Skip to main content
Log in

BigData oriented to business decision making: a real case study in constructel

  • S.I. : WorldCIST’20
  • Published:
Computational and Mathematical Organization Theory Aims and scope Submit manuscript

Abstract

Analyze and understand how to combine data warehouse with business intelligence tools, and other useful information or tools to visualize KPIs are critical factors in achieving the goal of raising competencies and business results of an organization The main objective of this paper is to present the development of a BI platform, using DW tools to create graphs and detailed reports for the Constructel company. The development of this work was thought and developed in stages, starting with the analysis of the theme, analyzing the literature review to support the case study; used tools; requirements gathering; architectural design; and ending with the development and implementation of a platform with dashboards and reports for the organization’s management. With the availability of this platform, it is intended that business managers will be able to identify solutions and anomalies in a more insightful and faster way, thus allowing to improve productivity and business quality, without neglecting the satisfaction of the organization’ internal employees, greater flexibility and availability so that managers can deal with other situations that are more technical and linked to the business itself.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abbasi M, et al (2018) SINGLE vs MapReduce vs relational: predicting query execution time. In: International conference: beyond databases, architectures and structures. Springer, Cham

  • Amaral L, Varajão J (2007) Planeamento de Sistemas de Informação. FCA-Editora de Informática, LDA, Lisboa, p 228

    Google Scholar 

  • Anuradha G, Roy B (2014) Suggested techniques for clustering and mining of data streams. In: International conference on circuits, systems, communication and information technology applications (CSCITA). IEEE

  • Bele D, Weis L, Maher N (2019) Sustainable development under the conditions of european integration. Part II, MISC

  • Bimonte S et al (2019) Design and implementation of active stream data warehouses. Int J Data Wareh Min (IJDWM) 15(2):1–21

    Article  Google Scholar 

  • Caldas MPK, Scandelari L, Luiz Kovaleski J (2006) Aplicações sobre uma DataWarehouse no ambiente das organizações e suas vantagens. In: Simpósio de Engenharia de Produção, XIII SIMPEP

  • Casagrande NG (2005) Metodologia para Modelagem de Arquitetura de Informação estratégica para pequenas empresas: Uma aplicação no setor de turismo rural

  • Chaturvedi A, Lone FA (2017) Analysis of BigData security schemes for detection and prevention from intruder attacks in cloud computing. Int J Comput Appl 158(5):26–30

    Google Scholar 

  • Chaudhuri S, Dayal U (1997) Dataware housing and OLAP for decision support. In: ACMSigmod record, vol 26. no 2. ACM, New York

  • Choo A, Saeger T (2011) Data analysis for yield improvement using TIBCO’s Spotfire data analysis software. In: CS mantech conference

  • Corrêa Â, Sferra HH (2003) Conceitos e aplicações de datamining. Rev Ciência Tecnol 11:19–34

    Google Scholar 

  • Färber F et al (2012) SAP HANA database: data management for modern business applications. ACM Sigmod Rec 40(4):45–51

    Article  Google Scholar 

  • Gligor G, Teodoru S (2011) Oracle analytics: engineered for speed-of-thought analytics. Database Syst J 2(4):3–8

    Google Scholar 

  • Grover V et al (2018) Creating strategic business value from big data analytics: a research framework. J Manag Inf Syst 35(2):388–423

    Article  Google Scholar 

  • Guarani G, Helmer S (2012) Integrating star and snowflake schemas in data warehouses. Int J Data Wareh Min (IJDWM) 8(4):22–40

    Article  Google Scholar 

  • Halper F, Stodder D (2014) TDWI analytics maturity model guide. TDWI research, pp 1–20

  • Ilacqua C, Cronstrom H, Richardson J (2015) Learning qlik sense: the official guide. Packt Publishing Ltd, Birmingham

    Google Scholar 

  • ITGP Privacy Team (2017) EU general data protection regulation (GDPR), An implementation and compliance guide - second edition. IT Governance Ltd

  • Ji-fan Ren S et al (2017) Modelling quality dynamics, business value and firm performance in a big data analytics environment. Int J Prod Res 55(17):5011–5026

    Article  Google Scholar 

  • Lachev T, Price E (2018) Applied microsoft power BI: bring your data to life! Prologika Press, New York

    Google Scholar 

  • Lennerholt C, van Laere J, Söderström E (2018) Implementation challenges of self-service business intelligence: a literature review. In: 51st Hawaii international conference on system sciences, 3–6 January 2018, Hilton Waikoloa Village, Hawaii, USA, vol 51. IEEE Computer Society

  • Levene M, Loizou G (2003) Why is the snowflake schema a good data warehouse design? Inf Syst 28(3):225–240

    Article  Google Scholar 

  • Marquesone R (2016) Big Data: Técnicas e tecnologias para extração de valor dos dados. Editora Casa do Código, São Paulo

    Google Scholar 

  • Martins P, Abbasi M, Furtado P (2015a) A scale: big/small data ETL and real-time data freshness. In: Kozielski S, Mrozek D, Kasprowski P, Małysiak-Mrozek B, Kostrzewa D (eds) Beyond databases. Architectures and structures. Advanced technologies for data mining and knowledge discovery. Springer, Cham, pp 315–327

    Google Scholar 

  • Martins P, Abbasi M, Furtado P (2015b) A scale: auto-scale in and out ETL + Q framework. In: Kozielski S, Mrozek D, Kasprowski P, Małysiak-Mrozek B, Kostrzewa D (eds) Beyond databases. Architectures and structures. Advanced technologies for data mining and knowledge discovery. Springer, Cham, pp 303–314

    Google Scholar 

  • Martins A, Martins P, Caldeira F, Sá F (2020) An evaluation of how big-data and data warehouses improve business intelligence decision making. In: World conference on information systems and technologies. Springer, Cham, pp 609–619

  • Meirelles F (2000) Fundação GetulioVargas-EscolaDeAdministraçãoDe Empresas De São Paulo. Pesquisa Anual De Tecnologia De Informação 13

  • Murray DG (2013) Tableau your data! fast and easy visual analysis with tableau software. Wiley, New York

    Google Scholar 

  • Naidoo SS (2019) Business intelligence systems input: effects on organizational decision-making. Capella University, Diss

    Google Scholar 

  • Namada JM (2018) O Organizational learning and competitive advantage. In: Handbook of research on knowledge management for contemporary business environments. IGI Global, pp 86–104

  • Olszak CM, Ziemba E (2003) Business intelligence as a key to the management of an enterprise. In: Proceedings of informing science and IT education conference

  • Ram J, Zhang C, Koronios A (2016) The implications of big data analytics on business intelligence: a qualitative study in China. Procedia Comput Sci 87:221–226

    Article  Google Scholar 

  • Sivarajah U et al (2017) Critical analysis of big data challenges and analytical methods. J Bus Res 70:263–286

    Article  Google Scholar 

  • Taurion C (1997) A Busca da Qualidade para Alavancar os Negócios. Developers’ Mag 10–11

  • Van Alsenoy B (2016) Regulating data protection: the allocation of responsibility and risk among actors involved in personal data processing

  • Velloso F (2014) Informática: conceitos básicos, vol 9. Elsevier, Rio de Janeiro

    Google Scholar 

  • Volitich D (2008) IBM Cognos 8 BI: the official guide. McGraw-Hill, New York

    Google Scholar 

  • Wieder B, Ossimitz M-L (2015) The impact of business intelligence on the quality of decision making-a mediation model. Procedia Comput Sci 64:1163–1171

    Article  Google Scholar 

  • Yeoh W, Koronios A (2010) Critical success factors for business intelligence systems. J Comput Inf Syst 50(3):23–32

    Google Scholar 

  • Yi X et al (2014) Building a network highway for big data: architecture and challenges. IEEE Netw 28(4):5–13

    Article  Google Scholar 

  • Yin S, Kaynak O (2015) Big data for modern industry: challenges and trends (point of view). Proc IEEE 103(2):143–146

    Article  Google Scholar 

Download references

Acknowledgements

“This work is funded by National Funds through the FCT Foundation for Science and Technology, IP, within the scope of the project Ref UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD), the Polytechnic of Viseu for their support.”

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Filipe Sá.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Martins, A., Abbasi, M., Martins, P. et al. BigData oriented to business decision making: a real case study in constructel. Comput Math Organ Theory 28, 271–291 (2022). https://doi.org/10.1007/s10588-021-09330-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10588-021-09330-3

Keywords

Navigation