I dislike the premise. I mean, good data is wonderful.
But if institutions are expected to release clear data or nothing, almost always it is the later.
What is important, is to offer as much methodology and caveats as possible, even if in an informal way. Because there is a difference between "data covers 72% of companies registered in..." vs expecting that data is full and authoritative, whereas it is missing.
(Source: 10 years ago I worked a lot with official data. All data requires cleaning.)
But surely we should expect some basic sanity checks on published data? This isn't some petrol stations being placed in the middle of a field due to minor typos or bad rounding, or some petrol stations' prices being listed as all 1.00 £/l out of laziness, or even a case of all unknown locations being listed as 0°0'0" N, 0°0'0" E by default. What the author reports appear to be mistakes which should be rather trivially detectable on input.
The problem is stats can actually do more with all the data including obvious errors. If you start filtering out data where they miss entered lat log you might introduce a new bias.
Sure we should indeed expect that they do that. But look at enough data and you'll learn that those expectations are a path towards never-ending frustration. I've been there, spending >100 hours cleaning data... that never got published because I was too damn focused on the dozens of years of errors that many, many people created.
To be clear, I'm not saying that we should accept messy data. Just, reality is messy and it's naive to think we can catch and remove all of reality's messiness -- which includes the bureaucratic slop that led to the data being published in the first place.
I don't think these issues are close to the issues the article talks about. The author does not talk about data coverage, data collection methodologies or missing values or whatever, but data that is actually wrong, ie location coordinates, prices, numbers that make no sense. Including swapping latitude/longitude and wrong decimal points in numbers.
On the other hand, I agree that bad (but usually fixable) data is better than no data.
I have mixed feelings about this. On one hand, yeah stop publishing garbage data, but as a FOIA nerd... I'll take the data in any state it is. I'm not personally going to be able to clean the data before I receive it. Does that mean I shouldn't release the unsanitized (public) data knowing that it has garbage data within? Hell no. Instead, we should learn and cultivate techniques to work with shit data. Should I attempt to clean it? Sure. But it becomes a liability problem very, very quickly.
Do you remove those weird implausible outliers? They're probably garbage, but are they? Where do you draw the line?
If you've established the assumption that the data collection can go wrong, how do you know the points which look reasonable are actually accurate?
Working with data like this has unknown error bars, and I've had weird shit happen where I fixed the tracing pipeline, and the metrics people complained that they corrected for the errors downstream, and now due to those corrections, the whole thing looked out of shape.
This isn't possible to answer generally, but I'm sure you know that.
Look -- I've been in nonstop litigation for data through FOIA for the past ten years. During litigation I can definitely push back on messy data and I have, but if I were to do that on every little "obviously wrong" point, then my litigation will get thrown out for me being a twat of a litigant.
Again, I'd rather have the data and publish it with known gotchas.
Should I have told the Department of Finance to fuck off with their messy data? No -- even if I want to. Instead, we learn to work with its awfulness and advocate for cleaner data. Which is exactly what happened here -- once me and others started publishing stuff about tickets data and more journalists got involved, the data became cleaner over time.
Sorry I meant to say that usually it's not always possible to clean the data if the data is corrupt in the first place, because it was collected in a buggy manner. And having a few inexplicable outliers in datasets can often erode confidence in the rest.
Since this is not the data you collected, I understand you have to work with what you have, by the way very interesting post, and nice job!
One of those people can republish their cleaned and validated version and the 999 others can compare it to the original to decide whether they agree with the way it was cleaned or not.
Clean data is expensive--as in, it takes real human labor to obtain clean data.
One problem is that you can't just focus on outliers. Whatever pattern-matching you use to spot outliers will end up introducing a bias in the data. You need to check all the data, not just the data that "looks wrong". And that's expensive.
In clinical drug trials, we have the concept of SDV--Source Data Verification. Someone checks every data point against the official source record, usually a medical chart. We track the % of data points that have been verified. For important data (e.g., Adverse Events), the goal is to get SDV to 100%.
As you can imagine, this is expensive.
Will LLMs help to make this cheaper? I don't know, but if we can give this tedious, detail-oriented work to a machine, I would love it.
>Clean data is expensive--as in, it takes real human labor to obtain clean data.
Yes, data can contain subtle errors that are expensive and difficult to find. But the 2nd error in the article was so obvious that a bright 10 year would probably have spotted it.
But sometimes the "provenance" of the data is important. I want to know whether I'm getting data straight from some source (even with errors) rather than having some intermediary make fixes that I don't know about.
For example, in the case where maybe they flipped the latitude and longitude, I don't want them to just automatically "fix" the data (especially not without disclosing that).
What they need to do is verify the outliers with the original gas station and fix the data from the source. But that's much more expensive.
Exactly. This is a big problem with "open data". A lot goes into cleaning it up to make it publishable, which often includes removing data so that the public "doesn't get confused". Now I have to spend months and months fighting FOIA fights to get the original raw, messy data because someone , somewhere had opinions on what "clean data" is. I'll pass -- give me the raw, messy data.
I do not disagree with that, but I am not sure what "raw data" means in some cases like the ones the article talks about. The 1.700.000 is no less or more raw than 1.700,000. Most probably somebody messed up some decimals somewhere, or somebody imported a csv in excel and it misinterpreted the numbers due to different settings. Similar to swapped longitude/latitude. That sounds different to me than, let's say, noisy temperature data from sensors. Rather, it seems more like issues that arose at the point of merging datasets together, which is already far from the data being raw.
The issue imo is that a person closer to the point the data was collected or merged is probably better equipped with understanding of what may be wrong with it, than a random person looking into that dataset. So I do not think it is unreasonable to have people in organisations take a second look into the datasets they publish.
When I say "raw", what I'm referring to is the preservation of the data's chain of custody. If I'm looking at the data with an intent to sue the respective government agency, then I have strong legal reasons to make sure that the data isn't modified. If I start from open data for example, the gov agency will have their data person sign an affidavit making this very clear and I will lose my case basically immediately.
The issue imo is that a person closer to the point the data was collected or merged is probably better equipped with understanding of what may be wrong with it
You'd think so, but just like most other systems, systems are often inherited or not thought out, so the understanding is external and we can't assume expertise within.
This can skew the dataset and lead to misinterpreted results, if which rows are wrong is not completely random.
Eg if all data from a specific location (or year etc) comes wrong, then this kind of cleaning would just completely exclude this location, which depending on the context may or may not be a problem. Or if values come wrong above a specific threshold. Or any other way that the errors are not in some way randomly distributed.
Removing data is never a neutral choice, and it should always be taken into consideration (which data is removed).
>Removing data is never a neutral choice, and it should always be taken into consideration (which data is removed).
Absolutely. If you have obviously wrong data your choices are generally:
1. Leave the bad data in.
2. Leave the bad data in and flag it as suspect.
3. Omit the dad data.
4. Correct the bad data.
Which is the best choice depends on context and requires judgement. But I find it hard to imagine any situation where option 1 is the right choice.
Obviously the best solution is to do basic validation as the data is entered, so that people can't add a location in the Indian ocean to a UK dataset. It seems rather negligent that they didn't do this.
Like I said in a different post, there are legal reasons for why you would want the original data. Deleting the data from the dataset is negligent.
If you want something to blame, blame the system that allowed the data to be bad in the first place. You're pointing your finger at the wrong people and it's unreasonable of you to call them negligent.
"obviously wrong" is a never ending rabbit hole and you'll never, ever be satisfied because there will always be something "obviously wrong" with the data.
Messy data is a signal. You're wrong to omit signal.
Data and metrics is 90% what upper management sees of your project. You might not care about it, and treat it as an afterthought, but it's almost the most important thing about it organizationally.
People who don't heed this advice get to discover it for themselves (I sure did)
IF you can't make the data convincing, you'll lose all trust, and nobody will do business with you.
I have also learned that rarely does anyone care if it’s any good, or means anything. This is generally true, but it’s especially true if you are going with the prevailing winds of whatever management fads are going on.
Like, right now, you can definitely get away with inflating the efficacy of “AI” any way you can, in almost any company. Nobody with any authority will call you on it.
