Abstract: The approaches proposed for assessing the life cycle inventory analysis(LCI)data quality during a few last years can be classified into two main categories.The first uses a data quality indicator(DQI) such as geographic representation,age of data or data acquisition method.The second represents the overall LCI quality in terms of uncertainty.Both approaches have critical drawbacks. A method is presented that enables combining the data quality indicator with data uncertainty.Five independent data quality indicators are suggested as necessary and sufficient to describe attributes of data quality that influence the reliability of the result.The attributes are coded on a similar scale of 1～5　in order to create a vector element of quality index.The measure of arithmetic average of the data quality vector components as a percentage of the total quality range attainable is equivalent measure for the aggregate DQI that represents the total quality associated with the data element.Once the input DQIs are determined for a given LCI models,each input data element can be transformed to a random variable based on a representative probability distribution and used in stochastic LCA modeling.The method is used to the production of steel to demonstrate the application feassible.