But I guess, see your name is H, but I'll introduce myself and then maybe we can go
around and introduce each other.
So ourselves.
So my name is Keegan and I founded Canonlytics.
So I've been working in the cannabis space for three or four years now.
Started as a laboratory analyst, found clever ways to collect data and save people a lot
of time.
And, you know, now I'm just trying to primarily help laboratories just do cannabis testing
better, simply, easily.
And I found that a lot of people have an interest in data analytics.
So my background is in economics.
So in a comparative advantage of mine.
So I just thought I would just share what I know and see if there's other people that
are interested in.
There's quite a few.
So it's always fun to talk.
Let's go next.
I guess Paul, would you want to just, I guess, reintroduce yourself real quick?
Sure.
Yeah.
My name is Paul.
I'm a data scientist with a major automotive manufacturer in Michigan.
I'm just wrapping up a master's program in data science.
I'm getting a little bit of feedback.
I don't know if that's coming from somebody's speaker.
So I'm wrapping up this master's program in data science, and I was looking for a project
sponsor and Keegan was nice enough to agree to help me with my graduate project.
It's on some retail analytics in the cannabis industry.
So yeah, and Charles has been helping as well.
So yeah, that's my story.
I hear you Keegan.
Sorry, I was on mute for the feedback.
So Charles is helping analytics with an interesting project right now.
So we're sprinting on a laboratory information management system.
The critical part of that is importing data from scientific instruments.
There are a lot of these instruments are spitting out data files and the labs are collecting
them in various means, often different means for different instruments.
And we think there's just a simple, easy way just to collect the data.
That way people at the lab, like the analysts, quality assurance manager, scientific director
can review the data, issue their certificates and spend more time doing science, less time
doing data entry, which is something computers excel at.
So Charles, would you want to pick up from there?
So yeah, my name is Charles and I have about 27 years of programming experience and I've
worked in a lot of different areas and now I'm sort of transitioning into data science
and machine learning.
So Charles is helping with the parsing and importing of the instrument data because he's
a good data wrangler.
And then H, would you like to introduce yourself?
You can pass if you'd like, we can jump in.
I'll go ahead.
Okay.
Can you hear me all right?
Yeah.
Cool.
I'm Heather.
I'm not going to say I'm an ex-scientist, but I haven't been in the lab since like 2014.
But since then, I have begun to use cannabis.
So it's like the two worlds have now combined where I've been out of the lab, but I still
have an innate interest in data science and in the cannabis industry.
So that is why I'm here.
Well, that's awesome.
You could, if you want to check out the CanLytics GitHub, it's open source.
And so I'll put the link in the chat.
And your unique background in the lab space would probably be helpful because you'd be
surprised there's the amount of people with know-how of how laboratories function and
work and the workflow.
It's limited.
There's a shortage.
So there's a high demand for people that understand that workflow and can articulate it.
So yeah, we're always happy to have you, the board.
Yeah.
The interest is there.
I just think that there's not too much available where I live, unfortunately.
So yeah, that's the conflict.
So I'm sort of visiting labs, but there's a lot of remote work that can essentially
be done.
So really anyone around the world who can own the repository and as a text editor and
make contributions.
That's a good point.
Good point.
Keegan, I have a quick question for you.
In the lab testing world, can any of that lab test work be done somehow remotely?
I was just curious if that, I don't know anything about it.
So I was just curious.
In an ideal world.
So the lab space is real interesting.
So there's amazing opportunities, right?
So the instruments are powerful.
So they have a lot of capabilities.
So if you had everything set up perfectly, you could operate instruments with minimal
effort by the analysts.
You could get the data and you could almost view the data remotely.
Potentially issue certificates remotely.
You could view things like chromatographs and even potentially operate the instruments
themselves remotely in an ideal world.
In reality, getting the network set up at the laboratories is a huge challenge.
So Heather can attest to this, it's hard to have everything communicating.
So typically the microbiology department has their data stream and things coming off of
the gas chromatographies coming in the HPLC, that data streaming somewhere.
And then they may have like a LCMSMS doing pesticides.
And then that may have an entirely different data stream.
