Providing confidence in life-cycle inventories (LCI) is dependent on being able to understand the source and extent of uncertainties in data and in the results produced with the data. From a situation several years ago of nearly no methodology for data quality considerations to a future where sophisticated data modeling approaches allow decision makers to obtain quantitative indications of the differentiability of alternatives, the science and art of data quality assessment are advancing rapidly. This paper provides perspective on why and how data quality issues are critical to successful implementation of LCI results and an overview of how practitioners are responding to the need for enhanced data quality assessment procedures. These procedures range from incorporation of individual data quality indicators to statistically-based models for estimation of parameter distributions. Careful consideration of data quality can markedly improve the interpretation and utility of LCIs.