6 ms·

I've made anecdotal observations similiar to this over the last 10 years. I work in AgTech. A big push for a while here has been "more and more more data". Sens

I've made anecdotal observations similiar to this over the last 10 years. I work in AgTech. A big push for a while here has been "more and more more data". Sensor-the-heck out of your farm, and We'll Tell You Things(tm). Most of what we as an industry are able to tell growers is stuff they already know or suspect. There is the occasional suprise or "Aha" moment where some correlation becomes apparent, but the thing about these is that once they've been observed and understood, the value of ongoing observation drops rapidly. A great example of this is soil moisture sensors. Every farmer that puts these in goes geek-crazy for the first year or so. It's so cool to see charts that illustrate the effect of their irrigation efforts. They may even learn a little and make some adjustments. But once those adjustments and knowledge have been applied, it's not like they really need this ongoing telementry as much anymore. They'll check periodically (maybe) to continue to validate their new assumptions, but 3 years later, the probes are often forgotten and left to rot, or reduced in count.

barathr 4y ago
Classic paper on soil moisture sensors (from 2010!) -- the title says it all: "Mate, we don't need a chip to tell us the soil's dry" https://doi.org/10.1145/1858171.1858211 https://doi.org/10.1145/1858171.1858211
chudi 4y ago
Most of the time this story is true, but think this way, the person that was using the system was an expert on the subject. If you can replace the expert with a person just looking at a graph from time to time to know if you have to irrigate the soils it's a different thing. Most of the data or ML tools show us something that the client as an expert already knows, but the true power of this tools is to give them to a non expert user and have roughly the same level of proficiency
ladyattis 4y ago
I think there's a problem at the heart of the matter, specifically the idea that the act of measurement is in itself powerful when in point of fact that this isn't universally the case. As the old adage goes: "garbage in, garbage out." Even more troubling, there is a physical limit to our ability to model what we measure. Take the retina, it has around a million light receptors and even if you assumed they only have two valid states then you're left with around 10^300,000 bits of information to process, so good luck with that. Same thing applies to whatever firms are measuring and what they think is conveying relevant information as they'll have similarly exponential increases if they don't filter out the vast majority of irrelevant data points and states.
- gmfawcett 4y ago
  > it has around a million light receptors and even if you assumed they only have two valid states then you're left with around 10^300,000 bits of information to process... That would only be a million bits (1 Mb). You're counting potential states, not bits.
gffrd 4y ago
I've observed the same in manufacturing … and fitness trackers a la FitBit. There's initial value from training yourself on what something looks/feels like … but diminishing returns after that. Whether there is more value to be found doesn't seem to matter. Factories would sensor up, go nuts with data, find one or two major insight, tire of data, and then just continue operating how they were before … but with a few new operational tools in their quiver. Same is true of fitness trackers: you excitedly get one, learn how much you really are sitting(!), adjust your patterns, time passes … then one day you realize you haven't put it on for a week. It stays in the drawer. Not unless they're threatened with ruin will people make changes to the standard way of doing things. This is actually … not bad! Continuity is important, and this is kind of a subconscious gating function to prevent deviation from a proven way of working. So, the change has to be so compelling or so pressing that they're forced to. Not a bad thing. While we think things change overnight in this world, they generally take awhile … stay patient … it's worth it.
- carabiner 4y ago
  I used to track sooo much health and fitness data... Then I realized it mostly wasn't actionable, or at least, I wasn't altering my decisions based on it. The answer was always, "more training." So I stopped.
  - jossclimb 4y ago
    I think they are only useful to runners, where weekly distance, speed and heart rate are massively useful. These are all very accurate things that can be captured, unlike weight resistance (swinging a kettlebell, dead lifting).
- pradn 4y ago
  I went on a diet a few years ago. I obsessively recorded every food I ate in MyFitnessPal. To this day, I know roughly how many calories pretty much everything I eat is. So, I've learned from the process and don't need the process as much any more. (I'm kidding about that - it's easy to underestimate how much you eat, and an extra 200 cal a day adds up over the years.)
azubinski 4y ago
Oh, those soil moisture sensors, they are so fascinating. I spent a number of exciting year developing a high frequency soil impedance scanner and finally understood why I was doing it. To confirm the obvious :)
e12e 4y ago
But isn't this the essence of industrialization and automation? Messure, adjust process; repeat until feedback loop is stable - document and keep doing the thing that works, over and over? If you want Toyota style continuous improvement you would need to improve in new areas of the process / new metrics, most of the time?
esel2k 4y ago
Very interesting. Left AgTech last year but had similar experiences, even worse where often the single most prominent use-case was to follow some painful necessary documentation of ag inputs (chemical, seeds, fertiliser) to get subsidies. Real inputs from data? Nah!
ff317 4y ago
I tend to think the problem is the "random digging for correlations" part. Having tons of data is a Good Thing, so long as you can afford the marginal cost of gathering and managing all that data so that it's ready at hand when you need it later. It's how you use the data that makes all the difference. If you're facing an issue you don't understand at all, don't go digging for random correlations in your mountain of data to find an explanation. Think like a scientist: you need a valid hypothesis first! Once you have a hypothesis about what your issue might plausibly be, then you make a prediction: "If I'm right, I suspect our Foobar data will show very low values of Xyzzy around 3AM every weekday night". Only then do you go look at that specific data to confirm or refute the hypothesis. If you don't get a confirmation, you need to go back to hypothesizing and predicting before you look again. You can't prove causation by merely correlating data.
- counters 4y ago
  > It's how you use the data that makes all the difference. If you're facing an issue you don't understand at all, don't go digging for random correlations in your mountain of data to find an explanation. Absolutely. But in my experience, there's this massive trend across the tech world that flat out rejects the value of domain/subject matter expertise. Instead, all you need is an engineer who can throw some ML at the uncurated mountain of data your organization has collected. Little to no value is placed on the resources that can frame an actionable hypothesis, even though the entire value proposition arises from this exercise! Maybe I'm just jaded. I end up wasting a lot of time trying to re-direct data scientists and engineers down more appropriate pathways than if the problem they're solving was just brought to my attention earlier. Sorry, I understand you spent two weeks shoe-horning dataset X into our analysis system for your work, but it's invalid for the question you're asking - use dataset Y instead, and you'll have an answer in an hour or two.
  - naijaboiler 4y ago
    No youre not. The next time i have to listen to some idiotic take that domain knowledge doesn't matter, i might shoot myself
  - 4y ago
0xdeadbeefbabe 4y ago
> goes geek-crazy for the first year or so The problem is that they don't stay geek-crazy?
PeterisP 4y ago
Fine-grained measurement is useful when you have options for fine-grained action. You don't need a chip to tell you that the soil is dry, but if you can use that chip to regulate drip irrigation that can apply substantially different flow to different plants, then you can get a not-too-much, not-too-little watering even if you have a big variation in conditions. You don't need a big analysis to acknowledge that everybody knows that a particular competitor has lower or higher prices and adjust your pricing; but doing that continuously on a per-product basis does require data and analysis.
- ryguyrg 4y ago
  Agreed. But how many executives will agree to take these fine-grained actions to achieve value from the data? How many data teams are able to build up a strong-enough argument to convince them? I've worked on many product-led-growth initiatives in the software industry. The software industry is probably the biggest 'believer in data' there is -- many scientific-forward minds who understand the value. However, even in the software industry, it's really hard to convince folks that if you make 5 improvements that net 1% conversion gain each, you can dramatically improve revenue.
calvinmorrison 4y ago
On the flip side, there's some great action coming from data insights. Look at Strella Biotech - they're putting sensors in sealed warehouses to detect spoilage for certain vegetables and fruits. That's something that can have great returns with just a few IoT devices and a novel sensor.
jschveibinz 4y ago
This analysis reminds me of the big interest in the use of hyperspectral imaging for agriculture. The idea was the greater spectral resolution (greater than Landsat) would result in more interesting information. Agriculture was one of the applications. But, once you did find the interesting stuff, you no longer needed a hyperspectral sensor. You could just look at one spot with a much lower cost sensor. So hyperspectral, like big data, is useful up front. But in the end, much simpler tools and algorithms will solve the problem on a continuing basis.
didip 4y ago
I've been telling folks, storing everything all the time is wasteful, a better alternative is: 1. Keep the raw full data for short period of time, at most 1 month. 2. Downsample what you need for longer period of time (5-10% of the full data). 3. Aggregate your metrics on a yearly basis to save money and compute costs.
- tuatoru 4y ago
  Yeah, there is perhaps a data usefulness duck curve. First data useful for specific immediate problems, not a lot of use for a while after that, then 15 or 20 years later, the big picture trends start to provide value for big decision making. Not many orgs keep their data that long, though. Or even think about the future that far.
pron 4y ago
Interesting. Sounds as if what's really needed isn't so much collecting and analysing lots of data, but an alarm that's triggered when observations deviate from a set of assumptions. Observations that confirm some definition of "normalcy" -- as most observations would -- can be discarded.
fijiaarone 4y ago
Or they could put on their work boots and go walk around the field and kick a few dirt clods, or science forbid! put a hand in the soil to check the moisture content.

