From Data to Decisions: Harnessing AI and Analytics
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P109Keywords:
Data-driven decision making, Artificial Intelligence (AI), Analytics, Predictive analytics, Data science, Machine learning, Business intelligence, Decision support systems, Data insights, Data-to-decision pipelineAbstract
Challenges of the increased volume of data and their inherent complexity are being experienced across industries and even countries at the same time. With such prospects, the opportunities to utilize artificial intelligence (AI) and advanced analytics to improve decision-making become unprecedented. This paper presents a general framework that transforms raw data into actionable knowledge, which involves machine learning models, statistical inference, and explainable AI. Using recent developments in AI-enabled analytics and decision support, the study makes use of existing cross-sector literature. It shows a reproducible approach to constructing a synthetic, cross-sector dataset including economic, environmental, health, and social performance indicators. Predictive and prescriptive analytic pipelines have been constructed, and their performance was compared with standard evaluative measures by using different algorithms. Explainability practices and feature importance were implemented to increase transparency and confidence of the stakeholders in the AI-generated recommendations. Experiments indicate that the suggested framework is entirely capable of making interpretable and high-precision forecasts and flexible judgment assistance in various ways. Additional contributions of the study are a generalized approach that can be implemented in various industries, empirical evidence introduced by representative real-world datasets, and open source code to ensure complete reproducibility. Such results add to the promise of AI and analytics as an amenity between data and informed, honest decision-making in sophisticated and multi-domain landscapes
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