Business analytics and decision science: A review of techniques in strategic business decision making
1 Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, United States of America.
2 Dangote Sugar Refinery Plc, Lagos Nigeria.
3 Independent Researcher, Colorado.
4 Independent Researcher, UK.
5 Spacepointe Limited, River State, Nigeria.
6 Department of Information Management, Ahmadu Bello University, Zaria, Nigeria.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 1761–1769
Article DOI: 10.30574/wjarr.2024.21.2.0247
Publication history:
Received on 09 December 2023; revised on 17 January 2024; accepted on 19 January 2024
Abstract:
Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation for predictive analytics, which utilizes statistical models and machine learning algorithms to forecast future trends and outcomes. By leveraging predictive analytics, organizations can anticipate market shifts, customer preferences, and potential risks, thereby making informed decisions. Prescriptive analytics uses predictive models to guide strategic decision-making, utilizing optimization algorithms and simulation tools to identify optimal actions. Decision science integrates analytical techniques with human judgment, focusing on consumer behavior and psychological factors to tailor marketing strategies and product offerings. Additionally, artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing strategic decision-making by automating complex tasks and providing real-time insights. Natural language processing (NLP) algorithms analyze unstructured data sources, such as customer reviews and social media posts, to extract valuable information and sentiment analysis. This enables businesses to gauge customer satisfaction levels and identify areas for improvement promptly. Decision trees, regression analysis, and clustering techniques are widely used in business analytics to segment customers, identify patterns, forecast sales trends, evaluate alternatives, assess risks, and optimize resource allocation. In conclusion, business analytics and decision science offer a plethora of techniques that empower organizations to make informed, data-driven decisions. By leveraging descriptive, predictive, and prescriptive analytics, along with AI and ML technologies, businesses can navigate complex environments, capitalize on opportunities, and mitigate risks effectively. This review underscores the importance of integrating analytical techniques with human expertise to achieve strategic objectives and sustainable growth.
Keywords:
Decision Science; Business Analytics; Analytics; Artificial Intelligence; Review
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0