Data, as a new type of asset, has huge potential value. However, due to the non-traditional material asset attributes of data assets, there are many difficulties in their valuation. This paper introduces optimized cost approach, optimized income approach, and optimized market approach, to address key challenges in data asset valuation, including low accuracy, inefficiency, limited applicability, lack of theoretical grounding, and implementation difficulties. Considering characteristics of data assets, this paper proposes a series of optimized approaches to evaluate data assets in real world. A case study involving a commercial bank is presented to validate the optimized approaches for evaluating the internal data assets of enterprises.