Nothing Special   »   [go: up one dir, main page]

Skip to main content

Towards a Model Based Process Assessment for Data Analytics: An Exploratory Case Study

  • Conference paper
  • First Online:
Systems, Software and Services Process Improvement (EuroSPI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1251))

Included in the following conference series:

Abstract

The ability to leverage data analytics can enhance the decision-making process in organizations by generating valuable insights. However, there is a limited understanding of how organizations can adopt data analytics as part of their business processes due to a lack of comprehensive roadmap with a structural approach like a Process Capability Maturity Model (PCMM). In this study, the development of a PCMM based on the ISO/IEC 330xx standard for the data analytics domain is proposed to assist organizations in assessing their data analytics processes capability level and providing a roadmap for improving them continuously. Towards this goal, we conducted an exploratory case study for one data analytics process to evaluate the applicability and usability of the proposed approach.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Manyika, J., Chui, M., Joshi, R.: Modeling the global economic impact of AI. McKinsey (2018). https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-AI-frontier-modeling-the-impact-of-ai-on-the-world-economy. Accessed 25 Dec 2019

  2. Gokalp, M.O., Kayabay, K., Akyol, M.A., et al.: Big data for Industry 4.0: a conceptual framework. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 431–434. IEEE (2016)

    Google Scholar 

  3. Hüner, K.M., Ofner, M., Otto, B.: Towards a maturity model for corporate data quality management. In: Proceedings of the ACM Symposium on Applied Computing, pp. 231–238. ACM (2009)

    Google Scholar 

  4. Khan, A.A., Keung, J., Niazi, M., et al.: GSEPIM: a roadmap for software process assessment and improvement in the domain of global software development. J. Softw. Evol. Process 31, e1988 (2019)

    Article  Google Scholar 

  5. Barafort, B., Mesquida, A., Mas, A.: ISO 31000-based integrated risk management process assessment model for IT organizations. J. Softw. Evol. Process 31, e1984 (2019)

    Article  Google Scholar 

  6. Automotive SIG: Automotive SPICE process assessment model. Final Release, v4 4:46 (2010)

    Google Scholar 

  7. Mc Caffery, F., Dorling, A.: Medi SPICE development. J. Softw. Evol. Process 22, 255–268 (2010)

    Google Scholar 

  8. Gökalp, E., Demirörs, O.: Model based process assessment for public financial and physical resource management processes. Comput. Stand. Interfaces 54, 186–193 (2017)

    Article  Google Scholar 

  9. ISO/IEC: ISO/IEC 33001:2015 information technology – process assessment – concepts and terminology (2015)

    Google Scholar 

  10. Korsaa, M., Johansen, J., Schweigert, T., et al.: The people aspects in modern process improvement management approaches. J. Softw. Evol. Process 25, 381–391 (2013)

    Article  Google Scholar 

  11. Varkoi, T., Mäkinen, T., Cameron, F., Nevalainen, R.: Validating effectiveness of safety requirements’ compliance evaluation in process assessments. J. Softw. Evol. Process 32, e2177 (2020)

    Article  Google Scholar 

  12. Ahern, D.M., Clouse, A., Turner, R.: CMMI. SEI Ser. Softw. Eng. (2001)

    Google Scholar 

  13. Gökalp, E., Demirörs, O.: Government process capability model: an exploratory case study. In: Mitasiunas, A., Rout, T., O’Connor, R.V., Dorling, A. (eds.) SPICE 2014. CCIS, vol. 477, pp. 94–105. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13036-1_9

    Chapter  Google Scholar 

  14. Lukman, T., Hackney, R., Popovič, A., et al.: Business intelligence maturity: the economic transitional context within Slovenia. Inf. Syst. Manag. 28, 211–222 (2011)

    Article  Google Scholar 

  15. Cosic, R., Shanks, G., Maynard, S.: Towards a business analytics capability maturity model. In: 2012 Proceedings of the 23rd Australasian Conference on Information Systems (ACIS 2012), pp. 1–11. ACIS (2012)

    Google Scholar 

  16. Raber, D., Winter, R., Wortmann, F.: Using quantitative analyses to construct a capability maturity model for business intelligence. In: 2012 45th Hawaii International Conference on System Sciences, pp. 4219–4228. IEEE (2012)

    Google Scholar 

  17. Halper, B.F., Stodder, D.: A Guide to Achieving Big Data Analytics Maturity (TDWI Benchmark Guide) (2016). https://tdwi.org/whitepapers/2018/01/aa-all-ms-a-guide-to-achieving-big-data-analytics-maturity.aspx. Accessed 6 May 2019

  18. Davenport, T.H., Harris, J.G.: Competing on Analytics: The New Science of Winning. Harvard Business Press (2007)

    Google Scholar 

  19. ISO/IEC: ISO/IEC 33002:2015 - information technology – process assessment – requirements for performing process assessment (2016)

    Google Scholar 

  20. Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pp. 29–39. Citeseer (2000)

    Google Scholar 

  21. Gökalp, M.O., Kayabay, K., Zaki, M., et al.: Open-source big data analytics architecture for businesses. In: 2019 1st International Informatics and Software Engineering Conference (UBMYK), pp. 1–6. IEEE (2019)

    Google Scholar 

  22. ISO/IEC TR 15504-5:1999: Information technology—software process assessment—part 5: an assessment model and indicator guidance (1999)

    Google Scholar 

  23. Pries-Heje, J., Johansen, J.: SPI Manifesto. Eur. Syst. Softw. Process Improv. Innov. (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mert Onuralp Gökalp .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gökalp, M.O., Kayabay, K., Gökalp, E., Koçyiğit, A., Eren, P.E. (2020). Towards a Model Based Process Assessment for Data Analytics: An Exploratory Case Study. In: Yilmaz, M., Niemann, J., Clarke, P., Messnarz, R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2020. Communications in Computer and Information Science, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-56441-4_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-56441-4_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-56440-7

  • Online ISBN: 978-3-030-56441-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics