IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxxivy2021i4-part1p850-871.html
   My bibliography  Save this article

Cybersecurity of Business Intelligence Analytics Based on the Processing of Large Sets of Information with the Use of Sentiment Analysis and Big Data

Author

Listed:
  • Anna Golebiowska
  • Weronika Jakubczak
  • Dariusz Prokopowicz
  • Ryszard Jakubczak

Abstract

Purpose: The research aims to characterize newest soultions, especially with the respect to cybersecurity aspects of Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data. Design/Methodology/Approach: The working hypothesis refers to assumption that current regulations and security solutions for Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data is under extreme preassure to meet evergrowing challenges. There are more and more dends form the legal regulators as well as from the market and that creates a lot of problems with data protection. The article uses legal and comparative analysis as well as structural and functional analysis. Additionally, the interpretation method is also present. Findings: Article indicates that the aforementioned issues with the respect to growing importance of internet including the Internet of Things and Internet of Everything are becoming of more and more importance and cannot go with appropriate level of cybersecurity since the data they collect is of the great importance. The trends immanent to Industry 4.0 require from business more effort and customer orientation. Growing population and access to Internet demands larger scales of business operations. Practical Implications: As a result of conducting the research, it is possible to identify threats and present some recommendations for cybersecurity of Business Intelligence. Originality/Value: This is a complete research for Cybersecurity of Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data.

Suggested Citation

  • Anna Golebiowska & Weronika Jakubczak & Dariusz Prokopowicz & Ryszard Jakubczak, 2021. "Cybersecurity of Business Intelligence Analytics Based on the Processing of Large Sets of Information with the Use of Sentiment Analysis and Big Data," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 850-871.
  • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:4-part1:p:850-871
    as

    Download full text from publisher

    File URL: https://ersj.eu/journal/2631/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Celina M. Olszak, 2014. "Business Intelligence In Cloud," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 10(2), pages 115-125, December.
    2. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Weronika Jakubczak & Anna Golebiowska & Dariusz Prokopowicz, 2021. "The Legal and Security Aspects of ICT and Industry 4.0 Importance for Financial Industry 4.0 Development," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 169-181.
    2. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    3. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    5. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    6. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    7. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    8. Tiago Carneiro & Winnie Ng Picoto & Inês Pinto, 2023. "Big Data Analytics and Firm Performance in the Hotel Sector," Tourism and Hospitality, MDPI, vol. 4(2), pages 1-13, April.
    9. Gianfranco Marotta & Phillipe Krahnhof & Cam-Duc Au, 2022. "A Critical Analysis of Budgeting Processes from the Pharmaceutical Industry and Beyond," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(3), pages 1-3.
    10. Dan Zhang & Loo G. Pee & Shan L. Pan & Jingyuan Wang, 2024. "Information practices in data analytics for supporting public health surveillance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(1), pages 79-93, January.
    11. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    12. Andrea Cappelli & Iacopo Cavallini, 2021. "The Potential of Big Data Analysis in the Shipbuilding Industry: A Way of Increasing Competitiveness," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(suppl. 1), pages 53-74.
    13. Tursunbayeva, Aizhan & Di Lauro, Stefano & Pagliari, Claudia, 2018. "People analytics—A scoping review of conceptual boundaries and value propositions," International Journal of Information Management, Elsevier, vol. 43(C), pages 224-247.
    14. Fernando López & Konstatin Kholodilin, 2023. "Putting MARS into space. Non‐linearities and spatial effects in hedonic models," Papers in Regional Science, Wiley Blackwell, vol. 102(4), pages 871-896, August.
    15. Katarzyna Kopczewska, 2023. "Spatial bootstrapped microeconometrics: Forecasting for out‐of‐sample geo‐locations in big data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1391-1419, September.
    16. Karolis Matikonis & Matthew Gobey, 2024. "Small Business Property Tax Reductions and Firm Productivity," Small Business Economics, Springer, vol. 62(1), pages 307-324, January.
    17. Sebastian Zupok, 2024. "Contemporary Tools for Creating Customer Value," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 545-559.
    18. Godé, Cécile & Brion, Sébastien, 2024. "The affordance-actualization process of predictive analytics: Towards a configurational framework of a predictive policing system," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    19. Bruno F. Abrantes & Klaus Grue Ostergaard, 2022. "Digital footprint wrangling: are analytics used for better or worse? A concurrent mixed methods research on the commercial (ab)use of dataveillance," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 187-206, September.
    20. Hassani, Abdeslam & Mosconi, Elaine, 2022. "Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

    More about this item

    Keywords

    Cybersecurity; critical infrastructure; business intelligence; Big Data.;
    All these keywords.

    JEL classification:

    • H56 - Public Economics - - National Government Expenditures and Related Policies - - - National Security and War
    • F52 - International Economics - - International Relations, National Security, and International Political Economy - - - National Security; Economic Nationalism
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ers:journl:v:xxiv:y:2021:i:4-part1:p:850-871. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.