[ianhorn]

I'd like to see something that could do this, handling the awfulness of real world tabular data. "What country has the highest GDP? Okay, which table has GDP? Is it the country_gdp table? No, that's an old one that hasn't been written to in 3 years. Ah here it is, but you need to join against `geopolitics`, but first dedup the crimea data, since it's showing up in two places, we're can't remember why it got written to twice there. Also, you need to exclude June 21 because we had an outage on the brazil data that day. What do you mean some of the country_id rows are NULL?" And so on. I dream that someday there's a solution for that. That's a looooong ways away, I'd bet.

[lacker]

The tough thing is that a common failure mode of many of the modern AI solutions is some output that looks superficially correct, but doesn't actually map correctly to the real world. When you want a table of data, it seems like the danger will be high that the table looks correct but isn't actually accurate. The problem here is about keeping sloppy data out of your table, which is tough for a statistical AI.

So yeah, I would expect this to be a long ways away.

[ianhorn]

For more interesting problems than "which country has the highest GDP?", it's about more than just sloppy data. If you want to include any covariates, how do you know which ones to include? You could try to include everything predictive, but then you'll use the client margin column to predict client revenue or something. Or you'll control for a column causally downstream, biasing your estimates, like estimating revenue differences and controlling for page views in an experiment that affects page views. There's so much that we just don't include in our databases that's crucial to using them, and it's not just about sloppiness.

[djohnston]

tbf that problem is also quite tough for data scientists, the model doesnt need to be flawless just better

[vosper]

This was my experience trying to work with the Johns Hopkins COVID data.

I don't know if Johns Hopkins became the canonical data source because they were amongst the first to have public data and charts, but honestly I was kinda surprised at the low quality, coming from a group called "Center for Systems Science and Engineering". Their data was far harder to use than it needed to be, even months into the pandemic.

Fortunately there were a handful of other projects dedicated to making it sane and resolving the inconsistencies, unreconciled changes in format, etc... that was really helpful.

[chmullig]

Which projects do you think have particularly high quality and easy to work with data? I was using that but it’s so messy...

[mmsimanga]

Metabase[0] lets you ask the question step by step. You pick the table, pick the filter, pick the aggregation and so on. I have been working in BI long enough to know that even this isn't going to answer all questions. It is cleaning data, filtering out stuff that shouldn't be included in the data set that somehow is included and other issues that makes it difficult to automate. The is usually a bunch of things not documented. I will be watching this with interest.

[0]https://www.metabase.com/

[riku_iki]

The problem which TaBERT supposedly can solve is speed of this process, you can have hundreds of tables, with dozens of columns with many unobvious relationships, and constructing query manually can take lot's of effort even using UI automation. And the idea is that you have some system, which creates this query instantly following short description.

[mlthoughts2018]

This could be seriously addressed with a configurable rule system similar to email filters + search. You have to store any of the metadata factors you want to consider in a companion index that can allow complex filtering or decision tree splits, then for introspection of SQL-like data sources, you can follow key and type relationships to determine what’s joinable.

Perhaps outputting several potential answers at the end, each explaining the “pathway” it chose to use (filters / decision tree splits + graphical path through keys / joinable types in the underlying data), and allow the user to select one or more results that they believe are valid pathways of criteria, or perhaps tweak individual filters and joins in the listed pathway for a given result.

I think this would offer a lot more value than trying to get a full natural language interface that “just works” on complex filtering conditions, where getting just one answer back (instead of seeing the variety of pathways the system could choose and what influence each step has on the end result) entails too many cases the ML system fails with unrealistic results.

[iandanforth]

Out of curiosity have you tried any of the data catalogs? Alation, Informatica, Collibra? Not that they solve this problem for you but that make it solvable.

[kfk]

What do these do? I am constantly told by IT we need data catalogues, they talk about it, they invest money, but I have never seen the results. Me and my team keep most metadata info of tables in json files, not pretty but at least I can see what it does