We work with a lot of messy public data. In theory it’s already “structured” and published in machine readable forms like Microsoft Excel spreadsheets, poorly designed databases, and CSV files with no associated schema. In practice it ranges from almost unstructured to… almost structured. Someone working on one of our take-home questions for the data wrangler & analyst position recently noted of the FERC Form 1: “This database is not really a database – more like a bespoke digitization of a paper form that happened to be built using a database.” And I mean, yeah. Pretty much. The more messy datasets I look at, the more I’ve started to question Hadley Wickham’s famous Tolstoy quip about the uniqueness of messy data. There’s a taxonomy of different kinds of messes that go well beyond what you can easily fix with a few nifty dataframe manipulations. It seems like we should be able to develop higher level, more general tools for doing automated data wrangling. Given how much time highly skilled people pour into this kind of computational toil, it seems like it would be very worthwhile.
A couple of weeks ago I attended TWEEDS 2020 virtually (like everything this year) and talked about Catalyst’s ongoing Public Utility Data Liberation (PUDL) project, and especially the challenges of getting a big pile of data into the hands of different kinds of users, using different tools for different purposes. It ended up sketching out a bit of a PUDL infrastructure roadmap for the next year, and so we thought it would be a good idea to write it up here too.
We’ll have a separate post looking at our 2021 data roadmap.
The US Energy Information Asymmetry
PUDL is all about addressing a big information asymmetry in the regulatory and legislative processes that affect the US energy system. Utilities have much more information about their own systems than policymakers and advocates typically do. As a result, regulators often defer to the utilities on technical & analytical points. Commercial data exists, but it’s expensive. We want to get enough data into the hands of other kinds of stakeholders that they can make credible quantitative arguments to regulators, and challenge unfounded assertions put forward by utilities.