Design of a Data Mart for Optimizing Product Sales Analysis at PT. X
Abstract
With the advancement of technology, companies can now efficiently manage and analyze their data, providing valuable insights that support better business decisions. PT. X is one such company that wants to leverage this capability to optimize its sales data analysis. Therefore, the goal of this study is to design an effective data mart. During the development process, we used Kimbal's Nine-Step Methodology alongside the ETL (extract, transform, load) process to ensure the data was accurately extracted, transformed, and loaded into the data mart. The outcome of this research is a data mart and star schema tailored to PT. X specific needs. Testing results showed that query execution time increased by 40% and data accuracy improved by 80%. Report generation time was also optimized, resulting in a process that was 83% faster. These results demonstrate how a well-structured data mart can improve decision-making efficiency and data reliability.
Keywords
References
L. Yu, “User experience of English online classroom E-learning based on routing algorithm and data visualization analysis,” Entertainment Computing, vol. 51, p. 100733, Sep. 2024. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S1875952124001010
F. Fonggo, J. T. Beng, and D. Arisandi, “Web-based canteen payment and ordering system,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 1007, no. 1, p. 012159, Dec. 2020. [Online]. Available: https://iopscience.iop.org/article/10.1088/1757-899X/1007/1/012159
L. Allen, J. Atkinson, D. Jayasundara, J. Cordiner, and P. Z. Moghadam, “Data visualization for Industry 4.0: A stepping-stone toward a digital future, bridging the gap between academia and industry,” Patterns, vol. 2, no. 5, p. 100266, May 2021. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S2666389921000921
F. Gurcan, G. D. Boztas, G. G. M. Dalveren, and M. Derawi, “Digital transformation strategies, practices, and trends: A large-scale retrospective study based on machine learning,” Sustainability, vol. 15, no. 9, p. 7496, 2023. [Online]. Available: https://www.mdpi.com/2071-1050/15/9/7496
S. Kraus, P. Jones, N. Kailer, A. Weinmann, N. Chaparro-Banegas, and N. Roig-Tierno, “Digital transformation: An overview of the current state of the art of research,” Sage Open, vol. 11, no. 3, p. 21582440211047576, Jul. 2021. [Online]. Available: https://journals.sagepub.com/doi/10.1177/21582440211047576
P. C. Verhoef, T. Broekhuizen, Y. Bart, A. Bhattacharya, J. Q. Dong, N. Fabian, et al., “Digital transformation: A multidisciplinary reflection and research agenda,” J. Bus. Res., vol. 122, pp. 889–901, Jan. 2021. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S0148296319305478
VSONE World, “The necessity of data mart in data analytics.” [Online]. Available: https://www.vsoneworld.com/the-necessity-of-data-mart-in-data-analytics
IBM, “Data mart: Definition, benefits, and use cases.” [Online]. Available: https://www.ibm.com/think/topics/data-mart
Tencent Cloud, “What is a data mart?” [Online]. Available: https://www.tencentcloud.com/techpedia/108029
R. J. Mositsa, J. A. Van Der Poll, and C. Dongmo, “Towards a conceptual framework for data management in business intelligence,” Information, vol. 14, no. 10, p. 547, Oct. 2023. [Online]. Available: https://www.mdpi.com/2078-2489/14/10/547
A. Martins, P. Martins, F. Caldeira, and F. Sa, “An evaluation of how big-data and data warehouses improve business intelligence decision making,” in Proc. Int. Conf. on Trends and Applications in Information Systems and Technologies, Springer, 2020, pp. 609–619. [Online]. Available: http://link.springer.com/10.1007/978-3-030-45688-7_61
A. B. Mohammed, M. Al-Okaily, D. Qasim, and M. K. Al-Majali, “Towards an understanding of business intelligence and analytics usage: Evidence from the banking industry,” Int. J. Inf. Manage. Data Insights, vol. 4, no. 1, p. 100215, Apr. 2024. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S2667096824000041
M. J. Hamad, M. M. Yassin, O. S. Shaban, and A. H. Amoush, “Using business intelligence tools in accounting education,” in Cutting-Edge Business Technologies in the Big Data Era, S. G. Yaseen, Ed. Cham: Springer Nature Switzerland, 2023, pp. 163–177. [Online]. Available: https://link.springer.com/10.1007/978-3-031-42463-2_16
S. G. Yaseen, Ed., Digital Economy, Business Analytics, and Big Data Analytics Applications, Cham: Springer International Publishing, 2022. [Online]. Available: https://link.springer.com/10.1007/978-3-031-05258-3
R. Kimball and M. Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd ed. Hoboken, NJ: John Wiley & Sons, 2013.
W. H. Inmon, Building the Data Warehouse, 4th ed. Hoboken, NJ: John Wiley & Sons, 2002.
R. Kimball and M. Ross, The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd ed. Hoboken, NJ: John Wiley & Sons, 2011.
T. C. Hammergren, Data Warehousing for Dummies, Hoboken, NJ: John Wiley & Sons, 2009.
V. Rainardi, Building a Data Warehouse. Berkeley, CA: Apress, 2008. [Online]. Available: http://link.springer.com/10.1007/978-1-4302-0528-9
C. Côté, M. Lah, and M. Saitakhmetova, ETL with Azure Cookbook: Practical Recipes for Building Modern ETL Solutions to Load and Transform Data from Any Source. Birmingham, UK: Packt Publishing Ltd, 2020.
A. Dhaouadi, K. Bousselmi, M. M. Gammoudi, S. Monnet, and S. Hammoudi, “Data warehousing process modeling from classical approaches to new trends: Main features and comparisons,” Data, vol. 7, no. 8, p. 113, Aug. 2022. [Online]. Available: https://www.mdpi.com/2306-5729/7/8/113
M. B. Biplob and M. M. Haque, “Development of an efficient ETL technique for data warehouses,” in Emerging Technologies in Data Mining and Information Security, M. S. Arefin et al., Eds. Singapore: Springer, 2022, pp. 243–255. [Online]. Available: https://link.springer.com/10.1007/978-981-16-6636-0_20
A. Walha, F. Ghozzi, and F. Gargouri, “Data integration from traditional to big data: Main features and comparisons of ETL approaches,” J. Supercomput., vol. 80, no. 19, pp. 26687–26725, Dec. 2024. [Online]. Available: https://link.springer.com/10.1007/s11227-024-06413-1
F. R. Kodong, N. M. Shanono, and M. A. A. Al-Jaberi, “The monitoring infectious diseases diffusion through GIS,” SciTech Framework, vol. 2, no. 1, pp. 23–33, 2020. [Online]. Available: https://scholar.google.com/scholar?cluster=15451960743116964518&hl=en&oi=scholarr
L. Dinesh and K. G. Devi, “An efficient hybrid optimization of ETL process in data warehouse of cloud architecture,” J. Cloud Comput., vol. 13, no. 1, p. 12, Jan. 2024. [Online]. Available: https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-023-00571-y
How To Cite This :
Refbacks
- There are currently no refbacks.
