BUSINESS INTELLIGENCE AND DATA MINING INTEGRATION IN TECH STARTUPS TO ENHANCE VENTURE CAPITAL ASSESSMENT

This research studies the combined use of business intelligence (BI) with data mining approaches to strengthen VC organizations' examination of technology startup ventures. A hybrid predictive approach connecting real-time market signals with analytical models addresses information disparities and valuation complexities implicated in early-stage assessment. Supervised machine learning techniques deploy deep neural networks combined with ensemble approaches to analyze multiple dataset dimensions that incorporate financial metrics with technological capabilities as well as market dynamics and team member characteristics. NLP, patent analytics and social signals enable data extraction for both structured and unstructured sources. The outcomes from this research include measurable bias reduction in investment decisions combined with more precise startup success prediction and uniformed protocols for VC risk evaluation.