Leveraging Predictive Analytics for Data-Driven Decision-Making in Enterprise Systems
Keywords:
Predictive Analytics, Enterprise Systems, Data-Driven Decision-Making, Machine LearningAbstract
Predictive analytics has, in turn, become a pivotal instrument used to optimize decision-making in enterprises based on historical data and statistical and machine learning to predict future outcomes. This article will discuss the integration of predictive analytics with enterprise software, including Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM). To evaluate both quantifiable results and situational issues, a mixed method based on literature research, reports, and case studies will be followed up on. Results suggest that predictive analytics enhances accuracy in forecasting, minimizes business operations cost, enhances customer satisfaction, and gives businesses a competitive advantage. Nevertheless, enterprises still have to deal with the poor quality of data, technical and fiscal barriers, lack of skills, and ethical issues regarding biases and transparency. The best practices, such as initiating pilot projects, investing in governance and infrastructure, establishing collaboration between units, and modernizing models, along with constant staff training, are defined as the ways to meet the effective adoption. Future trends such as automated machine learning, AI-powered decision engines, IoT-enabled edge analytics, and responsible AI are also addressed as factors of further development. The argument presented is that predictive analytics is a technological innovation and a strategic requirement of enterprises that want to achieve sustainable competitiveness in a data-driven economy.
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