Optimizing Business Decision-Making with Advanced Data Analytics Techniques

• Author(s): Oluchukwu Modesta Oluoha ; Abisola Odeshina ; Oluwatosin Reis ; Friday Okpeke ; Verlinda Attipoe; Omamode Henry Orieno
• Paper ID: 1703887
• Page: 184-203
• Published Date: 27-12-2022
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 5 November-2022

Abstract

In today’s dynamic business environment, data-driven decision-making has become a cornerstone of competitive advantage. Advanced data analytics techniques, including machine learning, predictive modeling, and big data processing, offer organizations the ability to extract meaningful insights, enhance operational efficiency, and improve strategic decision-making. This paper explores how businesses can leverage advanced data analytics to optimize decision-making processes, reduce uncertainty, and drive growth. The study examines various data analytics methodologies such as descriptive, diagnostic, predictive, and prescriptive analytics, illustrating their distinct roles in business intelligence. Descriptive analytics provides historical insights, while diagnostic analytics identifies underlying patterns and causes. Predictive analytics employs statistical models and machine learning to forecast future outcomes, and prescriptive analytics offers actionable recommendations based on predictive insights. The integration of artificial intelligence (AI) and machine learning in business analytics has revolutionized decision-making by automating data processing and enhancing real-time insights. These technologies enable businesses to identify trends, optimize resource allocation, and improve customer experiences through personalized recommendations. Additionally, big data analytics allows organizations to process vast volumes of structured and unstructured data, facilitating more accurate and data-driven strategies. Despite the potential benefits, implementing advanced data analytics presents challenges such as data quality issues, integration complexities, and ethical concerns regarding data privacy. Businesses must invest in robust data governance frameworks, skilled personnel, and scalable infrastructure to maximize the value of data analytics initiatives. Moreover, fostering a data-driven culture within organizations is essential for ensuring that decision-makers effectively utilize analytical insights. This research highlights successful case studies demonstrating the impact of advanced data analytics on various industries, including finance, healthcare, retail, and manufacturing. The findings underscore the transformative role of data analytics in enabling organizations to make informed, agile, and strategic decisions. Future research should focus on emerging trends such as explainable AI, real-time analytics, and the ethical implications of AI-driven decision-making. By harnessing advanced data analytics techniques, businesses can enhance their decision-making capabilities, achieve operational excellence, and sustain long-term growth in an increasingly data-centric world.

Keywords

Data Analytics, Decision-Making, Predictive Modeling, Machine Learning, Big Data, Business Intelligence, Artificial Intelligence, Prescriptive Analytics, Data-Driven Strategies, Business Optimization.

Citations

IRE Journals:
Oluchukwu Modesta Oluoha , Abisola Odeshina , Oluwatosin Reis , Friday Okpeke , Verlinda Attipoe; Omamode Henry Orieno "Optimizing Business Decision-Making with Advanced Data Analytics Techniques" Iconic Research And Engineering Journals Volume 6 Issue 5 2022 Page 184-203

IEEE:
Oluchukwu Modesta Oluoha , Abisola Odeshina , Oluwatosin Reis , Friday Okpeke , Verlinda Attipoe; Omamode Henry Orieno "Optimizing Business Decision-Making with Advanced Data Analytics Techniques" Iconic Research And Engineering Journals, 6(5)