Data quality and quality control are critical components in epidemiology research and related extension activities. The final results of disease warning systems can be no better than the data used to produce them. Data quality begins to be addressed in data collection with well defined protocols that provide standardized methods for making observations, and continues to be a focus in data management through production of a final “product”. The Pest Information Platform for Extension and Education (PIPE) from its inception in 2005 as a soybean rust real time observation and information system to its current objective of providing that information for more crops and pests, has continued to improve and refocus the data integrity components to assure that data collected can be used for decision making, research, and forecasting. It is critical to the continued success of the PIPE to pursue well developed collection and confirmation protocols that foster quality data collection and data management techniques. The challenges faced by the program include standardizing data collection to assure quality while allowing for variability that is very real in the field. Numerous mechanisms were put in place to address data quality from the field to end use, such as formulation and distribution of a Standard Operating Procedure (SOP) for confirmation, along with hands-on-training for field identification and lab confirmation. To assure quality control in data management, checks were put in place to assess integrity of data and to block data that did not meet those checks. Data collection was improved by soliciting feedback from pathologists and entomologists from the very beginning with protocol writing process, to the data entry, and finally the construction of final products. Another essential part that greatly assisted in quality control was the establishment of continuous and regular communication with experts from various areas.