Look at what management’s talking about and any pro-that numbers you come up with can be total gibberish, nobody minds. “Oh man, collecting good numbers for this and getting a baseline etc etc is practically impossible” ok so don’t and just use bad numbers that align with what management wants to do anyway. You’ll do great.
Not sure, but I have worked a lot on stuff where the metrics were often very easily convertible to business decisions, like expenses or income. Things like how much do we need to pay for infrastructure, how much money each customer/products brings in and how etc.
If the company were an airplane essentially upper management were flying it by instrument. It would've been a scandal if the metrics had serious issues.
Some of the metrics less directly tied to business stuff were a bit more 'creative' - as in I could justify why I did them that way, but still not 100% solid.
Stuff like optimizing data pipelines, where data scientist experiments which tended to take 1hr, now only took 10 mins.
I could say that data people were 6x as productive, but it's just as well possible they were just more careless with what they ran, but whatever, a white lie.
However saying that stuff takes 1/6th the time, when in fact it doesn't, is an absolute no go. Neither is not knowing why is there a run that took 500 hours or 5 seconds, both of which should be impossible.
Doing that stuff destroys the confidence in the rest of the data.
It is not just embarrassing, it can potentially kill your demo, project or even product as user will first look at data and then the tech behind it. If the data is wrong, it means the tech does not work. I never took data seriously during my demos in the first 10 years of my career and no wonder the audience rejected most of my work though it was backed by solid platforms.
That it's it's better to publish the garbage data than to not publish it though. I would worry about complaining too much lest they just decide to stop publishing it because it creates bad PR.
As long as the garbage data is authentic and the method used to produce it is adequately detailed, I agree with you that: "it's better to publish the garbage data than to not publish it"
But fake data or garbage data without the method, is better left unpublished !
This article assumes that there is a person with dedicated time to validate the data. Imagine you want this data and ask for it, but the government says, “sorry, we have this data, but we read an article that said we can only publish it if we spend a lot of time validating it. This data changes frequently and we don’t have a chunk of a full-time data analyst’s salary to spend on it, so we just aren’t going to publish anything. We’d rather put out nothing than embarrass ourselves, so you can’t even try to validate it yourself.”
In fact, the government agencies will argue that they have zero legal obligation to clean the data, let alone figure anything about the data, and that they're just giving you the data as-is. This happened to me on a FOIA call where I was trying to get data from the county state's attorney. They insisted they could only run a specific report and that they had no obligation to run any query, meaning I can't even get access to the data I need.
A couple of days after the UK Fuel Finder service launch last month, I wrote a hobby site using its API to get the cheapest local fuel prices: https://fuelseeker.net. I too discovered prices which had obviously been entered in pounds rather than pennies, or even missing altogether some cases. You would think that they could have done a bit more basic data cleansing on the server to catch that type of thing.
But, hey, we’re all wise after the event. To their credit though, they do seem to be actively reacting to feedback. I also contacted them about the bad data issue, and they are now adding user warnings about bad price values at the point of data entry (according to https://www.developer.fuel-finder.service.gov.uk/release-not...).
There an obvious incentive for petrol stations to 'accidentally' put too low price, so they can get top of the table on services like yours. So they probably need to do more than add warnings.
I was looking at that RAC chart this morning. Given it's Sunday, and I was reading before my morning coffee, I'm not ashamed to say it took me a good few seconds of zooming in and out to realise they'd used a decimal point where a comma should have been.
Easy type to make, but seriously, does no one even take a cursory look at the charts when publishing articles like this? The chart looks _obviously_ wrong, so imagine how many are only slightly wrong and are missed.
The fuel prices one could surely be solved with a tiny bit of validation; are the coordinates even within a reasonable range? Fortunately, in the UK, it's really easy to tell which is latitude and which is longitude due to one of them being within a digit or two of zero on either side.
What about most of Show HN's projects nowadays? Sometimes the docs straight up lie, and it takes 5 minutes to figure that out. Should they also be ashamed?
What about people who don't know how their own code works? Despite it working flawlessly? I'm asking because I don't really know.
