Like, my default is to throw a dataset I'm hacking on into an SQL database so I can actually query the thing. But no I don't want to upload my 400MB log file. I'll just use grep, or build a CSV and deal with Excel filtering.
Airtable should be awesome at reducing the cost of database-ifying these random datasets to zero. But the sales constraints put it in this niche where it's not the default tool of choice.
Disclaimer, I’m the founder of Baserow.
I found what Airtable is doing to be deeply attractive. But their costs and their lock in and their pricing model and it's just...
UGH.
Microsoft Access was a good idea with a terrible implementation.
There HAS to be a unfilled niche here.
nocodb looks to be the best answer so far? Because it ties to a backend postgres database, it can be used along side bespoke applications. It still needs development though. I'm watching it like a hawk.
We had an Access database that managed grant funding for an entire public University and in many ways it worked a lot better than the SaaS app that recently replaced it. Need to collect a new set of data? No problem, give me 4 hours and it'll be ready to use :P.
I'd love to have something like Access but that worked very well as a platform-agnostic web app and could easily integrate with cloud infrastructure.
Make it easily deliverable over network, and you have killer product.
being able to have an excel grid and a chart view on the same page would probably suit your use case as well. being able to use python for the formulas is a nice touch too.
the free hosted version has almost all features available as far as i remember. there's also a docker version that's easy to get up and running and doesn't have any limitations
Would it? At the end of the day, someone else still has to proofread and QA the commands/formulas/program or it's just blind trust that the decision is being made on. Trust (or ignorance) that the creator knew what they were doing and developed it in an accurate way before action is taken on the decision being made. The interface really makes no difference, it's the human component and "process" for creation that needs to be fine tuned. Things like the London whale situation was a process failure where one person had too much power to execute trades without oversight, review, QA, testing, etc. [0] All things that are pretty standard in a software developer's day-to-day but the rest of the world has not realized or adjusted to the fact that they are now software developers too.
[0] Excel wasn't the problem with the London whale at all, they made a mathematical error "modelers divided by a sum instead of an average"
The Reinhart-Rogoff issue was technically an error in Excel, but also an error by the authors for not actually verifying the results before publishing. It didn't hurt that their particular biases were in line with the results.
The technical problem can be addressed with more warnings and safeguards, but they are meaningless if no one uses them.
> While using RR’s working spreadsheet, we identified coding errors, selective exclusion of available data, and unconventional weighting of summary statistics. [0]
I'm not a fan of tools giving warnings for these types of "coding errors". Although a warning I can think would be nice is where math just doesn't work as expected. The recent floating point discussion [1] seems appropriate as it's just not very intuitive and as a programmer you need a pretty deep level of understanding to know that the resulting math is likely not accurate. But, it also seems to effect nearly every programming language and is not a quirk of one specific thing.
I'd be interested to read more if you have info outline the actual error within Excel. If there is some 2+2=5 situation, I'd be interested to learn about that. I feel like every time someone says "Excel error", it's actually "human error". It would be like if every car accident was a vehicle malfunction but we all know it's most likely an operator issue.
[0] http://peri.umass.edu/fileadmin/pdf/working_papers/working_p...
Even in a corporate environment, I use spreadsheets to support big decisions every day, the format is as you say universal and easily interpretable, but many people never go that far. They trust that I did it correctly and take the "output" as truth. If I screw up big ($), it might cost me my job, but is it really my fault if nobody else even bothered to check my work?
It's not our fault, our brains are wired to find quick & dirty; simple not small is beautiful. But people also contemptuous of what they understand, and accept at face value what they don't. Michael Crichton, much disparaged in his later years for contentious political stances, had much of value to say in his Gell-Mann Amnesia Effects' "wet sidewalks cause rain" critique of how we parse information.
Still, I'm glad it's transparent in this case and thus exposable. How much more is hidden, guarded jealously even, as though methodology was trade secret instead of, you know, the underpinnings of proposed outcome?
More concrete definitions of where a formula should apply would be good, for example, leave the formula cell in one place and specify that one argument should come from this range of cells and the other from this range of cells, and the output should be mapped to this range.
It's a nice easy GUI wrapper for R and just works.
I stumbled across it a year ago and now use it daily.
(My background is that I teach Python and Data Science to large corps.)
[Study that was at the core for Europe's austerity and European debt crisis contained excel errors when fixed showed the inverse of original hypothesis.](https://www.nytimes.com/2013/04/19/opinion/krugman-the-excel...)
Programmable commands instead of a data grid would be huge improvement to quality but people use excel in many ways. Python is out of reach for most people. SQL would be an improvement as well. I assumed airtable or similar would replace excel over time. But the sunk cost for existing report and the sharablity seems to keep excel in control.