Research on Corporate Portfolio Optimization Based on Data Analysis

In the era of the digital economy, enterprise portfolio management is undergoing a paradigm shift from experience-driven to data-driven. With the deep integration of financial technology and business intelligence, massive transaction data, supply chain information, and market opinions constitute new decision-making elements, but data silos, outdated algorithmic models, and other problems constrain the release of investment performance. Currently, enterprises are generally facing the contradiction between the rigidity of portfolio structure and the dynamic market environment; the traditional financial index-led evaluation system is difficult to capture the growth potential of emerging industries; and the value mining of unstructured data, such as carbon footprint tracking, is still in the exploratory stage. The "data redundancy trap" in some industries is a cause for alarm, with energy companies over-relying on historical capacity data leading to biased valuation of new energy projects, and retailers neglecting the time-series characteristics of consumer behavior, resulting in inaccurate forecasts of inventory turnover. These practical dilemmas reveal that the establishment of a synergistic mechanism between data governance frameworks and intelligent analytics models has become the key to breaking through the bottleneck. Portfolio optimization is not only about resource allocation efficiency, but also a strategic fulcrum for enterprises to build digital competitiveness, the value of which extends to supply chain resilience enhancement, ESG strategy implementation, and other deep dimensions.