Oh, man, I saw it's like in the 60s there.
We got a cold snap, buddy.
It's just, yeah, it was like not last night, the night before it got down to like 45.
And I was like, wow, it's very unseasonably cool right now.
I'm jealous, man.
It's gonna be 109 here on Saturday.
What?
What part of Oregon are you in?
Portland.
It's gonna be 109 in Portland, Oregon?
That's what they're forecasting, yes.
Oh my gosh.
I mean, so to me, you know, based on my limited knowledge, that seems like way out of character.
It is, especially this early in the year.
Oh my gosh.
I just thought because you're closer to the coast, it'd be and kind of north, it'd be
cooler a bit.
Nope.
Oh my gosh.
Okay, we need to mix up the weather from your state and my state.
Yes.
Well, one thing that's interesting is, it'd be interesting to see if these heat snaps
have an effect on harvest, right?
That's notorious for these farmers, right, is there's one bad event and then, you know,
there's a monkey wrenching.
Yeah.
You're gonna have to start doing, you're an economics guy, Keegan, in, what do they call
it?
Futures trading or something like that?
Or like different agricultural type products?
Well it may be soon with cannabis where they're doing futures on yields.
So yields in production and yields in processing.
Never know.
You never know.
So, well, just, I see Heather's joined us again today, so essentially, Charles has done
some diligent work parsing data this past week and I've put together a user interface
and so now there's just a little bit of business logic left and we'll have a rudimentary laboratory
information management system put together.
Well, congratulations guys, that's cool.
Exactly.
So, like, there's still a few pieces to fill in, but yeah, it's coming together well.
So if you want, I could just show you at some point today just what's together and then,
yeah, maybe mostly of a talking day today because I've mostly been in the weeds with
that, but I'm curious, have any of you had any data adventures this past week?
Heather kind of grimaced there for a second.
Observed data.
I'll be an observer today.
Okay.
So I did a little bit of work this week on my graduate project.
Unfortunately, most of it's been writing, so my introduction, everything for my paper,
but I was able in Google Cloud in their AI platform to feed in my first set of data into
the market basket analysis algorithm.
I haven't really looked at the results much.
I was just trying to get the code to work properly.
So probably by next week, I think I will have some basic results to share if we wanted to
as far as some association rules that come out of the retail transactions at some dispensaries.
So I might have something rudimentary to share next week.
Okay.
And so these are just sort of associations between purchases that you may not be really
apparent on first glance?
Yeah.
So there's going to be association rules on two different levels.
The first level will be on association rules between the different product categories.
So there's eight product categories in the Washington data set.
And then the other association rules will be actually down at the product level to see
if any interesting relationships pop up there as far as complimentary products being bought
together.
That one's going to be more challenging because the data set that needs to feed the algorithm
is very, very sparse, but it should be handled in the AI platform in Google Cloud.
But that one's going to be a little harder to tease some intel out of it just because
some of the measures for these association rules become pretty diluted.
But we'll see.
And that's part of this whole process is kind of exploring this data.
So we'll see what pops out.
But to begin with, it's just going to be looking at the categories of the different products
to see how those relate.
So yeah.
So of course, I think the category is going to be promising.
The sample name is going to be all over the board.
So that may be difficult.
Yeah.
Do you have any data points on total price, like the total amount per receipt or number
of items by chance?
I mean, that information's in the raw data, but for the market basket analysis, I'm just
incorporating the product categories at the moment.
That particular modeling, not that I'm aware of that.
You may be able to incorporate pricing in some capacity, but I haven't really, if that's
what you're thinking of, I haven't really considered that angle yet.
Well, what I'm trying to get at is basically, in general, there is maybe low quality and
high quality cannabis.
How you differentiate the two may be just sort of arbitrary.
So it may not be the best.
Well, keep thinking along those lines, because this is just a starting point.
And if it gives us a springboard into looking at some different angles, that would be useful.
One of the things that I had sent some information to you, I think it was last week, and Charles
and Heather, just to kind of fill you in, to do this type of analysis, typically you
do it in retail stores and you're looking for, I gave the analogy before, but if I buy
peanut butter at my grocery store, then I might be also likely to buy bread and jam.
And that's kind of what this algorithm does, is it shows you those relationships.
But I wanted to look at some representative dispensaries to get kind of, treat them like
flagship dispensaries that they might be representative of the industry.
And Keegan helped me out a little bit last week with some good questions and some advice
on looking at dispensaries by median income of their zip codes, because he had noticed
previously that sometimes there might be a relationship between median income and kind
of the product base at a certain dispensary and how much they're selling and things like
that.
So I use that median income as a way of trying to differentiate some of the dispensaries
and that was helpful.
So I'm using that as kind of my basis for picking, in my project I said I'm picking
three dispensaries, but I'll probably actually look at more dispensaries and look at the
association patterns of more than just three.
But that analysis kind of helps.
So thanks for that Keegan, just kind of recommending to go down that path and look at those.
So yeah, so that's kind of where I am right now.
Like I said, I hope to have something to share next week, some rudimentary stuff, but Keegan,
I know that we're meeting Friday here in Detroit.
So but I may have something to show you on Friday as well, but we'll see.
I do need to bring you up a little bit to speed on some of the more of the details of
the project, but I don't want to.
So that's cool.
And yeah, just so everyone knows, we're attending CannaCon in Detroit on Friday, which should
be big and it's exciting.
And so I'll get to learn a lot about the Michigan market, which I don't know much about.
And then basically I just started to think about, okay, so what makes the dispensaries
look different?
Right.
And so I'm sure you've seen them.
So there's, you know, sometimes you have the classier, the nicer looking dispensaries,
then they'll maybe be like in a prime real estate.
Like so for example, in Portland, like in like, I'm sure in downtown Portland, there's
like some pretty nice dispensaries.
No, man, they keep all that stuff out of downtown.
Yes.
And so anything that's anything that can be perceived as seedy, they move out to the neighborhoods.
But yeah, there are, well, there's sort of like the bigger chains now, right?
There are places, you know, that have like four or five, I think there's some places
that have like 10 locations, right?
And they have nicer stores, and then they're like, you know, the smaller, sort of like
mom and pop places where they only have like one location.
And so yeah, they're, the chains are kind of like in nicer neighborhoods and the stores
are really nice and clean looking.
And yeah, I don't know about, yeah, I don't really know that there's a big difference
in pricing.
What about just product, like the products they stock?
Like, they all stock the same products or does it store the chains?
I don't know.
I'm not, you know, like a store of these things.
And that's why we'll have to look at the data, right?
Because it would just be anecdotal evidence.
So we'll have to kind of parse this out.
But I know they're like, they're billboards around town, you know, like one chain, you
know, like they're the exclusive carrier of, you know, Tommy Chong brand, and he's the
exclusive carrier of Snoop Dogg's brand.
It's interesting that you're talking about this, Charles, you remember I-75 here in Detroit.
That's the main north-south highway that leads, that passes right by Detroit and goes north.
Well, I had to drive down I-75 yesterday to get to the airport to pick up my mom and I
had to go through Detroit.
And to your point about these billboards, almost all the billboards on I-75 are all
cannabis billboards now.
And it's been about four or five months since I've been down that highway.
It is amazing how much it's changed, the billboards in that period of time.
It's either cannabis dispensary billboards or lawyer billboards.
So to your point, yeah, about just the advertising and everything, I noticed it as well here.
I think that that could be an interesting study.
I don't know how you'd get the data, but yeah, because people are always trying to find out,
does advertising pay off?
Like what's the return on investment for advertising?
If you could think of a way to measure that, that'd be an incredibly interesting question.
Yeah.
I did have a chart and Keegan, I sent this chart to you via email last week, but I've
added some additional information to it or pointed out some different information within
this chart.
And maybe later on in the conversation, we can talk about it as a group, because I would
just be curious to what people thought about the data that I had in this chart.
It's essentially median income versus the 2020 dispensary annual sales.
And you see some interesting relationships that emerge out of that chart.
But anyway, it's something that if anybody's interested in looking at, maybe we can talk
about it.
You can share your screen if you want, and we can talk about the chart real quick.
Okay.
Let me share a window here one second.
Let me know if you can see this.
Yes.
Okay.
All right.
So on the bottom axis here, the X axis, we've got median income by zip code.
So this is an anomaly here, but basically median income and increases and you get something
median income up to over $140,000 up here.
And on the left-hand side, you see annual sales from dispensaries.
So some of the top selling dispensaries are up here around, was it 14 million?
Am I reading that correctly?
Yeah.
$14 million annual 2020 sales.
So this is per your suggestion, Keegan, and thank you again for recommending I look at
this.
So what we see is that the really high selling dispensaries tend to, well, we've got a smattering
of them.
It goes down a little bit here, but the main high selling dispensaries here kind of fall
in the middle of the median income curve or maybe not the middle, but kind of lower section
here.
Well, I get a representation of the higher selling dispensaries across the median incomes
by zip code.
So for my project, I'm going to pick dispensaries that are in this oval so I can get a good representation
across median income.
But what I think is interesting is some of these other areas.
So this big block here on the lower left-hand side, that's where the vast majority of the
operators are, right, the vast majority of the dispensaries operate in this lower median
income area and they have lower sales.
All right.
So this may be the mom and pop quadrant or whatever we call it.
Here we have lower annual sales, but in higher median income areas.
So also mom and pop, but in more wealthier areas.
And then you get the ones that are just more successful regardless of median income and
they just sell more, right?
They kind of bubble up here and you got this kind of transitional area where I've got the
little triangle and these are folks that are just seem to be making their way up the annual
sales range.
