Intelligent Data Analytics for Evidence-Based Innovation Decisions

Kezang Sherab

Intelligent Data Analytics for Evidence-Based Innovation Decisions

Author(s)	Kezang Sherab
Country	Bhutan
Abstract	The increasing complexity of modern business and organizational environments has created a strong requirement for intelligent systems capable of supporting accurate, timely, and evidence-based innovation decisions. Traditional decision-making approaches often depend on limited information, historical experience, and manual analysis processes, which may become insufficient when organizations operate within rapidly changing technological and competitive landscapes. Intelligent data analytics has emerged as a strategic capability that enables organizations to transform large volumes of structured and unstructured data into meaningful insights for innovation planning, evaluation, and implementation. This research examines Intelligent Data Analytics for Evidence-Based Innovation Decisions by analysing how advanced analytical technologies support organizations in identifying opportunities, predicting market trends, evaluating innovation performance, and improving strategic decision-making. The study adopts a conceptual research approach based on theoretical perspectives related to data analytics, artificial intelligence, innovation management, knowledge discovery, and organizational intelligence. The analysis highlights that intelligent analytics frameworks integrate artificial intelligence, machine learning, predictive modelling, and data visualization techniques to enhance innovation decision processes. These technologies enable organizations to identify hidden patterns, evaluate emerging opportunities, optimize resource allocation, and develop more effective innovation strategies. The study identifies important challenges associated with intelligent data analytics, including data quality issues, analytical complexity, privacy concerns, algorithmic bias, technological dependency, and the requirement for skilled analytical professionals. Successful implementation requires strong data governance, analytical capabilities, ethical practices, and alignment between technology and organizational objectives. The research concludes that intelligent data analytics provides a powerful foundation for evidence-based innovation decisions. By combining computational intelligence with human expertise, organizations can develop more adaptive, informed, and sustainable innovation strategies capable of responding to future uncertainties.
Keywords	Intelligent Data Analytics, Evidence-Based Decisions, Innovation Management, Artificial Intelligence, Predictive Analytics, Data-Driven Strategy, Organizational Intelligence.
Field	Engineering
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-13

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American International Journal of Research and Innovation

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Intelligent Data Analytics for Evidence-Based Innovation Decisions

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