Data product valuation: Pricing, risk, and ROI of enterprise datasets

The study lays emphasis on the implementation of predictive, market factor modelling and sensitivity-based simulations in the latest market conditions to increase accuracy and reliability of product valuation.Exploiting the historical transaction datasets, the author will use vendor-specific negotiations and portfolio optimization architecture to submit an overlay strategy where it intends to combine intrinsic elements (e.g.cost of production, quality rating) with the forecasted benefits of machine learning models.The technique involves ROI, volatility quadrant mapping, and slope, as well as Pareto analysis in procurement decision, along with integrated portfolio charts to determine trade-offs made mentioned in this paper with reference to the achievement of stable pricing strategies, facilitation of beneficial procurement negotiation and successful investment decision making in manufacturing, retail and a commodity-based business.