barathr 4y ago

Classic paper on soil moisture sensors (from 2010!) -- the title says it all: "Mate, we don't need a chip to tell us the soil's dry" https://doi.org/10.1145/1858171.1858211 https://doi.org/10.1145/1858171.1858211

chudi 4y ago

Most of the time this story is true, but think this way, the person that was using the system was an expert on the subject. If you can replace the expert with a person just looking at a graph from time to time to know if you have to irrigate the soils it's a different thing. Most of the data or ML tools show us something that the client as an expert already knows, but the true power of this tools is to give them to a non expert user and have roughly the same level of proficiency

ladyattis 4y ago

I think there's a problem at the heart of the matter, specifically the idea that the act of measurement is in itself powerful when in point of fact that this isn't universally the case. As the old adage goes: "garbage in, garbage out." Even more troubling, there is a physical limit to our ability to model what we measure. Take the retina, it has around a million light receptors and even if you assumed they only have two valid states then you're left with around 10^300,000 bits of information to process, so good luck with that. Same thing applies to whatever firms are measuring and what they think is conveying relevant information as they'll have similarly exponential increases if they don't filter out the vast majority of irrelevant data points and states.

gmfawcett 4y ago
> it has around a million light receptors and even if you assumed they only have two valid states then you're left with around 10^300,000 bits of information to process... That would only be a million bits (1 Mb). You're counting potential states, not bits.

gffrd 4y ago

I've observed the same in manufacturing … and fitness trackers a la FitBit. There's initial value from training yourself on what something looks/feels like … but diminishing returns after that. Whether there is more value to be found doesn't seem to matter. Factories would sensor up, go nuts with data, find one or two major insight, tire of data, and then just continue operating how they were before … but with a few new operational tools in their quiver. Same is true of fitness trackers: you excitedly get one, learn how much you really are sitting(!), adjust your patterns, time passes … then one day you realize you haven't put it on for a week. It stays in the drawer. Not unless they're threatened with ruin will people make changes to the standard way of doing things. This is actually … not bad! Continuity is important, and this is kind of a subconscious gating function to prevent deviation from a proven way of working. So, the change has to be so compelling or so pressing that they're forced to. Not a bad thing. While we think things change overnight in this world, they generally take awhile … stay patient … it's worth it.

carabiner 4y ago
I used to track sooo much health and fitness data... Then I realized it mostly wasn't actionable, or at least, I wasn't altering my decisions based on it. The answer was always, "more training." So I stopped.
- jossclimb 4y ago
  I think they are only useful to runners, where weekly distance, speed and heart rate are massively useful. These are all very accurate things that can be captured, unlike weight resistance (swinging a kettlebell, dead lifting).
pradn 4y ago
I went on a diet a few years ago. I obsessively recorded every food I ate in MyFitnessPal. To this day, I know roughly how many calories pretty much everything I eat is. So, I've learned from the process and don't need the process as much any more. (I'm kidding about that - it's easy to underestimate how much you eat, and an extra 200 cal a day adds up over the years.)

azubinski 4y ago

Oh, those soil moisture sensors, they are so fascinating. I spent a number of exciting year developing a high frequency soil impedance scanner and finally understood why I was doing it. To confirm the obvious :)

e12e 4y ago

But isn't this the essence of industrialization and automation? Messure, adjust process; repeat until feedback loop is stable - document and keep doing the thing that works, over and over? If you want Toyota style continuous improvement you would need to improve in new areas of the process / new metrics, most of the time?

esel2k 4y ago

Very interesting. Left AgTech last year but had similar experiences, even worse where often the single most prominent use-case was to follow some painful necessary documentation of ag inputs (chemical, seeds, fertiliser) to get subsidies. Real inputs from data? Nah!

