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.








Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.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
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
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
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
Färber F et al (2012) SAP HANA database: data management for modern business applications. ACM Sigmod Rec 40(4):45–51
Gligor G, Teodoru S (2011) Oracle analytics: engineered for speed-of-thought analytics. Database Syst J 2(4):3–8
Grover V et al (2018) Creating strategic business value from big data analytics: a research framework. J Manag Inf Syst 35(2):388–423
Guarani G, Helmer S (2012) Integrating star and snowflake schemas in data warehouses. Int J Data Wareh Min (IJDWM) 8(4):22–40
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
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
Lachev T, Price E (2018) Applied microsoft power BI: bring your data to life! Prologika Press, New York
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
Marquesone R (2016) Big Data: Técnicas e tecnologias para extração de valor dos dados. Editora Casa do Código, São Paulo
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
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
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
Naidoo SS (2019) Business intelligence systems input: effects on organizational decision-making. Capella University, Diss
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
Sivarajah U et al (2017) Critical analysis of big data challenges and analytical methods. J Bus Res 70:263–286
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
Volitich D (2008) IBM Cognos 8 BI: the official guide. McGraw-Hill, New York
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
Yeoh W, Koronios A (2010) Critical success factors for business intelligence systems. J Comput Inf Syst 50(3):23–32
Yi X et al (2014) Building a network highway for big data: architecture and challenges. IEEE Netw 28(4):5–13
Yin S, Kaynak O (2015) Big data for modern industry: challenges and trends (point of view). Proc IEEE 103(2):143–146
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
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10588-021-09330-3