How is it fair to compare a Show HN project with official government datasets? People depend on government datasets, multi-billion dollar businesses are built on top of them. A show HN project is typically someone building it in a weekend. They’re not even remotely in the same league.
Sure it is expensive to check every number, but at least some of it can be automated and flagged for human review, no? Switching lat/long numbers. For example
If they publish a lie they should be ashamed, even if their lie is orders of magnitude less impactful.
And if someone publishes a flawless code but have no idea how it works, its not their code, quite clearly, AMD they should be ashamed if they lie it is.
I dislike the premise. I mean, good data is wonderful.
But if institutions are expected to release clear data or nothing, almost always it is the later.
What is important, is to offer as much methodology and caveats as possible, even if in an informal way. Because there is a difference between "data covers 72% of companies registered in..." vs expecting that data is full and authoritative, whereas it is missing.
(Source: 10 years ago I worked a lot with official data. All data requires cleaning.)
But surely we should expect some basic sanity checks on published data? This isn't some petrol stations being placed in the middle of a field due to minor typos or bad rounding, or some petrol stations' prices being listed as all 1.00 £/l out of laziness, or even a case of all unknown locations being listed as 0°0'0" N, 0°0'0" E by default. What the author reports appear to be mistakes which should be rather trivially detectable on input.
The problem is stats can actually do more with all the data including obvious errors. If you start filtering out data where they miss entered lat log you might introduce a new bias.
Sure we should indeed expect that they do that. But look at enough data and you'll learn that those expectations are a path towards never-ending frustration. I've been there, spending >100 hours cleaning data... that never got published because I was too damn focused on the dozens of years of errors that many, many people created.
To be clear, I'm not saying that we should accept messy data. Just, reality is messy and it's naive to think we can catch and remove all of reality's messiness -- which includes the bureaucratic slop that led to the data being published in the first place.
I don't think these issues are close to the issues the article talks about. The author does not talk about data coverage, data collection methodologies or missing values or whatever, but data that is actually wrong, ie location coordinates, prices, numbers that make no sense. Including swapping latitude/longitude and wrong decimal points in numbers.
On the other hand, I agree that bad (but usually fixable) data is better than no data.
Yep, expect in real data actually confusing columns, NaNs casted to values like 1673, duplicates, etc, etc.
I prefer to get data with swapped lat/lng (a trivial fix), or prices said in dollars but being in cents, to no data.
Agreed, pretty much all data is flawed. I still want my hands on it.
I read the premises as "1. at least look at it 2. Have a way to fix it"
Those seem reasonable asks.
Edit to add: the tragedy of the school in Minab is an example of how bad things can go--and it just hints at how much worse bad data can bem
I have mixed feelings about this. On one hand, yeah stop publishing garbage data, but as a FOIA nerd... I'll take the data in any state it is. I'm not personally going to be able to clean the data before I receive it. Does that mean I shouldn't release the unsanitized (public) data knowing that it has garbage data within? Hell no. Instead, we should learn and cultivate techniques to work with shit data. Should I attempt to clean it? Sure. But it becomes a liability problem very, very quickly.
What does it mean to clean the data?
Do you remove those weird implausible outliers? They're probably garbage, but are they? Where do you draw the line?
If you've established the assumption that the data collection can go wrong, how do you know the points which look reasonable are actually accurate?
Working with data like this has unknown error bars, and I've had weird shit happen where I fixed the tracing pipeline, and the metrics people complained that they corrected for the errors downstream, and now due to those corrections, the whole thing looked out of shape.
"What does it mean to clean the data?"
This isn't possible to answer generally, but I'm sure you know that.
Look -- I've been in nonstop litigation for data through FOIA for the past ten years. During litigation I can definitely push back on messy data and I have, but if I were to do that on every little "obviously wrong" point, then my litigation will get thrown out for me being a twat of a litigant.
Again, I'd rather have the data and publish it with known gotchas.
Here's an example: https://mchap.io/using-foia-data-and-unix-to-halve-major-sou...