ff317 4y ago

I tend to think the problem is the "random digging for correlations" part. Having tons of data is a Good Thing, so long as you can afford the marginal cost of gathering and managing all that data so that it's ready at hand when you need it later. It's how you use the data that makes all the difference. If you're facing an issue you don't understand at all, don't go digging for random correlations in your mountain of data to find an explanation. Think like a scientist: you need a valid hypothesis first! Once you have a hypothesis about what your issue might plausibly be, then you make a prediction: "If I'm right, I suspect our Foobar data will show very low values of Xyzzy around 3AM every weekday night". Only then do you go look at that specific data to confirm or refute the hypothesis. If you don't get a confirmation, you need to go back to hypothesizing and predicting before you look again. You can't prove causation by merely correlating data.

counters 4y ago
> It's how you use the data that makes all the difference. If you're facing an issue you don't understand at all, don't go digging for random correlations in your mountain of data to find an explanation. Absolutely. But in my experience, there's this massive trend across the tech world that flat out rejects the value of domain/subject matter expertise. Instead, all you need is an engineer who can throw some ML at the uncurated mountain of data your organization has collected. Little to no value is placed on the resources that can frame an actionable hypothesis, even though the entire value proposition arises from this exercise! Maybe I'm just jaded. I end up wasting a lot of time trying to re-direct data scientists and engineers down more appropriate pathways than if the problem they're solving was just brought to my attention earlier. Sorry, I understand you spent two weeks shoe-horning dataset X into our analysis system for your work, but it's invalid for the question you're asking - use dataset Y instead, and you'll have an answer in an hour or two.
- naijaboiler 4y ago
  No youre not. The next time i have to listen to some idiotic take that domain knowledge doesn't matter, i might shoot myself
- 4y ago

0xdeadbeefbabe 4y ago

> goes geek-crazy for the first year or so The problem is that they don't stay geek-crazy?

PeterisP 4y ago

Fine-grained measurement is useful when you have options for fine-grained action. You don't need a chip to tell you that the soil is dry, but if you can use that chip to regulate drip irrigation that can apply substantially different flow to different plants, then you can get a not-too-much, not-too-little watering even if you have a big variation in conditions. You don't need a big analysis to acknowledge that everybody knows that a particular competitor has lower or higher prices and adjust your pricing; but doing that continuously on a per-product basis does require data and analysis.

ryguyrg 4y ago
Agreed. But how many executives will agree to take these fine-grained actions to achieve value from the data? How many data teams are able to build up a strong-enough argument to convince them? I've worked on many product-led-growth initiatives in the software industry. The software industry is probably the biggest 'believer in data' there is -- many scientific-forward minds who understand the value. However, even in the software industry, it's really hard to convince folks that if you make 5 improvements that net 1% conversion gain each, you can dramatically improve revenue.

calvinmorrison 4y ago

On the flip side, there's some great action coming from data insights. Look at Strella Biotech - they're putting sensors in sealed warehouses to detect spoilage for certain vegetables and fruits. That's something that can have great returns with just a few IoT devices and a novel sensor.

jschveibinz 4y ago

This analysis reminds me of the big interest in the use of hyperspectral imaging for agriculture. The idea was the greater spectral resolution (greater than Landsat) would result in more interesting information. Agriculture was one of the applications. But, once you did find the interesting stuff, you no longer needed a hyperspectral sensor. You could just look at one spot with a much lower cost sensor. So hyperspectral, like big data, is useful up front. But in the end, much simpler tools and algorithms will solve the problem on a continuing basis.

didip 4y ago

I've been telling folks, storing everything all the time is wasteful, a better alternative is: 1. Keep the raw full data for short period of time, at most 1 month. 2. Downsample what you need for longer period of time (5-10% of the full data). 3. Aggregate your metrics on a yearly basis to save money and compute costs.

tuatoru 4y ago
Yeah, there is perhaps a data usefulness duck curve. First data useful for specific immediate problems, not a lot of use for a while after that, then 15 or 20 years later, the big picture trends start to provide value for big decision making. Not many orgs keep their data that long, though. Or even think about the future that far.

pron 4y ago

Interesting. Sounds as if what's really needed isn't so much collecting and analysing lots of data, but an alarm that's triggered when observations deviate from a set of assumptions. Observations that confirm some definition of "normalcy" -- as most observations would -- can be discarded.

fijiaarone 4y ago

Or they could put on their work boots and go walk around the field and kick a few dirt clods, or science forbid! put a hand in the soil to check the moisture content.