And it's for a variety of factors, right?
You may set up your instruments in staggered times.
You may set one up in January.
You may set one up in April and another one up in August.
They could be different manufacturers.
So you may go with Agilent for your HPLC.
And then you may have another vendor for your, if you're doing heavy metals, like an ICPMS.
And then if you've got two different vendors, they don't have the incentive to help you
aggregate your data.
Yeah.
It sounds like there's a lot of potential for, I guess, like, well, what you're doing,
right?
Like lab automation, bringing together everything in one source and integration, and then probably
scaling as well, if you get past those hurdles.
It's interesting.
Okay.
Well, it's, well, exactly.
And so that's actually what Charles and I are working on really this week.
So basically by next week, hopefully we'll have like a minimal use case in potentially
in production.
Oh, that's awesome.
Congrats.
So, well, we've got some coding ahead of us, but the idea is the data files are a little
different instrument to instrument and maybe how the data files are output.
But you can maybe kind of abstract that a little bit and just kind of take that into
consideration and because ultimately you're just getting the data.
So really you just want to get the data from all the instruments and just, okay, this instrument's
an Agilent.
So we'll just take that into consideration when we're parsing the data.
Or this one's a Schumas do HPLC.
So we need to parse the data slightly differently.
But then once the data is parsed, let's just upload it to the database, all the data relatively
the same.
So we can kind of structure the data the same and get it in real time.
So from just deep thought about the laboratory space, I think this is a particular bottleneck
that you can shave off a huge amount of time on your turnaround time because it's essentially.
So your instrument, you've loaded up maybe 50 to 100 samples and each sample takes 10
minutes to run.
So you could wait for 500 to 1000 minutes and then parse all the data and then issue
your certificates.
Or if you somehow have this nice and automated, sample one gets tested, maybe you get 10 samples
tested, you can issue 10 certificates and then everything else is still running.
So it helps smooth out your production so it's not so lumpy.
And you can prioritize things, you can get some.
It's amazing.
So if you review sample one's results before the instrument's finished running, you can
potentially run and prepare a new quality assurance check.
This will happen.
So if you're running edibles and you just get a wild result, you typically do a quality
assurance check, just a sanity check.
Okay, this is what we think it was.
There was an analyst in it.
Like missed pipette or some sort of.
Right.
So it sounds like with the, and maybe Heather knows a little bit about this as well, but
so the opportunity for you to streamline and optimize is there, but you can't scale people
very well.
So from a technical expert kind of perspective, we talked about this a little last time, but
where do you think the bottlenecks are as far as like having the right people in the
lab and that being a bottleneck?
Honestly, it's really just based on my experience, the finances in the lab.
So all of this mention HPLC, LCMS, like everything I did was manual up until the very last time
that I left.
I hope to go back one day, but all the column work, everything, having to walk in and out
of the, any dark room, cold room, everything is running not with an automated system that
will even track and tell you like, oh, this is running low.
You should add this or wash with this solution.
There's nothing to everything was manual.
So my, and would it, I mean, ultimately I, you know, my decision to leave academia was
also because you have to decide, do you want to, you know, to fight for grants for the
rest of your life?
And the thing is that's okay if you want to do that, but I know that I didn't have what
it took.
And I know the impact within the lab on of the finances on the anybody working in there,
they become competitive among each other within the lab.
And that can affect the, I guess, the scientific growth of everybody working there.
It's so, I don't mean to like stray off the main topic, but that's just my opinion is
it's just literally how much money does your lab have and can they keep the people in there
long enough so that they make something and create something, find something that's a
breakthrough something.
Heather hit on an interesting observation that goes to, yeah, like, well, yeah, how
we really envision labs.
So basically it's like, Oh, Keegan, we lost you.
No, I'm just thinking.
So basically you have like your method development managers.
And so you want them like doing science and research.
So you want them trying to optimize your methods, figure out how things are running.
But what they end up doing is like Heather says, they have to record temperatures.
They have to, you know, they have to make reagents.
They have to record IDs in their lab notebooks.