Should I have told the Department of Finance to fuck off with their messy data? No -- even if I want to. Instead, we learn to work with its awfulness and advocate for cleaner data. Which is exactly what happened here -- once me and others started publishing stuff about tickets data and more journalists got involved, the data became cleaner over time.
Sorry I meant to say that usually it's not always possible to clean the data if the data is corrupt in the first place, because it was collected in a buggy manner. And having a few inexplicable outliers in datasets can often erode confidence in the rest.
Since this is not the data you collected, I understand you have to work with what you have, by the way very interesting post, and nice job!
So you expect the 1000s of people trying to use the fuel price data to each individually clean and validate it, rather than the supplier doing it?
One of those people can republish their cleaned and validated version and the 999 others can compare it to the original to decide whether they agree with the way it was cleaned or not.
What...?
Clean data is expensive--as in, it takes real human labor to obtain clean data.
One problem is that you can't just focus on outliers. Whatever pattern-matching you use to spot outliers will end up introducing a bias in the data. You need to check all the data, not just the data that "looks wrong". And that's expensive.
In clinical drug trials, we have the concept of SDV--Source Data Verification. Someone checks every data point against the official source record, usually a medical chart. We track the % of data points that have been verified. For important data (e.g., Adverse Events), the goal is to get SDV to 100%.
As you can imagine, this is expensive.
Will LLMs help to make this cheaper? I don't know, but if we can give this tedious, detail-oriented work to a machine, I would love it.
>Clean data is expensive--as in, it takes real human labor to obtain clean data.
Yes, data can contain subtle errors that are expensive and difficult to find. But the 2nd error in the article was so obvious that a bright 10 year would probably have spotted it.
Agreed--and maybe they should have fixed it.
But sometimes the "provenance" of the data is important. I want to know whether I'm getting data straight from some source (even with errors) rather than having some intermediary make fixes that I don't know about.
For example, in the case where maybe they flipped the latitude and longitude, I don't want them to just automatically "fix" the data (especially not without disclosing that).
What they need to do is verify the outliers with the original gas station and fix the data from the source. But that's much more expensive.
Exactly. This is a big problem with "open data". A lot goes into cleaning it up to make it publishable, which often includes removing data so that the public "doesn't get confused". Now I have to spend months and months fighting FOIA fights to get the original raw, messy data because someone , somewhere had opinions on what "clean data" is. I'll pass -- give me the raw, messy data.
I do not disagree with that, but I am not sure what "raw data" means in some cases like the ones the article talks about. The 1.700.000 is no less or more raw than 1.700,000. Most probably somebody messed up some decimals somewhere, or somebody imported a csv in excel and it misinterpreted the numbers due to different settings. Similar to swapped longitude/latitude. That sounds different to me than, let's say, noisy temperature data from sensors. Rather, it seems more like issues that arose at the point of merging datasets together, which is already far from the data being raw.
The issue imo is that a person closer to the point the data was collected or merged is probably better equipped with understanding of what may be wrong with it, than a random person looking into that dataset. So I do not think it is unreasonable to have people in organisations take a second look into the datasets they publish.
When I say "raw", what I'm referring to is the preservation of the data's chain of custody. If I'm looking at the data with an intent to sue the respective government agency, then I have strong legal reasons to make sure that the data isn't modified. If I start from open data for example, the gov agency will have their data person sign an affidavit making this very clear and I will lose my case basically immediately.
You'd think so, but just like most other systems, systems are often inherited or not thought out, so the understanding is external and we can't assume expertise within.Or just omit the rows that are obviously wrong (and document the fact).
> omit the rows that are obviously wrong
This can skew the dataset and lead to misinterpreted results, if which rows are wrong is not completely random.
Eg if all data from a specific location (or year etc) comes wrong, then this kind of cleaning would just completely exclude this location, which depending on the context may or may not be a problem. Or if values come wrong above a specific threshold. Or any other way that the errors are not in some way randomly distributed.
Removing data is never a neutral choice, and it should always be taken into consideration (which data is removed).
>Removing data is never a neutral choice, and it should always be taken into consideration (which data is removed).
