9 Data quality, cleaning, and preparation
[Content to be extracted from existing chapters and developed]
9.1 Assessing data quality
What quality are the data (Fan 2015)? Measurement error? Are observations missing? How frequently is it collected? Is it available historically, or only in real-time? Do the data have documentation describing what it represents? These are but a few questions whose answers may impact your project or approach. By extension, it affects what and how you communicate.