And so you end up spending, if things aren't like super efficient, then you end up spending
a lot of time just taking measurements and doing like trivial tasks.
And then you never have time to do any research or any science or you have limited time.
It really cuts into your time.
So I didn't know that.
So I was just naively thinking that when these labs are running, they're just trying to crank
through generating lab results and reports so they can get to the customer.
So it's more there's actually research going on in these testing facilities.
Well, essentially, yes.
So like at the cannabis industry, it's cutting edge and it's competitive.
So there's some like research articles.
So there's like some like gold standard articles and like everybody tries to kind of base their
methods off of these articles.
But then you kind of run into complications.
You realize, oh, in practice, this will gunk up your instrument.
And so you have to, you know, take extra precautions or you have to there's like quality assurance
steps that you can take that aren't well defined.
So the standardization doesn't seem like it's because it's a new industry.
There's not that kind of I'm not say a recipe or formula, but it seems like that's all kind
of being kind of discovered and developed.
Exactly.
It's real like and it's it's kind of competitive, so people are kind of they like, you know,
they they've spent a lot of time and money developing their methods.
So like, when they're doing these things, they may break a piece in their instrument
or they may spend a whole bunch of money on strange compounds or reagents.
So they may like try this one reagent.
And then they may.
So basically, like when you're putting things on the HPLC, you use solvents.
And then when you put things on a GC, you use carrier gases.
And you can use different types of them.
So you you may experiment and so you say, oh, this one gas.
Like I'm not like a GC expert, but maybe there's hydrogen or nitrogen and various mixes.
And then with the HPLC, I think there's like methanol and maybe acetonitrile.
And so you may experiment and you'd say, oh, maybe methanol works better.
Or maybe the acetonitrile works better.
And.
Sorry, Keegan.
So if I guess states have different standards for their testing, right?
So there's no standardized across the board like these 12 things that we have, we have
a threshold and you can't pass the threshold on these 12 measures, whatever they are.
There's nothing like that.
This kind of the Wild West.
Well, typically you have to pass.
You have to like.
Like match like standards, they said.
So I'm not sure if I'm using the right terminology.
Which the word proficient like proficiency tests.
So they'll basically send you a substance and you have to measure the.
Like the like the concentrations within a certain bound.
So you have to be like within maybe 10 percent of what they expect or something like that.
So you know, when everybody's getting their license and getting accredited, they basically
have to prove that, OK, yes, we can measure pesticides.
We can get the concentrations, you know, relatively close to what they're going to be because
there's always a bound that your confidence bounds.
And so with a lot of the method development is, is just getting more accurate, more consistent.
Because then.
When you're more accurate and more consistent, like like there's less things that are called
like out of spec.
So then.
To say you like, you know, you run your your standard and it's not what you expect.
So you see you're expecting it to come out at 80 percent THC.
This is a rough example and your standard comes out at like 60 percent THC.
You're like, OK, something went wrong.
What went wrong?
And then, you know, potentially we're going to have to maybe relook at all of these samples
because something may have just gone wrong in the.
I see.
So there are there are different standards between states.
Like what's what you have to test for and what's acceptable.
Oregon is really different than Washington.
And I don't know about California or Michigan or any other states, but I know between Oregon
and Washington, there's a huge difference about what's what's allowable and what they
have to test for.
OK, so that that definitely complicates things, doesn't it?
Yes.
And like Charles said, with pesticides, there's a whole nother level of complication because
the cannabinoids, they've got it pretty well dialed in.
There's like a few like, you know, tricks of the trade where people can better analyze
the cannabinoids and others.
But generally they can measure them fairly well.
And that's all about just everybody's just trying to do it as quickly as possible.
So everyone's trying to just shorten their test time.
Right.
So you could do it a long one, maybe like 16 to 18 minutes.
If you get a super good instrument, you can do it in like four to six.
And then typically maybe somewhere in between there, you know, you're trying to push that
down.
The pesticides you're testing.
Many some states, maybe just 12, but typically 70 plus in Canada, it's in the hundreds.
So like 120 or so.
And then when you're doing that, there's all these complications.
So maybe this one compound is just notorious for false positives.
