Not long ago, obtaining data for a GIS-based project was an arduous task. Because great time and effort was involved with either creating your own data or obtaining data that someone else created, you had to think carefully about the quality of the data that would go into your project. While it can still be cumbersome to obtain data at specific scales for specific areas, cloud-based data services, crowdsourced maps and databases and real-time streaming make it easy for anyone to obtain vast amounts of data in a short amount of time. In an environment where so much data is available, is data quality still of concern? I believe that yes, data quality does matter. In fact, because it is so easy to obtain data nowadays, and with the advent of crowdsourcing and cloud-based GIS, I submit that data quality considerations actually matter now more than ever. Consider the following three examples that focus on criticizing, analyzing and scaling your data.