How advanced analytics optimizes the efficiency and productivity of business process
Keywords:
business process, efficiency, productivity, advance analyticsAbstract
This paper explores how advanced analytics can enhance business process performance by examining key techniques, implementation pathways, benefits, and potential obstacles. Advanced analytics encompasses data mining, machine learning, and both predictive and prescriptive approaches. It is increasingly embedded in organizational workflows to improve efficiency, productivity, and competitiveness. The discussion covers methods such as process mining, predictive modeling, prescriptive optimization, automation, and artificial intelligence, alongside strategic enablers including planning, change management, infrastructure readiness, workforce training, and continuous performance tracking. The study outlines tangible advantages, from operational streamlining and productivity gains to more informed decision-making, supported by cross-industry case illustrations. At the same time, it acknowledges persistent challenges such as data privacy risks, integration complexities, and resistance to organizational change. Finally, it recommends future research into emerging AI-driven technologies, enhanced safeguards for data governance, and the cultivation of a strong culture of evidence-based decision-making.
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