Absolutely. If you have obviously wrong data your choices are generally:
1. Leave the bad data in.
2. Leave the bad data in and flag it as suspect.
3. Omit the dad data.
4. Correct the bad data.
Which is the best choice depends on context and requires judgement. But I find it hard to imagine any situation where option 1 is the right choice.
Obviously the best solution is to do basic validation as the data is entered, so that people can't add a location in the Indian ocean to a UK dataset. It seems rather negligent that they didn't do this.
Like I said in a different post, there are legal reasons for why you would want the original data. Deleting the data from the dataset is negligent.
If you want something to blame, blame the system that allowed the data to be bad in the first place. You're pointing your finger at the wrong people and it's unreasonable of you to call them negligent.
"obviously wrong" is a never ending rabbit hole and you'll never, ever be satisfied because there will always be something "obviously wrong" with the data.
Messy data is a signal. You're wrong to omit signal.
100%. There is even signal in the pattern of errors. If you remove some errors but not others, you lose signal.
Deleting the row loses some information, such as the existence of that gas station.
A better solution is to add a field to indicate that "the row looks funny to the person who published the data". Which, I guess is useful to someone?
But deleting data or changing data is effectively corrupting source data, and now I can't trust it.
Why would you give this sort of work to a machine that can't be responsibly used without checking its output anyway?
It's not obvious to me that LLMs can't be made reliable.
Data and metrics is 90% what upper management sees of your project. You might not care about it, and treat it as an afterthought, but it's almost the most important thing about it organizationally.
People who don't heed this advice get to discover it for themselves (I sure did)
IF you can't make the data convincing, you'll lose all trust, and nobody will do business with you.
I have learned that you must have data.
I have also learned that rarely does anyone care if it’s any good, or means anything. This is generally true, but it’s especially true if you are going with the prevailing winds of whatever management fads are going on.
Like, right now, you can definitely get away with inflating the efficacy of “AI” any way you can, in almost any company. Nobody with any authority will call you on it.
Look at what management’s talking about and any pro-that numbers you come up with can be total gibberish, nobody minds. “Oh man, collecting good numbers for this and getting a baseline etc etc is practically impossible” ok so don’t and just use bad numbers that align with what management wants to do anyway. You’ll do great.
Not sure, but I have worked a lot on stuff where the metrics were often very easily convertible to business decisions, like expenses or income. Things like how much do we need to pay for infrastructure, how much money each customer/products brings in and how etc.
If the company were an airplane essentially upper management were flying it by instrument. It would've been a scandal if the metrics had serious issues.
Some of the metrics less directly tied to business stuff were a bit more 'creative' - as in I could justify why I did them that way, but still not 100% solid.
Stuff like optimizing data pipelines, where data scientist experiments which tended to take 1hr, now only took 10 mins.
I could say that data people were 6x as productive, but it's just as well possible they were just more careless with what they ran, but whatever, a white lie.
However saying that stuff takes 1/6th the time, when in fact it doesn't, is an absolute no go. Neither is not knowing why is there a run that took 500 hours or 5 seconds, both of which should be impossible.
Doing that stuff destroys the confidence in the rest of the data.
It is not just embarrassing, it can potentially kill your demo, project or even product as user will first look at data and then the tech behind it. If the data is wrong, it means the tech does not work. I never took data seriously during my demos in the first 10 years of my career and no wonder the audience rejected most of my work though it was backed by solid platforms.
That it's it's better to publish the garbage data than to not publish it though. I would worry about complaining too much lest they just decide to stop publishing it because it creates bad PR.
As long as the garbage data is authentic and the method used to produce it is adequately detailed, I agree with you that: "it's better to publish the garbage data than to not publish it"
But fake data or garbage data without the method, is better left unpublished !
Hard disagree on that. They just need a basic smell test before they put it out.
Agree. Maybe just add a Disclaimer.md file.
This article assumes that there is a person with dedicated time to validate the data. Imagine you want this data and ask for it, but the government says, “sorry, we have this data, but we read an article that said we can only publish it if we spend a lot of time validating it. This data changes frequently and we don’t have a chunk of a full-time data analyst’s salary to spend on it, so we just aren’t going to publish anything. We’d rather put out nothing than embarrass ourselves, so you can’t even try to validate it yourself.”