And so if you run it your instrument one way, you'll get a false positive.
And then you're like, okay, let's run it the other way.
And then it's like, oh, no, that's not what we think it is.
So there's and this is where I'm not an expert.
No, I'm sorry.
There's a lot going on.
I definitely don't want to, you know, totally run a rough shot over the over the conversation.
So I was just, it's really fascinating.
I know there's a lot going on here.
And I just didn't, I really don't know much about this area, especially, you know, in
the testing space.
So just curious.
It's it's yeah, we don't have to get bogged down in this, but it's yeah, it's quite interesting.
And it adds a lot of good data points that are often missing.
Because now we like, you know, we know a lot about the actual products themselves.
So we can kind of see, okay, well, what appeals to people.
So this is where some some ideas are, but there's not much research, but just kind of
seeing, okay, dude, you know, certain people gravitate to certain, you know, breakdowns
of these compounds of chemicals and like the terpene.
Gotcha.
Well, thanks for sharing that.
I, yeah, like I said, I didn't have much perspective on it.
But now I kind of see where all the some of the new nuance creeps in here and where the
opportunity space is.
Yes, because, you know, like, like I said, you know, you know, I'm in essentially the
business of data wrangling.
And this is like, you know, Charles is going to find out quickly.
This is hundreds, depending on how you want to count them thousands of data points.
And so you can't necessarily get them all.
But basically, we're just trying to collect as much data as we can and help help the labs
essentially review the data, right?
Because if they're spending all their time entering in data, then that's a lot less time
that they get to spend actually reviewing it, making sure things like look like they're
in spec and enduring a method development.
Yeah, I could see where the lab would definitely be could potentially be a choke point right
in the whole the whole value chain.
Because if you have trouble getting the right people in the lab, have trouble with standards,
you have trouble with calibrating your tests and all these things, it definitely seems
like there could be a lot that could be improved.
Yes.
And just from from what I've observed, it seems that for whatever reason, it's basically
like the maybe the like the wholesalers, it's all like the wholesalers are always just trying
to find retailers to, you know, buy their products.
And it seems like, okay, once they've established their relationship, then it's like, okay,
now let's get our products tested and sold.
And so then they basically they show up at the lab and they're like, hey, will you test
this like we're ready to sell it.
And then the labs like, oh, you know, we're backed up.
Yeah, it's going to take like five days plus the weekend and or method development managers
on vacation or, you know, what have you.
And so then like, yeah, the wholesalers just calling up the lab saying like, hey, like
we're trying to trying to sell our products.
You know, we're we're results.
Yeah.
And so this is I just had it was not in her head.
It sounds like she's probably suffered through some of this stuff.
Have you had her?
Well, yeah, I mean, I feel like sometimes there's like a dearth of terpene terpene levels
in the flower in the state that I live.
And it doesn't matter what the brand is.
It's just a pattern that I've noticed.
There are little local things like that.
Like why going on?
Maybe I'm overthinking it.
You know, it could have been that gorilla glue.
I don't know.
So maybe so that could be a sign of basically people just rushing things to market.
Yeah, you can tell.
Like this is usually almost at two percent mercy.
Why is it my why?
Not that I wouldn't still buy it necessarily, but it's like point four percent or less than
like it's not.
I mean, it's the same, you know, genetically the same strain, but it doesn't hit as hard.
So depending on what you need it for medically or otherwise, that's just, you know, something
I'm mindful of.
So yes, I'm laughing at my own pain.
Well, it's interesting to observe because we've talked about this before.
The great debate is, was does THC matter?
And in your case, you're going beyond that and you're saying, you know, mercy matters,
which is which is interesting because we.
You'll hear a lot of different takes.
So a lot of people put a lot of stock into the cannabinoids terpene data and they'll
even base their breeding strategies on that.
And then other people will just, you know, dismiss it and they just go by rules of thumb.
But they are there's both successful players in both camps.
So is there a big drive to quickly set up labs to meet the demand?
Well, it doesn't necessarily have to be quickly.
There is just a high demand to set up labs.
So.
And it takes some time.
So maybe you're right.
