/questioning-the-lambda-architecture. Accessed 31 May 2016.

  • Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety. Application Delivery Strategies, 949, 1–4.

    Google Scholar 

  • LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 20–31.

    Google Scholar 

  • Malaka, I., & Brown, I. (2015). Challenges to the organisational adoption of big data analytics: A case study in the South African telecommunications industry. In SAICSIT ’15: Proceedings of the 2015 annual research conference on South African Institute of Computer Scientists and Information Technologists, September 2015 (27). https://doi.org/10.1145/2815782.2815793

  • Marz, N. (2011). How to beat the CAP theorem. http://nathanmarz.com/blog/how-to-beat-the-cap-theorem.html. Accessed 31 Dec 2015.

  • Marz, N. (2012). Big data lambda architecture. http://www.databasetube.com/database/big-data-lambda-architecture/. Accessed 19 Dec 2015.

  • Mey, S. (2015). Projekt News – Stream 3.0: Big Data-Helferlein für Redaktionen. http://get.torial.com/blog/2015/09/news-stream-3-0-big-data-redaktionen/. Accessed 17 Sept 2015.

  • Miller, H. G., & Mork, P. (2013). From data to decisions: A value chain for big data. IT Professional, 15(1), 57–59.

    Article  Google Scholar 

  • Nelson, R. R. (1991). Why do firms differ, and how does it matter? Strategic Management Journal, 12(S2), 61–74.

    Article  Google Scholar 

  • Pellegrini, T. (2012). Semantic web in network-based entertainment applications - building blocks for metadata economics using BBC Music Beta as an example. In J. Müller-Lietzkow (Ed.), Ökonomie, Qualität und Management von Unterhaltungsmedien (pp. 253–276). Nomos.

    Chapter  Google Scholar 

  • Pellegrini, T. (2014). Datenlizenzierung als Diversifikationstreiber in der Medienindustrie. In H. Rau (Ed.), Digitale Dämmerung: Die Entmaterialisierung der Medienwirtschaft (pp. 267–280). Nomos.

    Google Scholar 

  • Picot, A., & Propstmeier, J. (2013). Big data. Media Economics, 10, 34–38.

    Google Scholar 

  • Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 9, 1–23.

    Google Scholar 

  • Prahalad, C. K., & Hamel, G. (1990). The core competence of the corporation. Harvard Business Review, 3, 79–91.

    Google Scholar 

  • Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. mm1 Consulting and Management PartG, Stuttgart, Germany

    Michael Eble & Julian M. Hoch

Authors
  1. Michael Eble
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julian M. Hoch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Eble .

Editor information

Editors and Affiliations

  1. FH St. Pölten, St. Pölten, Austria

    Jan Krone

  2. FH St. Pölten, St. Pölten, Austria

    Tassilo Pellegrini

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Fachmedien Wiesbaden GmbH, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Eble, M., Hoch, J.M. (2022). Databases, Data Warehousing, and Data Analytics. In: Krone, J., Pellegrini, T. (eds) Handbook of Media and Communication Economics. Springer, Wiesbaden. https://doi.org/10.1007/978-3-658-34048-3_16-2

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-34048-3_16-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Wiesbaden

  • Print ISBN: 978-3-658-34048-3

  • Online ISBN: 978-3-658-34048-3

  • eBook Packages: Springer Reference Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation