Good morning.
And so I guess as you know, I'm doing a venture here in Oklahoma City or Oklahoma in general
for a little bit, essentially doing some outreach to the labs here.
So I'll show you on the map the places I've been and you know, that you know, CanLytics
is spreading the word about our, you know, Canvas testing solutions and I'll tell you
a little bit about the data here.
But before that, like, how are things going with you Charles?
Pretty good.
I think I have a job.
Oh.
I think I'm going to start like in a week or two.
Well, please, please tell me.
It's working with like 3D imaging and making measurements off of 3D image scans using,
using like, you know, using machine learning.
Interesting.
So have you done much machine learning work?
So there is actually a lab that CanLytics is interested in the work CanLytics is doing
and one of the things they're interested in is finding uses for machine learning.
So how do you go about even using it?
You get the problem first or do you have a good model in mind?
So how do you even go about things?
Well, I mean, yeah, you need to figure out, you need to figure out what they want to solve
and then, you know, and then try and find a model that will work with that or a lot of
times you have to build your own model, you know, and you have to do some feature engineering
and it's, you know, it's a lot of trial and error and kind of just sort of knowing, you
know, having some kind of intuition as to what to try.
So say, so the problem we have at hand is, say like a lab wanted to like maybe almost
predict the chance of a sample failing as it comes in the door.
So that way, you know, you can just have an eye out, you know, in case and you can like,
you know, run those samples right away in case there's a failure, you can, you know,
let people know right away.
So is that a reasonable use for machine learning?
And if so, how would you grow about even starting?
Yeah, I mean, you could do that.
I thought about trying to do that with the Washington data.
There aren't a lot of failures in there, although I would imagine in most cases there aren't
a lot of failures.
But so you're probably going to, the only thing you're really going to know ahead of
time, right, is the producer and the strain, right?
And so it was real interesting.
So at KanaCon, there was a presentation about heavy metals.
And what's interesting is if you look at the distribution of, you know, heavy metals that
have been tested, say in water, soil, even in this case, they were looking at human blood
samples.
It's not evenly distributed.
So there's, you know, there's hot spots, you know, certain places are sort of cleaner
and others are dirtier than others.
So I think geographic region, I mean, I've got a hunch it may play a factor, but I don't
know how well it would explain.
Yeah, you can use geographic region as one of the inputs, although that would be correlated
to the producer.
But you might have a producer you've never seen before.
So yeah, you might just want to, instead of going by the producer, you could go by where
they're located, time of year, maybe time of year that has some effect on it.
So yeah, you would just try and find things that you're going to know ahead of time and
try and predict, you know, the outcome.
It seems like a problem you could, you know, you could at least try.
Whether you have enough data and whether that data would give you enough information to
predict something is another question.
Do you think that's even the most value added use?
Or like, do you think it would even be that worthwhile?
Or are you thinking, like, so for example, can you just discover gold with machine learning,
just find avenues that you hadn't thought of, or do you really have to have a, you know,
great value added idea to begin with?
You can find, I mean, you can find things that you don't know about.
You could do some sort of like clustering algorithm.
I mean, that's something else.
You could, yeah, you could do a clustering algorithm and see, you know, if certain, if
certain regions or producers or strains and that, you know, fall, fall into a group that
fails more.
So, see if there's any similar characteristics.
Yeah.
I mean, I would be interested in working on something like that.
That would be, that would be pretty cool.
Well, with the Washington State data, we could technically do that, right?
So we've got their geographic location and potentially the failure rates, right?
So, so I think we may need to add that to the list.
So I could, I guess work on getting those data points, just the, well, we already have
several of these data points.
So we've got essentially their latitude and longitude.
So may just want to do, I like to do counties, but we could get more granular if we can get
as granular as we would like.
And so the next step would just be to calculate, you know, failure rates of the variable of
interest that we're interested in.
So, so the main things that you could test for failing would be the microbials.
And like you said, those may have a real low failure rate.
And then really the only other thing in Washington State is the residual solvents, right?
Because pesticides and heavy metals aren't, there's not regular testing on that, on those
compounds.
So do you think like residual solvents like from processors would vary from location to
location or?
It might, I mean, isn't that part of the problem is that some producers just, you know, heavily,
you know, like heavily spray their crops and others don't.
And you know, I mean, like, yeah, I mean, it's certainly worth investigating to see.
Yeah, so the residual solvents will be like your butanes, your propanes, and other extraction
compounds.
So yeah, I mean, it could be, you know, the type of equipment that they use to do the
extraction, how experienced they are.
Here's actually a good thought.
So at Canacon, right, the, they had a wide range of equipment, right?
So if you're, when you do processing, you have, you can invest quite a bit in extraction
equipment.
So once you've done your, you know, your propane or butane or your hydrocarbon extraction,
you can, one, you can invest a lot in an expensive extraction equipment, and then you're going
to need to essentially, you know, evaporate off all the hydrocarbons.
And so, you know, people have different budgets, right?
So a smaller processor is going to use, you know, less efficient equipment than, you know,
a large scale processor.
So process, so large scale processors may be more effective, you know, they may be more
effective at removing unwanted hydrocarbons from their products.
And so they may have a lower failure rate.
And so that would actually be worthwhile, you know, knowing.
So, you know, if you like, if you scale, does your failure rate go down as well?
Is there, you know, scaling for cost saving reasons?
Well, you know, economies of scale, essentially.
And so it'd be interesting to see if that applies to failure rates.
Yeah, that would be interesting to see.
That would be, you know, that's the kind of thing I've been trying to figure out, like
what kind of predictive machine learning type things can we do with this data?
And that would be an interesting thing.
And I've seen, there was like, I think it was a story on the news about people doing
extractions, you know, and like, having small operations out of their garage and stuff.
So yeah, I wonder, you know, I would imagine that the type of equipment that you use has
some effect on it.
And hopefully, if you're spending more money on equipment, you're getting higher quality
product out of it.
Well, exactly.
And that's what they're saying is, you know, you're getting a more pure product.
And so one would hypothesize that you would have a, you know, a lower failure rate.
We can potentially quantify.
So the way I think you can potentially measure the processors.
So there may just be a one size fits all processing license in Washington state.
So what if you, you know, proxy the size or the amount that they spend on their equipment
by their sales.
So you would just look at it, say, total sales by processor, say by month.
And then look at the failure rate for that processor for that month and see if those
are correlated.
So you'd run a regression of the failure rate on total sales.
So one would think that the larger processors have larger sales, thus they can spend more
on their equipment.
And we would hypothesize have a lower failure rate.
Yeah.
So I think we just discovered a research project.
Maybe a little large to embark on today, but I think that's another one that we can chalk
onto the chalkboard and potentially look at as early as next week.
Yeah.
Well, I guess just go ahead.
I guess we can go ahead and break into a little bit of work that I've done here recently and
I can present some of that if you would like.
So
So can you give me a second?
So long story short.
Canlytics has put together a map here where we're trying to map the cannabis testing licenses
in the United States, potentially the world as well, but for now the United States.
And so just to get some on the map over here.
Just been introducing Canlytics to various laboratories here in Oklahoma City and then
popped over to Tulsa, a handful of laboratories here.
I'm actually in Grove right now and I'll stop by Genesis testing just to say hey.
So just to get a look at the Oklahoma industry here.
So Oklahoma does have interesting data.
So stories.opengov.com, Oklahoma State.
They just have snapshots of their revenue.
However, you know, and they also have
a list of their licensed businesses so you can see the licensed growers, processors,
dispensaries, laboratories in the state.
So here we have PDFs.
And so we would like this data in a more readily consumable format.
And there may be others as well.
So Canlytics, we're trying to make data accessible.
So today we're going to make this data accessible.
So just to kind of show you some of the work I've done here.
So just using a handful of tools.
We'll basically go and we'll download the PDFs.
So we'll basically download each of these.
And then we'll want to parse them just to get them into a consumable format.
And then I just do a little cleaning of the names.
Just a lowercase and snake case.
Oh, I've committed this code to the Manifest Data Science Repository.
So you can follow along and really read into the script here when you really have time
to delve into that.
And then if you just want to grab the data, then we have it already created and uploaded
for you.
Alright, so let's look at the data.
As most states are moving in this direction just towards a little bit of privacy for the
cultivators and processors, your geographic location is at the county level.
So you don't really get much granularity than that, and so that's to respect the privacy
of these businesses.
However, for a lot of our analysis, the county level or even the zip code is perfectly sufficient
for our analysis.
And just to show you the scale of these, just in Oklahoma, there are some north of 11,000
licensees.
And this would include your transporters, your library, your laboratories, your waste
disposal companies.
It's first glance striving, or thriving.
So we really want to try to quantify this a bit more because all we really know is just
the number of licensees.
But these people may have just licensed.
They may not be operational.
So we want to get a bit better look at the industry.
So the next data point we can get is essentially the revenue.
You can download this as a CSV.
So I've downloaded that.
And once again, we need to parse this appropriately.
So one thing that I began to note, so you'll see this here in a second.
So let's go ahead and read in the revenue.
And once again, just parse the data just to get it into your format that we can use.
All right.
So does anything jump out at you, Charles?
It's linear and increasing.
Yes.
Almost perfectly.
Do you have much faith in that data?
No.
So that was something that's a little concerning to me is it's one would say how linear.
And so that's actually what I actually did next was I so here I crossed on just a, you
know, time index.
That way, you know, we just have a counter.
Then we can just calculate just a quick trend.
This is basically just running a regression of total revenue on time.
You know, where, you know, t equals zero.
Right.
Then, you know, we'll just, you know, plot the trend with total revenue.
And so, you know, we essentially have, you know, just a perfectly linear line.
So we just kind of, you know, now I'm just trying to think about like how to explain
this.
So, so honestly, the first things that came to mind is maybe the software developer just
put in placeholders, but that just doesn't seem likely.
Then I'm thinking, is maybe that's just what they're accounting for, or maybe they're
only taxing a certain amount per month.
It's hard to explain.
So do you have any thoughts on this Charles or what are we looking at?
No, it looks suspicious.
But maybe that's just like how they're accounting for it.
Like they're just like, they just, the licensees just submitted the year total and they're
just like accruing it per month, but it doesn't really explain like how they have a through
or am I just, or am I interpreting this data wrong?
Are maybe more and more people getting into the system legally?
Or have they loosened their requirements on medical marijuana?
Let's just calculate the, because it looks like there's about a, what is it, seven million
different.
So let's see.
Because basically I was wanting to start doing the statistics on this data, but like I said,
it just doesn't look, well, it just, I don't know.
I just need some explanation.
Like I'm just not certain what's going on.
So it's just.
So it looked like it was like, just when you were hovering over it, there was like 11, 12,
13% growth.
And so all the, every month grew, you know, by roughly the same amount.
Unless, unless this is just like.
Or are we looking at something weird?
Like are we just looking at like the aggregate or something?
And maybe like the differences, what's going on?
Because you see, it's not like, this is strange.
Well, why don't we write a quick for loop.
Yeah.
What's going on?
Thank you for bearing with me.
The tortoise always beats the hare.
So it's not the same every month.
This is interesting.
So let's just try to capture these values and analyze the difference here for a second.
So let's just look at this.
That's what you would sort of expect.
Like a revenue plot would look a bit more, doesn't that look a bit more reasonable?
Yeah.
So do you think this is just year to date?
Is that what we're looking at here?
Maybe.
When did medical marijuana become legal in Oklahoma?
It looks like sales began essentially in July of 2020.
I want to say January of 2020, but it looks like we just have data from July.
I mean, this just could be like a ramping up as people get into the system and start
moving from like, you know, the black market to the legal market.
Interesting.
Let's do this.
So I think that may just be.
Though I think this is not necessarily what we want.
Let's go back to.
And keep in mind, this is the tax, not the revenue.
So okay, so Charles, this is not what I'm thinking.
I'm thinking this is just a running.
Oh, yeah.
Right.
Because there's actual, then there's total.
The actual and the total are always the same.
Yes, I think this is just a.
You know, you know, cumulative.
You know, the cumulative graph.
So August includes July, September includes July and.
August.
And so then if you look at the difference, then.
You know, this is essentially, you know, your change in.
Tax size tax per month, so this would be the taxable.
This would represent the taxable revenue.
If I'm interpreting this correctly, and so it may be worth reaching out to.
The OMA.
This body, the Oklahoma Medical.
Just to to clarify that that is what we are looking at.
So we can actually.
Perhaps adjust our code real quick.
To account for this.
Pandas just do a simple.
Excellent so pain.
This is just a built in.
If that we can use so.
It does not.
We can't explain this initial five billion.
Because.
Well, let's see what the mean is over here.
Or mean.
Where was our initial.
Let's look at some of the statistics that they've produced.
So they're saying that in March.
We're not certain the year that almost one million came from.
It's really hard to this way this data really needs some exploration because it's.
Doesn't it seem like their industry is like staggeringly large.
What's the like.
Hold on.
I was calculating the total revenue.
I was applying the tax rate, but we need to do the change first.
Okay, so let's try.
It's just a fact that it's significant.
Okay, so let's see.
Okay, so that does look about right.
So it looks like they're getting about 5 million in excise tax per month.
That sounds a bit more reasonable, right?
Yes.
Let's see when this was published.
This was published in 2019.
So in 2019, they were getting about 1 million, a little less than 1 million with their excise
tax.
So it appears that by July of 2020, when they have this data published, it looks like the
first data point is about 5 million per month.
And then it looks like they're increasing about 5 million each month.
So this must be the cumulative.
Right.
Okay, and so then we can actually add on this first data point here.
Just your little hotfix here.
Just to go ahead and get the first data point back.
See how can we...
Can we just add... essentially just want to get that first data point back.
Because I never want to throw away...
Something I'm using...
Okay, so I didn't use that correctly.
Do you know off the top of your head how to sign the value?
Don't you want to use like iLock or lock?
Potentially.
So basically we want to fill in this first revenue observation.
Right.
I mean, it's something like...
Can we do that?
I think so.
Well, maybe we should give a suggestion.
Yeah, you're just sending it on a copy, which is not...
So I don't think that's the best practice.
You just need to say cop.copy.
Nonetheless, for now...
You're safe.
You're not going to do anything...
We'll serve our purpose well.
Let's address this later.
So we'll just look at it from August onwards, just so we can expedite things.
We'll come back and address this.
Okay, so does this look a bit more reasonable for revenue?
It does not.
Well, it actually does not sound very good.
They're getting about 77 million and the excise tax is 7%.
Yeah, so that's about right.
So it does...
I mean, in my opinion, it's a staggering amount of sales, right?
You're doing 77 million in sales in August, then it dips down towards December, which
is an interesting observation that it looks like it's inversely related with Christmas
shopping.
And then you just see this nice...
You see quite a steady rise to what's this, 90 million already in May.
So you have 89 million in May.
You're on...
Let's see.
So let's calculate how much revenue they've raised this year in 2021.
So we can say, okay, we want revenue data.
Let's just do this quickly.
So maybe we can just say PD.
Let's see, 2021... right, so that worked.
So there...
So if you... we just want to look at revenue so far for 2021, fairly steady increase.
And so let's go ahead and...
So in the first five months, Oklahoma's grossed almost 400 million in cannabis sales.
So I think Colorado is about a billion or perhaps even $2 billion industry.
And so here, Oklahoma, five months has done 400 million.
So let's just say...
So one could expect if they keep on this trajectory, that it wouldn't be surprising to see Oklahoma
gross one billion in cannabis sales this year.
So that's...
So that's 70 million in excise tax if I'm doing the math correctly.
So that sounds about right.
So they're doing a little more than five million a month.
So they're getting about $70 million.
And I mean, that's not chump change.
So...
No.
I mean, well, this is why everybody's interested in this industry, right?
It's going to...
It generates a lot of money.
Well, let's just money left on the table.
If you're not doing it, in fact, not only are people getting benefits from the medicinal
consumption, right?
So Oklahoma, this is actually all medicinal sales.
So these are all people who are using cannabis for medicinal purposes.
So not only are they getting that benefit, but yes, the state's also...
70 million, that's like...
It could easily be like 35 schools or however they want to spend that money.
That's hopefully going to have quite a positive impact.
So I think we've made a little sense of the data here.
And I think if we have time in the last little bit here, the interesting measure is we could
potentially look at the average revenue per retailer or grower.
So that way we can kind of get a measure of how large these businesses are.
So let's just do a dispensary account essentially.
So we're going to have to do this quite quickly, Charles, but that's why we're just crunching
numbers.
That's what we're all about here.
Let's quickly find out with your call and then isolate them.
Then count them.
There's about 2000.
And then keep in mind that these were probably...
These probably fluctuated throughout the year, but we can use a proxy here.
So we can say...
Well not really a proxy, but an estimate, so we can just say estimated revenue per dispensary
per month.
So that would just be our revenue divided by the number of dispensaries.
And then let's see if this makes sense at all.
Here's when you need to define everything.
And then this is yet another positive impact on the community, right?
Because you've got your patients getting their medicinal benefit, you have the government
getting their revenue, which they can distribute to the public through public goods like education,
roads.
You do see a lot of road construction in Oklahoma, which is a good thing to see, right?
They're fixing up their roads big time.
And then you look at the dispensaries and look at this.
They're grossing...
Look at the mean here.
The average is a little north of 34,000 a month for dispensary.
People who are opening up a cannabis dispensary, which requires a little bit of overhead, but
not that much more than opening up a restaurant.
They're going to be less and they can easily gross half a million dollars a year.
That's a good sized business.
So there's 2000 of them, right?
I'm sure they're not all making the same revenue, but right?
So overnight, Oklahoma turned on the switch and now you have 2200 businesses making an
average of $400,000 a year.
That's phenomenal.
That's good for them.
That's good for their employees, the people that work for them.
You have a large multiplier effect from this, right?
Because when a business opens, right?
They need an accountant.
They need to make signage.
They need to do advertising.
There's a huge ripple effect throughout the economy where these companies are now utilizing
other companies.
So I think I'll leave it there for today, but it's just phenomenal to see.
Any comments, Charles?
No, it's great.
They have to hire people who spend money around the community.
It might be interesting to look like sales by county or sales by zip code.
So Oklahoma does have a public records request.
So similar to Washington state, you can make public records requests to OMA.
And so it would be worthwhile asking.
So they just have their dashboard, which just has the totals.
So I think it is worthwhile to ask what other data is public?
So can we get the sales by county, so to speak?
Even if we have to do the data wrangling and crunching ourselves, they can at least get
us in the right direction.
So you're right, because I think further analysis is needed, because as we've seen in Washington
state, the distribution of licensees varies.
So you've got your concentrated retailers along the Puget Sound, and then you've got
the cultivation heavy Easternors.
We've got several fruitful avenues to pursue for next week.
So we've got failure rates to look at.
We've got geographic location to look at.
And there was one at the beginning that we'll have to review in the recording.
So I think it's been a productive day.
Yeah, that's good.
It was a good meeting.
So we were going to look at the processors to see if processor size affects failure rates.
So those are the takeaways for today.
Economies of scale are worth looking at.
The geographic location is worth looking at.
And as always, the number one rule is look at the data.
Well, awesome, Charles.
I think we'll leave it there for today and pick up next week.
Hopefully we can get things cleared away as to the timing.
And I look forward to crunching some numbers with you next week.
Okay, great.
Ask me to go to the home.
I will.
Get some good barbecue.
There's really good barbecue out there.
Ooh, I'll get some.
And keep, well, you know, keep the secret secret.
But, you know, definitely feel free to share anything that you can about what you've learned
about machine learning.
Because that's something, you know, we would love to learn more about as well.
Okay.
Awesome, Charles.
Well, once again, have a fantastic day and we'll be in touch.
Okay, see ya.
Bye now.