So maybe there is demand to do it quickly because people always wait too long to do
things.
So, for example, with the labs, you really want to get started like a year before, at
least before.
Your state were to get cannabis permitted.
So like a year before.
So if you like saw that, oh, I think.
Whatever the next state is to permit cannabis.
Louisiana maybe, but.
Or rather a lot of labs are setting up in Florida.
I think New York's not yet online.
So I bet you there's a lot of labs that are setting up right now in New York.
And it's not always too late to set up a new lab in a state that's already going because
there's a high demand for it.
But it does take some time because you're going to have to get your building, get all
your instruments in there, get your team, then get like sort of internally validated
to make sure like, yes, we can test things.
Then you're going to have to get like certified by the state.
And then, yeah, then start ironing things out, growing your team, hiring lab analysts.
Yeah, and that's not even considering all the financing that must be involved to try
and set something like this up.
Must be a lot of money.
Yes.
So it's interesting to see people pull it off because there's some, so like there's
an awesome example of somebody in Washington state, Jeff Doughty, Capital Analytics.
So he basically started as a cultivator, just a small cultivator, and then bought an HPLC,
which is not cheap.
That could run you $40,000 to $80,000.
That's not negligible.
But then he just started testing cannabinoids in-house.
And then people just were like, oh, like, and so I think he's maybe started doing a
lot of research and development testing.
So just doing essentially cannabinoids.
Yeah, essentially cannabinoids.
And then he built up essentially, well, he built up a lab.
So then you would get a GC next.
So you would get a GC or a little cheaper.
There may be a real cheap one, maybe like 15, but maybe 15 to 30 or 40,000.
And then you can start testing turkeys.
And at the same time, you can set up your microbiology lab just to do simple testing
of microbes and micro toxins.
That's still going to be expensive.
But if you get a small team, you may be able to run that for 10 or 15 or 1,000.
So you can start a small lab if you've got a science.
It often takes an entrepreneur and someone with a good science background that can be
the same person I've observed.
But it can be any team.
But I've seen it done where they start small just doing the basic tests and then gradually
build up some clients.
And then they can buy the big instruments like the LCMSMS, which is going to be like
300,000.
And the ICP, which is 175.
And then once you get instruments like that, you have to get expert.
You really, really need method development managers and several chemists and several
analysts just to make sure you can get everything done.
So I would honestly recommend.
Well I'm not sure what I would recommend.
So you do see labs just starting up just with everything, just going full speed.
But I don't think there's anything wrong with gradually growing your lab.
As we were talking about before with some of the shortage of the technical experts,
that's another area, right?
Could you potentially, this might be a little naive, but could you potentially teach somebody
to do the lab tech work without necessarily having an advanced degree to meet that kind
of demand and still be effective at what they would be doing?
Well I'm sort of living proof.
So I just showed it at the laboratory and I had a degree in economics.
I principally got the job because like I said, there's a real shortage in people with, it's
interesting.
So you'd think there'd be more, but there is a shortage with people with scientific
degrees that are interested in doing lab work and aren't already snatched up, especially
people with experience.
And then, so it's tough to find people.
So I had a degree in economics.
So I think the lab manager thought, okay, maybe this person can end up doing statistics
at some point.
But it's really just, we just need somebody competent who can just read the, what's called
the standard operating procedures, the SOPs.
So we just need someone who can just read those and just go through the steps because
it's not the trickiest thing in the world.
And honestly, I think lab work's real fun and it's fun work because you basically have
got your steps there.
And so you can just go through the steps.
And yes, it does help to like know a lot about the reagents and the chemicals, but you have
all the steps right in front of you.
So it says pretty clearly like, okay, you want to pipette some, pipette like one milliliter
of methanol into this vial and then put some cannabis in there.
And then go through these steps.
So it's pretty straightforward, but you need someone preferably with a degree in science
who can go through and follow the steps and just be reliable and all that.
For whatever reason, it's hard to, yeah, it's hard to find lab analysts, I think.
Yeah.
That's interesting.
Yeah.
