Hi @breyten -
First a clarification - the Fiscal Data Package is a standard for packaging fiscal data with its metadata in a ‘data package’. The datapackage uses a json file to describe its contents - which is usually CSV files when working with tabular data, but could be any other file as well. Similarly, XBRL uses XML as its underlying data format.
So, to rephrase your question - why is a CSV file better than an XML file? Because everyone has software that can open and analyze CSV files, while almost no one has the necessary skills or tools to work with an XBRL flavored XML file.
This example touches the fundamental difference between FDP and XBRL - FDP was designed as an open standard, while XBRL wasn’t. Each part of the FDP standard is meant to be as portable and compatible as possible with as many tools and systems. It is much less strict than XBRL, and will adapt and embrace various fiscal systems and methods. It has extensive tooling, and as mentioned above, one doesn’t even need to have it to make use of the data.
XBRL, on the other hand, is very complex. It’s quickstart documentation in the official website advises not to attempt and work with it on your own, but to use one of the available commercial software suites instead. The ‘hello world’ package is a zip file containing 40 different, interconnected, XML files.
Now, don’t get me wrong - being strict and complex does have its merits; for example, in case you’re a regulator that needs to inspect large financial institutions. However, when we talk about making data accessible and useful to the public, openness, usability and simplicity trump strictness.
As to how to model the data -
In my experience (working with government budgets and spending data from all around the world), the best way to publish data is in its most denormalized form. Publishing structured data will always make it more cumbersome to process and analyze, and is often better represented as a combination of inter-connected flat tables. These flat tables support hierarchies (even multiples of them, e.g. administrative, functional & economic classifications in budget files etc.).
If you’d like, we can schedule a call where we will go over your data, a few examples from around the world and see what model fits best your needs.
/cc @sandervdwaal @pwalsh