Electricity rates contain some of the most serially nested data tables out there – parsing through and showing narratives without getting lost is hard and getting harder by the year. If you are good enough at this kind of thing to post here, go get a job in the energy industry and thank me later.

I realize that most posts are of a single end infographic, but some graduating readers may be interested in a real professional use case that supports why hiring managers value data visualization as a broadly defined skillset.

Premise I had hunch was true: This dealership spends more charging their fleet these 6 EV fast charge stations in the 4 summer months than the rest of the year combined.

Primary Data Source: said dealership's electric meter 15 minute interval file which is a .csv format with 36000 rows and two columns (time and kwh). Accompanied by a verbal "we're on B-19 Rate".

Secondary Data Source: CA electric utility rate tariff ELEC_SCHEDS_B-19.pdf

This was just all in excel, so why do it this way even though other specialized tools like flourish, powerBI, etc. could make this look even more beautiful? Because I knew the recipient in this case uses excel daily for finance applications and could follow along with my work socratically step by step which adds trust. They also would place more value turning it around quickly in order to get feedback on the storyline before working. The reality of that world is that data pretty much comes into your world ugly everytime, and only by adding context and other information sources can you create something new for your reader that has never been created before.

Hard: writing the nested IF loop to assign the correct TOU lookup key for each row.
Harder: interpreting the tariff with the dozens of B-19 subclasses rate this meter would be read as.