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Data Science in the Business Environment: Architecture, Process and Tools

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Advanced Computing (IACC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1528))

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Abstract

Data science involves the collection, management, processing, analysis, visualisation and interpretation of huge amounts of data. It is a multi-disciplinary field that integrates systematic thinking, methodology, process and technology to develop intelligence with respect to real-world problems. This paper focuses on the business environment and identifies the components of data science forming a conceptual architecture before proposing a composite data-driven process model. Representative tools and techniques are applied to relevant case studies demonstrating innovation in undergraduate programme design, customer analytics and the marketing of insurance.

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References

  1. Adams, M., Upreti, V., Chen, J.: Product-market strategy and underwriting performance in the united kingdom’s property–casualty insurance market. Eur. J. Finance 25(11), 1012–1031 (2019)

    Article  Google Scholar 

  2. Bean, R.: Why is it so hard to become a data-driven company? Harvard Bus. Rev. Digital Articles 5, 1–5 (2021)

    Google Scholar 

  3. Cao, L.B.: Data science: challenges and directions. Commun. ACM 60(8), 59–68 (2017)

    Article  Google Scholar 

  4. Cochran, J.J.: INFORMS Analytics Body of Knowledge. John Wiley & Sons, Hoboken, NJ (2019)

    Google Scholar 

  5. Data Science Council of America. https://www.dasca.org

  6. Donoho, D.: 50 years of data science. J. Comput. Graph. Stat. 26(4), 745–766 (2017)

    Article  MathSciNet  Google Scholar 

  7. EDISON: Building the data science profession. https://cordis.europa.eu/project/id/675419/ reporting

  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  9. IBM Analytics White Paper: Foundational Methodology for Data Science. https://tdwi.org/~/media/64511A895D86457E964174EDC5C4C7B1.PDF

  10. Iacobucci, D., Petrescu, M., Krishen, A., Bendixen, M.: The state of marketing analytics in research and practice. J. Market. Anal. 7, 152–181 (2019)

    Article  Google Scholar 

  11. Kaggle: Health Insurance – JOB-A-THON – Analytics Vidhya. https://www.kaggle.com/imsparsh/jobathon-analytics-vidhya

  12. Lu, J.: Data science in the business environment: insight management for an Executive MBA. Int. J. Manage. Educ. 20(1) (2022). Elsevier

    Google Scholar 

  13. Lu, J., Cairns, L., Smith, L.: Data science in the business environment: customer analytics case studies in SMEs. J. Model. Manag. 16(2), 689–713 (2021)

    Article  Google Scholar 

  14. Lu, J.: Data Science in the Business Environment: skills analytics for curriculum development. In: G. Nicosia et al. (eds.) Machine Learning, Optimization and Data Science. Lecture Notes in Computer Science, Vol. 11331, Springer International Publishing AG, pp.116–128. ISBN 978–3–030–13708–3 (2019)

    Google Scholar 

  15. Marr, B.: Big Data: Using Smart Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance. John Wiley & Sons, Chichester (2015)

    Google Scholar 

  16. Provost, F., Fawcett, T.: Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media, California (2013)

    Google Scholar 

  17. Sedkaoui, S.: Data Analytics and Big Data. Wiley (2018)

    Google Scholar 

  18. SFIA: The Skills Framework for the Information Age. https://www.sfia-online.org/en

  19. Skiena, S.: The Data Science Design Manual. Texts in Computer Science, Springer (2017)

    Google Scholar 

  20. Tableau: Visual analytics platform. https://www.tableau.com

  21. Wild, C. J., Pfannkuch, M.: Statistical thinking in empirical enquiry. Int. Stat. Rev. 67(3), 223–265 (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Winchester, Winchester, SO22 5HT, UK

    Jing Lu

Authors
  1. Jing Lu
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Correspondence to Jing Lu .

Editor information

Editors and Affiliations

  1. Bennett University, Greater Noida, India

    Deepak Garg

  2. Missouri University of Science and Technology, Rolla, CA, USA

    Sarangapani Jagannathan

  3. Model Institute of Engineering and Technology, Kot Bhalwal, Jammu and Kashmir, India

    Ankur Gupta

  4. University of Malta, Msida, Malta

    Lalit Garg

  5. Bennett University, Greater Noida, India

    Suneet Gupta

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Lu, J. (2022). Data Science in the Business Environment: Architecture, Process and Tools. In: Garg, D., Jagannathan, S., Gupta, A., Garg, L., Gupta, S. (eds) Advanced Computing. IACC 2021. Communications in Computer and Information Science, vol 1528. Springer, Cham. https://doi.org/10.1007/978-3-030-95502-1_22

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  • DOI: https://doi.org/10.1007/978-3-030-95502-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95501-4

  • Online ISBN: 978-3-030-95502-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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