Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company

Oluwaseun Oladeji Olaniyi *

University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.

Anthony Idoko Abalaka

Ashland University, 401 College Avenue, Ashland OH, 44805, United States of America.

Samuel Oladiipo Olabanji

Midcontinent Independent System Operator (Miso energy) 720 City Center Drive, Carmel, Indiana 46032, United States of America.

*Author to whom correspondence should be addressed.


Abstract

The present study evaluates Walmart’s existing big data analytics with business intelligence techniques, accentuating their strengths and weaknesses, and suggests improvements for implementation and maintenance through the literature review of the scholarly journals addressing similar topics. Big data analytics is receiving loads of attention globally in the business environment within every sector of the economy.  Incorporating the job plan as an additional input component in their models would be beneficial for Walmart to improve the precision and appropriateness of their data analysis and decision-making procedures. Walmart is a company that heavily invests in utilizing big data to improve its operations; this includes optimizing in-store experiences and predicting product trends. Scholarly articles emphasize the importance of advanced data analytics tools like MapReduce and Apache Spark for effective big data strategies. Social media content influences engagement and sentiment. Social network data aids sales forecasting but presents challenges. Big data analytics with business intelligence enhances performance and decision-making. Walmart's success in big data analytics relies on a data-driven culture but faces security challenges. Many Fortune 1000 companies adopt innovative solutions to improve performance and customer experiences but require significant resources. Embracing big data analytics with business intelligence remains a compelling investment for sustaining a competitive edge. Walmart's success in big data analytics is due to a data-driven culture and advanced infrastructure, including the Data Café.

Keywords: Big data analytics, information technology, business intelligence, walmart


How to Cite

Olaniyi, Oluwaseun Oladeji, Anthony Idoko Abalaka, and Samuel Oladiipo Olabanji. 2023. “Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company”. Journal of Scientific Research and Reports 29 (9):64-72. https://doi.org/10.9734/jsrr/2023/v29i91785.

Downloads

Download data is not yet available.

References

Bogdan M, Borza A. Big data analytics and organizational performance: meta-Analysis study. Management and economics review. 2019;4(2),147–162. Available:https://doi.org/10.24818/mer/2019.12-06

Mariani M, Baggio R, Fuchs M, Höepken, W. Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 2018;30(12):3514-3554.

Available:https://doi.org/10.1108/IJCHM-07-2017-0461

Fortune. Fortune 500:2023. Available:https://fortune.com/ranking/fortune500/

ProjectPro. How has big data analysis helped increase Walmart's sales turnover? Iconiq Inc.2023.

Available:https://www.projectpro.io/article/how-big-data-analysis-helped-increase-walmarts-sales-turnover/109

Marr B. Walmart: big data analytics at the world’s biggest retailer. Bernard Marr & Co; 2021.

Available:https://bernardmarr.com/walmart-big-data-analytics-at-the-worlds-biggest-retailer/

Reed, C. (2023, April 17). Walmart data breaches: full timeline through. Firewall Times; 2023.

Available:https://firewalltimes.com/walmart-data-breaches/

Mühlhoff R. Predictive privacy: Towards applied ethics of data analytics. Ethics and Information Technology, 2021;23(4):675-690. Available:https://doi.org/10.1007/s10676-021-09606-x

Sen R, Jindal A, Patel H, Qiao S. AutoToken: predicting peak parallelism for big data analytics at Microsoft. Proceedings of the VLDB Endowment, 2020;13(12),3326–3339. Available:https://doi.org/10.14778/3415478.3415554

Singh M, Ghutla B, Lilo Jnr R, Mohammed AFS, Rashid MA. Walmart’s Sales Data Analysis - A Big Data Analytics Perspective. 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE); 2017;114–119. Available:https://doi.org/10.1109/APWConCSE.2017.00028

Chun H, Leem B-H, Suh H. Using text analytics to measure the effect of topics and Sentiments on social-media engagement: Focusing on the Facebook fan page of Toyota. International Journal of Engineering Business Management. 2021;13. Available:https://doi.org/10.1177/18479790211016268

Said F, Zainal D, Azlina AJ. Big data analytics capabilities (BDAC) and Sustainability reporting on Facebook: Does tone at the top matter? Cogent Business & Management. 2023;10(1).

https://doi.org/10.1080/23311975.2023.2186745

Rodrigues AP, Fernandes R, Bhandary A, Shenoy AC, Shetty A, Anisha M. Real-time Twitter trend analysis using big data analytics and machine learning techniques. Wireless Communications & Mobile Computing (Online); 2021.

Available:https://doi.org/10.1155/2021/3920325

Boldt LC, Vinayagamoorthy V, Winder F, Schnittger M, Ekran M, Mukkamala RR, Lassen NB, Flesch B, Hussain A, Vatrapu, R. Forecasting Nike’s sales using Facebook data. 2016 IEEE International Conference on Big Data (Big Data). 2016;2447–2456. https://doi.org/10.1109/BigData.2016.7840881

Maddodi S, Krishna PK. Netflix big data analytics- the emergence of data-driven Recommendation. International Journal of Case Studies in Business, IT, and Education (IJCSBE), 2019;3(2):41-51.

Available:https://doi.org/10.5281/zenodo.3510316

Olagbaju OO, Babalola RO, Olaniyi OO. Code Alternation in English as a Second Language Classroom: A Communication and Learning Strategy. Nova Science; 2023. Available:https://doi.org/10.52305/YLHJ5878

Olagbaju OO, Olaniyi OO. xplicit and Differentiated Phonics Instruction on Pupils’ Literacy Skills in Gambian Lower Basic Schools. Asian Journal of Education and Social Studies. 2023;E 44(2):20–30.

Available:https://doi.org/10.9734/ajess/2023/v44i2958

Olaniyi OO, Okunleye OJ, Olabanji SO. Advancing data-driven decision-making in smart cities through big data analytics: A comprehensive review of existing literature. Current Journal of Applied Science and Technology. 2023;42(25):10–18. Available:https://doi.org/10.9734/cjast/2023/v42i254181

Olaniyi OO, Olaoye OO, Okunleye OJ. Effects of Information Governance (IG)on profitability in the Nigerian banking sector. Asian Journal of Economics, Business and Accounting. 2023;23(18): 22–35.

Available:https://doi.org/10.9734/ajeba/2023/v23i181055

Olaniyi OO. Omubo DS. The Importance of COSO Framework Compliance in Information Technology Auditing and Enterprise Resource Management. The International Journal of Innovative Research & Development; 2023

Available:https://doi.org/10.24940/ijird/2023/v12/i5/MAY23001

Olaniyi OO, Omubo DS. WhatsApp Data Policy, Data Security, And Users’ Vulnerability. The International Journal of Innovative Research & Development; 2023. Available:https://doi.org/10.24940/ijird/2023/v12/i4/APR23021