Implementing effective data quality control processes requires participation from subject matter experts, GIS practitioners, and interested stakeholders, both inside and outside an organization. Collecting, organizing, and managing data quality feedback from such a diverse community of participants is often a challenging and resource intensive effort.
Many organizations, especially those operating under constrained budgets and resources, are forced to short-cut or skip entirely data quality review. This means there is a lot of GIS data out there of dubious quality which often only reliably supports the work of a single team or project and not those of others.
Poor quality data impacts decision making during everyday situations that require accurate data to support multiple organizational needs. For example, utilities need to maintain accurate positions of their assets because incorrect information can impact their completion of construction or repair projects and outage notifications to customers.
Also, poorly implemented data quality practices negatively impact an organization's ability to improve its business processes, and deliver new services and other geo-enabled products. Business needs constantly evolve as organizations seek efficiencies in delivering services to customers while minimizing costs. The data services and the corresponding data quality requirements that support these evolving needs must also evolve. Data quality practices based on manual processes or custom applications hinder the ability to quickly assess whether data services will support the new activities and if not, the level of effort required to bring those services into compliance with the new business needs.