So I could see some bright entrepreneur that's working in a lab that could go, I could boil
this down to like you're talking about some of these lab analyst type steps, like recipes
or whatever, and then start training these people to meet that demand.
That is an opportunity there too, it seems like.
Oh, well, like definitely.
And like Heather, you may seem like you could do something like this, where it's just like,
you just basically kind of give people instructions on, okay, this is how you pipette, you hold
the pipette straight.
You don't tilt it or you're going to get a wonky measurement.
So there's just little things like that.
And then once you kind of have given your people instructions, then they're off to the
races.
I think there's a, like you said, there's a, not even in the lab space, but everywhere,
but it's just essentially just a video tutorial.
So if you're an expert, you would basically maybe have like a video tutorial on how you
could test microbial or test microbes in cannabis, or this is how you do an extraction for pesticides.
This is how you clean the filter on your HPLC, or this is how you do inside the HPLC, they
have what's called the column.
And sometimes you have to change them and like put in a new one.
And it's a really intimidating thing for someone who may not have like a background in science,
because it's basically like, you're going to have to kind of take apart the instrument,
maybe like unscrew some things.
And it's a little involved, but it's one of those things where if you learn, it's real
valuable.
So I think there, like you said, there's value to be added where you could just have simple
video instructions on how you can go about testing and maintaining the lab.
Yeah.
So I've got a background in aviation.
I've done some flying.
And then when I was in the Air Force as a missile launch officer, working with the ICBMs,
that was all checklist driven.
And these checklists were extensive and flying is a checklist discipline activity as well.
You can write a checklist for just about anything.
I know a lot of surgeons, a lot of doctors use checklists in the OR and stuff.
So I mean, it's just people rely on them heavily.
And it sounds like this could be one of those spaces that could rely on.
Of course, you definitely given the opportunity, you want somebody who can run a checklist,
but they also have the context of understanding of why they're doing what they're doing because
that helps.
But it seems at the bare minimum for a lot of these lab tech roles, some of the work
could be boiled down to checklists and for the more simplistic things.
Definitely, and this actually kind of brings us full circle.
So if the method development manager is spending their time doing data entry, they're not going
to have very much time to train the new chemist or the new lab analyst.
And that's a critical, critical part of the whole operation.
You would be surprised that when things are running well, the senior chemists and the
method development managers, they may spend a lot of their time essentially mentoring
or explaining things to the junior chemists or the lab analysts.
So even the method development managers, everybody can learn from each other.
The micro team can learn from the gas chromatography team.
So that's interesting.
So thank you for sharing all this.
I actually now have a general picture in my mind based around the instruments that you've
been talking about, like the HPLC and the GC, was it LC, SMS, the bio, is like a bio
lab team for looking at microbes and that sort of thing.
So just by virtue of you just talking about these different spaces, I kind of started
to get a picture in my mind of the different teams or functional groups within the lab.
So that's pretty cool.
Exactly.
I mean, there are like small labs where people wear a lot of different hats.
It's sort of when your lab grows, these become like different departments because they're
real specialized and you want almost like someone with a degree in microbiology or biology
in your testing for microbes.
And there's different types of chemistry.
So certain chemists are really good at analyzing heavy metals.
And then others are just really good at using like the liquid chromatography for cannabinoids.
And then so everyone has their own sort of expertise.
So yeah, I was just popped into my head, but you know, when you take your car into a dealership
to get worked on, right, you've got all these different folks that know different aspects
of the car's function and they know a lot about troubleshooting and fixing issues and
things like that.
But sometimes you just need to take it to the jiffy lube, right, and have a certain
thing done and it's done quickly and it's done well over and over again.
And it just, it seems like they're, I don't know, my prediction of, you know, in the future
that I imagine that somebody somewhere is going to see this opportunity and gobble it
up and turn it into a standardized, as much as you can, obviously.
You're talking about the method develop manager and there's definitely an art and a science
to this, but I can see somebody optimizing this opportunity at some point.
They're trying.
Are they?
And on all the board, and so it's, you know, if they can do it well and just test things
well and cheaply, then I mean, let's do that because right now, like I said, there's a
ton of improvement that needs to be made.
So we'll see.
