This conceptual paper will illustrate the impacts of low quality data, describe the requirements of the data cleaning process, compare the data cleaning process for paper versus ePRO, and provide recommendations of how ePRO can be implemented to decrease the level of effort of data cleaning. Before data can be analyzed, data cleaning must occur to ensure high data quality. Low quality data can have major impacts on data analysis/results along with cost impact.  As estimated by the Data Warehousing Institute, the costs of low quality data exceeds $600 billion annually. Data cleaning process includes querying for errors, typos, outliers, out-of-range responses, missing data, deviations, etc. Paper: query data for erroneous/out-of range values; these values need to be cross-checked with original paper form to identify if error is associated with patient entry or data entry staff. If associated with patient entry, it may need to be set as missing. When original paper is lost, values may need to be set to missing as accuracy cannot be confirmed. Time/dates may be out-of-range or missing, which require cross-checking with original paper. If time/date cannot be confirmed, the entire entry may need to be set as missing. Missing values in data need to be identified and cross-checked with original paper to confirm if value was skipped by patient or by data entry staff. ePRO: can be implemented to prevent entry of out-of range values; includes time/date stamps; patient direct data entry eliminates error by data entry staff; can be programmed to not allow skipped responses to prevent missing data responses. End-of-study time is precious to the pharma industry where results need to be analyzed for submissions. Data cleaning with paper can be labor intensive and ePRO can save time with preventing errors from occurring, reducing time needed for data cleaning.