In fact, the government agencies will argue that they have zero legal obligation to clean the data, let alone figure anything about the data, and that they're just giving you the data as-is. This happened to me on a FOIA call where I was trying to get data from the county state's attorney. They insisted they could only run a specific report and that they had no obligation to run any query, meaning I can't even get access to the data I need.
Clean vs not clean data is the wrong fight.
>we don’t have a chunk of a full-time data analyst’s salary to spend on it
I found the errors in a few minutes with a $198 tool.
Concluding passage:
> Authors should have their work proof read
Agreed.
Opening passage:
> A quick plot of the latitude and longitude shows some clear outliners
"outliners"
Ouch!
OP here. Ouch indeed. I did actually get it proofread. But that was missed. I can't fire my proofreader, as we are married. ;0)
Now fixed.
Not fixed at this hour
You might need to do a refresh.
A couple of days after the UK Fuel Finder service launch last month, I wrote a hobby site using its API to get the cheapest local fuel prices: https://fuelseeker.net. I too discovered prices which had obviously been entered in pounds rather than pennies, or even missing altogether some cases. You would think that they could have done a bit more basic data cleansing on the server to catch that type of thing.
But, hey, we’re all wise after the event. To their credit though, they do seem to be actively reacting to feedback. I also contacted them about the bad data issue, and they are now adding user warnings about bad price values at the point of data entry (according to https://www.developer.fuel-finder.service.gov.uk/release-not...).
There an obvious incentive for petrol stations to 'accidentally' put too low price, so they can get top of the table on services like yours. So they probably need to do more than add warnings.
Why did the title of this post get moderated from:
"Stop Publishing Garbage Data, It’s Embarrassing"
To the rather lamer:
"Twice this week, I have come across embarassingly bad data"
?
I saw the RAC one this morning, though I was miss reading the graph, as why would the RAC publish such an obvious mistake.
I have written my own Home Assistant custom component for the UK fuel finder data, and yes, the data really is that bad.
I was looking at that RAC chart this morning. Given it's Sunday, and I was reading before my morning coffee, I'm not ashamed to say it took me a good few seconds of zooming in and out to realise they'd used a decimal point where a comma should have been.
Easy type to make, but seriously, does no one even take a cursory look at the charts when publishing articles like this? The chart looks _obviously_ wrong, so imagine how many are only slightly wrong and are missed.
The fuel prices one could surely be solved with a tiny bit of validation; are the coordinates even within a reasonable range? Fortunately, in the UK, it's really easy to tell which is latitude and which is longitude due to one of them being within a digit or two of zero on either side.
Did a bot write this title?
If you are putting out data without doing even the most basic validation, then you should be ashamed.
What about most of Show HN's projects nowadays? Sometimes the docs straight up lie, and it takes 5 minutes to figure that out. Should they also be ashamed?
What about people who don't know how their own code works? Despite it working flawlessly? I'm asking because I don't really know.
> Sometimes the docs straight up lie, and it takes 5 minutes to figure that out. Should they also be ashamed?
Yes.
How is it fair to compare a Show HN project with official government datasets? People depend on government datasets, multi-billion dollar businesses are built on top of them. A show HN project is typically someone building it in a weekend. They’re not even remotely in the same league.
Sure it is expensive to check every number, but at least some of it can be automated and flagged for human review, no? Switching lat/long numbers. For example
This has become a spam site for AI shovelware projects that are nearly always posted by accounts with no activity here outside of self promotion.
If they publish a lie they should be ashamed, even if their lie is orders of magnitude less impactful.
And if someone publishes a flawless code but have no idea how it works, its not their code, quite clearly, AMD they should be ashamed if they lie it is.
It's just, like, my opinion, but I like it :)
>Sometimes the docs straight up lie, and it takes 5 minutes to figure that out. Should they also be ashamed?
Yes. Lying is bad, even if some people are trying hard to normalise it.
>What about people who don't know how their own code works? Despite it working flawlessly?
I think that is fine, as long as you aren't making untrue claims.