Rationale — Data quality is acceptable and meets the need for which it is intended. Data that is collected, produced, and reported must be fit for purpose. That is, of sufficient accuracy and integrity proportional to its use and cost of collection and maintenance. Data is used in all areas of the transportation decision-making process from planning to design to operations to performance management. Furthermore, it is increasingly being used externally by citizens and customers to inform their personal decisions, and by stakeholders to assess the aggregate performance of a transportation organization. Significant human and system resource is consumed in the collection, manipulation and dissemination of data whether of high quality or not, so it is essential that the most effective use of public funds is achieved through appropriately directed attention to data quality and the procedures to realize quality. Additionally, data must be archived appropriately to preserve both its usefulness and the historical record. When possible, data should be spatially oriented. Data quality increases as the application of the data increases. Data that has spatial orientation or attribution can easily be used in GIS systems. When data assets can be analyzed in a spatial context, not only can a greater analysis be completed in terms of geographic context, but also the data and any analysis results can be more easily communicated via mapping and other formats more applicable to public understanding.
Implications — When data is fit for purpose appropriate cost decisions are made in its collection and use. In cases where a rough sketch is appropriate, appropriate data collection and use may follow. Where large programs, investments, or systems are being developed and vetted, those data must be fit for that purpose. Data precision is matched to the task at hand.