Skip to main content

A Data-Driven Framework for Business Analytics in the Context of Big Data

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 909))

Abstract

A vast amount of complex data has been generated in every aspect of business and this enables support for decision making through information processing and knowledge extraction. The growing amount of data challenges traditional methods of data analysis and this has led to the increasing use of emerging technologies. A data-driven framework is therefore proposed in this paper as a process to look at data and derive insights in a procedural manner. Key components within the framework are data pre-processing and integration together with data modelling and business intelligence – the corresponding methods and technology are discussed and evaluated in the context of big data. Real-world examples in health informatics and marketing have been used to illustrate the application of contemporary tools – in particular using data mining and statistical techniques, machine learning algorithms and visual analytics.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Alteryx: The business grammar report: a study of european decision-makers’ attitudes to data and analytics in modern business (2016). https://www.alteryx.com/resources/the-business-grammar-report-a-study-of-european-decision-makers-attitudes-to-data

  2. Computing Research: Big Data & IoT Review 2017 (2017). https://www.computing.co.uk/ctg/news/3010002/computing-big-data-iot-review-2017

  3. Gartner IT Glossary (2001). https://www.gartner.com/it-glossary/big-data

  4. GitHub (2017). https://github.com/QUT-BDA-MOOC/FLbigdataStats

  5. 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 

  6. Hand, D.J., Smyth, P., Mannila, H.: Principles of Data Mining. MIT Press Cambridge, USA (2001)

    Google Scholar 

  7. IBM: IBM SPSS Statistics for Windows, Version 22.0. IBM Corporation, Armonk, NY (2013)

    Google Scholar 

  8. IBM developerWorks: Hive as a tool for ETL or ELT (2015). http://www.ibm.com/developerworks/library/bd-hivetool

  9. Khan, I., Gadalla, C., Mitchell-Keller, L., Goldberg, M.S.: Algorithms: The new means of production. Digitalist Magazine (2016). www.digitalistmag.com/executive-research/algorithms-the-new-means-of-production

  10. Kimball, R., Ross, M.: The Data Warehouse Toolkit – The Definitive Guide to Dimensional Modeling. Wiley, New York (2013)

    Google Scholar 

  11. Lans, R.: Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses, Morgan Kaufmann Publishers Inc. (2012)

    Google Scholar 

  12. Lu, J., et al.: Data mining techniques in health informatics: a case study from breast cancer research. In: Renda, M.E., Bursa, M., Holzinger, A., Khuri, S. (eds.) ITBAM 2015. LNCS, vol. 9267, pp. 56–70. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22741-2_6

    Chapter  Google Scholar 

  13. Lu, J., Hales, A., Rew, D.: Modelling of cancer patient records: a structured approach to data mining and visual analytics. In: Bursa, M., Holzinger, A., Renda, M.E., Khuri, S. (eds.) ITBAM 2017. LNCS, vol. 10443, pp. 30–51. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64265-9_4

    Chapter  Google Scholar 

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

    Google Scholar 

  15. Marr, B.: Big Data In Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Wiley, Oxford (2016)

    Book  Google Scholar 

  16. Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22–31 (2014)

    Article  Google Scholar 

  17. Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehouse 5(4), 13–22 (2000)

    Google Scholar 

  18. Shmueli, G.: Practical Time Series Forecasting with R: A Hands-on Guide. Axelrod Schnall (2016)

    Google Scholar 

  19. Wiese, L.: Advanced Data Management: For SQL, NoSQL, Cloud and Distributed Databases. De Gruyter Textbook (2015)

    Google Scholar 

  20. Wyatt, J.: Plenary Talk: Five big challenges for big health data. In: 8th IMA Conference on Quantitative Modelling in the Management of Health and Social Care, London (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jing Lu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lu, J. (2018). A Data-Driven Framework for Business Analytics in the Context of Big Data. In: Benczúr, A., et al. New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00063-9_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00062-2

  • Online ISBN: 978-3-030-00063-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics