Define Synthetic Data Features

[amc-corey-cox]

We should define the features we need in our near-term and ideal synthetic data sets. Much of this is captured in the parent issue of this issue.

These are copied from there:

I think the main features of a good synthetic data set are:

• Contains no real data and thereby is immune from having any sensitive or potentially sensitive information
• Substantially represents one or more real-world data set we are targeting

I'll be using this issue to track our effort in creating gold standard synthetic data sets for the Data Model-Based Ingestion Pipeline.

Some key aspects of representing a data set are:

• similar data field names (exactly the same if reasonable)
• similar data field ranges
• similar data distribution
• similar data field discrepancies
• representation of missing values within appropriate fields
• representation of data errors within appropriate fields