Like I said, there are definitely people trying.
So there are like big laboratories, multi-state laboratories, but I would love to see how
the sausage is made.
I would love to see how organized their data is.
Yeah, good stuff.
Good conversation.
Definitely.
I'm not dissing them.
I'm sure, I think there's some phenomenal labs out there.
Any multi-state laboratory probably has a grid system to keep things organized.
Yeah.
But I don't want to keep, I keep asking questions.
I don't want to keep Tony in the conversation, so I'll be quiet.
Oh no, it's good to get some feedback.
Yeah, I guess we were going to talk about like sales data and things today, but myself
and now Charles, we're just going to be in the weeds with this, at least for the next
week or so.
Yeah.
But we're just going to try to make our contribution here because like you said, there's a whole
lot that could be done.
And like, I'm not going to pretend I'm going to, like, so you, like kind of like you were
talking about with, or maybe we were talking about with processors and cultivators where
they can start automating this, like assembly line style.
People would love to do that in the lab space because just like, oh yeah, just put the sample
here and it'll grind it up and put it on the HPLC and test it.
And all you have to do is sign the certificate and send it out.
But that's like, like we said at the beginning in the ideal world, that's how things would
work.
And it's just, just real smooth automated, but in practice, there's a lot of moving pieces.
Yeah.
Well, you gave me a taste of that today.
There's tons.
Yeah.
And it's like, and that role standardized.
So there's different vendors.
You'll learn quickly when you talk with scientists that they all have real strong opinions about
how things should be done.
So, so it's fun when you get a couple of scientists in a room.
Yeah.
It's been a good conversation.
So I guess we've got 10 minutes left.
Do you want to just look at a forecasted some Colorado data real quick or just call it a
day or it's up to you guys.
I know that you mentioned last week that you might be showing something.
So if you have too much trouble, I'd like to see it.
Oh yeah, sure.
We've got 10 minutes left.
So we can run through, I do some data analysis real quick.
So Colorado, like we were saying, does a fairly good job at publishing their data and better
than I thought, unless it was updated recently.
So at first I thought you just had to get monthly reports, which are useful because
you have sales by county.
And I sort of had mentioned this to Paul, but an interesting thing you could do here
is basically run a regression of total sales in each county on the county's medium income
and the population, because I realized Paul that you want to control for population because
obviously places with the high population are going to have higher sales.
But it would be interesting to see if the medium income affects total sales in each
county.
I don't think that's true by the population.
The article I read about the counties along the Oregon-Idaho border, they actually have
a higher per capita sales than in Multnomah County, which is the Portland area, because
you have an influx of people coming from over the border.
So you basically would need a...
I would have to think about how you would interpret the results.
You always have to think about how you interpret the results when you start tossing a lot of
variables, but you could basically add a control, like a zero or one, if the county is a border
county.
So that would maybe control for the cross state sales, theoretically.
But just to keep this moving quickly, that is an analysis you could do.
For now, we're just grabbing these totals, which are actually conveniently in this historical
report.
So I already had some Colorado data, and so I just sort of depended this on.
So I really need to flesh this data out, because Colorado also does these annual reports.
So I think they're quarterly, but they don't publish them that frequently.
But if you look at one of these, you have some amazing data points here.
So you have the new businesses.
I think you have the expired licenses, and then the ones renewed.
So this may have been where I was calculating the entries and exits from.
But long story short is, I haven't actually parsed this data yet, or at least not in an
automated way.
So there's fruit here to be had, but it may be high-hanging fruit.
But nonetheless, just wanted to show you this data, because you can get some good plants
here.
That was cool.
But we're just going to just look at the totals, total sales, just for simplicity's sake for
today.
And in fact, we had looked at this data back in March or so, and I really should have done
the forecast then.
And I thought I was planning on it, but somehow I didn't get around to it.
It doesn't look like.
So now is better than never is the Python philosophy.
So we're just going to do some quick forecasts of this Colorado data.
So we'll just kind of walk through this.
So just real quick to just show you what the data looks like.
So as we see, Colorado has quite impressive sales.
Just to look at just the last handful of observations.
