Everyone is talking or attempting to use Artificial Intelligence, Machine Learning, or AI. Industry leaders would say that data is the most important ingredient to 'make' AI work. A common theme for the proper or efficient use of AI is a need for 'good data' or data of the highest quality. GIS Practitioners have coined the phrase 'Garbage In and Garbage Out' as a rallying cry for better data since the formation of the discipline. Most organizations have some set or repository of data they utilize for their business, and some even create their data. The state of quality of these data repositories can fluctuate from abysmal to pristine. So how can organizations improve the quality of existing data or ensure that the data being collected now, is the best the organization can make it? This presentation claims that data must be valued as highly as revenue or profits if data quality is to improve. If data are valued highly, then care is taken as it is collected, compiled, and collated, and arguably, quality will improve, and there is a fighting chance for the use of AI to succeed for whatever purposes is required.
