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Franklin Associates, Ltd. (FAL) is developing a methodology to deal with the issues of uncertainty and data quality in Life Cycle Inventories (LCI). In traditional LCIs, single point estimates of input variables (such as fuel requirements) are used to determine single point estimates for the output variables (such as total energy used or solid waste generated). These point estimates contain no information about the uncertainty of the data, and therefore give a false sense of precision. If LCIs are to become more widely used by decision makers and others, an acceptable method of dealing with uncertainty needs to be developed. This paper discusses the data uncertainty methodology being developed at Franklin Associates, and uses a previously completed case study as a real-world example of its use. The FAL methodology involves the assignment of data quality indicators to the variables used as inputs to our computer models.