In April of this year, they did 200 million in cannabis sales in Colorado, and it's just
hard to really comprehend the scale of that.
I mean, it may make sense, I guess, based on the population in Colorado, but to me,
it seems just like a staggering amount of revenue.
And it seems like there's some seasonality in the sales here because you've got dips
in wintertime and peaks in summertime.
Do you think that's due to just harvesting times or for outdoor crops, or is there something
else going on?
It's related to the economy.
So as we were talking about earlier, there's some studies, and I'm going to add them to
the repository after this, but just to back up this claim, so I'll correct myself if I'm
wrong.
Unless I'm wrong, I believe studies show that as your income rises on average, your cannabis
consumption decreases.
So there's an interesting play in cannabis sales related to economic growth, really.
So basically, when the economy is doing well, people's income increases on average.
And so there's counteracting effects.
So there's an income effect where people generally have more income, and when you have more income,
you just buy more of everything.
So when you have more income, you'll buy more cannabis.
But then there's a substitution effect where as people have higher incomes, they spend
a relatively lower portion of their income on cannabis, and they spend it on other things.
I'm not sure why this is, maybe.
I don't know why, but this is just what people have observed.
Which effect dominates?
It actually kind of looks like the income effect dominates where when the...
Well, the pandemic was a strange time, but generally I feel that cannabis sales spike
when economic growth spikes and goes down with economic growth, but they're not perfectly
correlated by any means.
That's cool.
Because Washington State, do you know if Washington State has a lot of summary stats like this,
like Colorado does?
I'll need to check.
I could do some digging around and checking.
I just would wonder off the top of your head if you knew.
I mean, for some reason, I just don't think they do, but there are some services, more
than a few third parties that do aggregate the total sales.
For some reason, I don't think the state does.
I think they were, but under their current traceability system, I don't think they're
doing summary statistics.
Okay.
That's cool stuff.
And then just to just wrap up real quick, we can just do a quick forecast just with
ARIMA.
And so I'll get the presentation where we talked about ARIMA uploaded, but basically
just using past observations to predict the future.
And as you can see, our forecasts really aren't that much more informative than really we're
just kind of continuing the trend.
But it's useful because we can kind of get a rough estimate of what the year total may
be.
And so now we can get an early estimate that we're looking at, like 2.5 billion in cannabis
sales in Colorado.
Wow.
And just to give you some perspective, in my hometown, they got a new elementary school,
and I think they spent 2 million on their new elementary school.
So you could, if you had spent all of that on schools, you could buy, or they could find
1200 new elementary schools.
Well, that's total revenue.
So they're not getting all of that in taxes, but they're getting a heck of a lot in taxes.
They're probably getting, I think in Colorado, 25% or so in taxes.
Maybe not that much, but I think in Oklahoma, they're getting 7%.
So you could maybe make between maybe 80 and 300 elementary schools with the amount of
tax revenue that Colorado is bringing in.
So I always kind of like to kind of put things in perspective.
And I thought that was an interesting way to make these huge amounts of money more concrete.
Yeah, it's a good data storytelling technique there.
Exactly because I'm not saying they're necessarily putting their money towards education, but
you know, that's a lot of public good that this was revenue that was getting captured
by, in some cases, not necessarily the best actors.
I mean, I'm not saying everybody in the illegal cannabis markets are bad people, but I'm sure
there are some shady characters there.
So at least now, the people making revenue, they have to file for a license.
They typically get a background check.
So they're at least not criminals.
So anywho, I personally kind of think that's a positive thing, but think of it what you
will.
Sounds good.
But we've run a little extra here.
So thank you for staying tuned and seeing that quick presentation of data.
Thanks for answering all those questions.
I really appreciate it.
And good luck to you and Charles in the week or so coming up with the work that you're
doing.
Definitely.
Thanks for coming, Paul and Heather and Charles.
So thanks for listening.
It's always fun to share.
And then until next week, we'll be making laboratory software and check in next week
and we should have a lot to tell about how things went.
And hopefully we've got some software up and running.
Sounds good.
All right.
Thanks guys.
Take care.
Bye.