  The American Community Survey (ACS) is the bedrock underpinning any analysis
of the US population, urban areas included. The Census Bureau delivers the ACS
data in multiple formats, yet in each the raw data is difficult to export in
bulk and difficult to sift through. We argue that Enigma's approach to the data
delivery, such as our raw data and metadata presentation, reflects the survey's
logical structure. It can be explored, interlinked, and searched; making it
easier to retrieve the appropriate data applicable to a question at hand. We
make the use of data more liquid via curated tables and API access; even
metadata and notes from technical documentation are programmatically
accessible. Additionally, we are working towards opening our scalable and
reproducible ingestion process of ACS estimations. This paper details all of
the ways the Census Bureau currently makes the data available, the barriers
each of these raise to applying this data in analysis and how our approach
overcomes them. Finally, this paper will address other recent innovations in
making Census datasets more usable, the use cases suited to each and how they
fit into the wider application of data science.