And then you get a few outliers.
So on the lower right-hand corner here, you see these guys are again, probably mom and
pop shops, but in high income areas.
What I thought was really interesting, and I don't know if you can see this and probably
I'm obscuring it a little bit, but in median income, you kind of get this division right
going up here.
So you see high income and lower income, and it seems to be like this kind of somewhat
of a gap in between.
I just thought it means kind of an artifact of the data, but I thought that was kind of
interesting that median income, I mean, I'm not saying haves and have nots.
It's not that differentiating, but it's kind of-
Curious.
Yeah, it is curious.
It just seems like that's just the way that the cookie crumbled here.
We see two main groupings of median income.
But yeah, it's just interesting to go through this exercise.
What I would like to see over time would be interesting is how much of these dispensaries
as they mature move upwards.
Maybe they move upwards, or how many of these dots disappear as they're going to be consolidation.
And then the consolidation, I'm sure, would probably push some of these dispensaries to
be more profitable as they get bought by other companies maybe.
I don't know, just kind of thinking out loud.
It's a fun exercise to do this.
Obviously, it brings up more questions than answers.
I think these are helpful groupings.
Like how you identified the big cluster at the bottom.
I think you're right where those are people who are basically, they can get property where
they can find it.
Maybe they don't have the best location.
They may not be paying that much for rent, and they have low sales.
Looks like similar people in those same neighborhoods, like you said, are breaking out.
And so that could even be, those may even be chains in the lower income neighborhoods
where they've just got, maybe they were able to get a slightly better location.
Sure.
This right here, if you, okay, so see my oval?
So the actual dispensaries that are in that oval are the records that are in this kind
of yellow color.
So the top grossing dispensary was this one in Wenatchee, if I'm saying that properly.
Crafts they're called craft cannabis in Wenatchee.
Then you've got Main Street, marijuana in Vancouver.
But you can see these kind of top tier, when I say top tier, they're highest producing
across the median scale.
But here, what I noticed right off the bat is, so look, you've got Main Street, marijuana,
and then you've got Main Street, marijuana East.
So they've been around a long time.
They were one of the first dispensaries in the area, because Vancouver is just across
the Columbia River from Portland.
Okay, so they're well established.
Yeah.
And yeah, that's...
I would almost add a classifier to those as like zero or one, and I would give them a
one for chain.
And so it's going to be tough to identify these, but if you have a clever way, maybe
if they each include their names, because you can kind of identify these as like the
brand is Main Street, marijuana, and they've got two locations.
Yeah, that would be a good differentiator between those.
Yeah, it would just really be cool to monitor this over time, just to see how the market
is starting to mature or develop.
And then the question is, why do they break away?
So I met somebody from Zip's Cannabis, and they're a pretty big, well established dispensary
in Tacoma.
And it could just be...
One of those things is once you start getting your market dominance, then you're able to
grow, and it's hard for other people to get a foothold.
Everybody knows you, you've got a customer base.
So what is it?
Do they just have a really well-placed location?
That's a possibility.
Yeah.
And you were mentioning also about doing some regression in this to kind of tease out some
of those drivers behind this.
That's not part of my graduate project, but it would be fun just to do that, just to kind
of start teasing out some of these variables.
And this kind of work, to know what the drivers behind market growth and development...
I mean, this is stuff that you're probably familiar with, Keegan, but that kind of information
is going to be very useful for market consolidators, these kind of people with big pockets that
are going to go into this world and look to pull the market together.
I'm sure that they would be very interested in that kind of information.
Exactly.
It's just how do you measure it?
And so...
Yeah.
And this is sort of a wild idea, but...
One point I listened to an economic seminar and they were talking...
It was a health one.
They were talking about, oh, it's not good for your health to live through a certain
proximity to a road.
And I think they were somehow measuring the width of the roads, whether that was lanes
or highways or something.
So I wonder if there's any way to sort of give in the location of the dispensary to
know what number of lanes road is that on?
Is it on a two-lane road?
Is that on a four-lane road?
Because basically what I'm trying to parse out is I've got a sneaking suspicion that
some of these are just well-placed and then that gives them the opportunity for growth.
So they're just...
For example, the Green Lady in Olympia, they've got Green Lady East and Green Lady West.
They're both almost outside of city limits because I don't know if you're really allowed
to have something in like downtown Olympia.
And so they basically just have one on each side of Olympia.
So they literally have the Olympia Market cornered.
Yeah, no, that makes a lot of sense.
And I'm sure that lots of retail companies have probably gone down this path many, many
times with this kind of analysis.
I mean, how do you choose the right location for a store?
I'm sure that that's probably well-documented approach to doing this kind of stuff.
It might be just worthwhile to see what already exists in literature to see what we could
snag and use for that kind of analysis.
A lot of this space, I mean, the work that you're doing in the lab world is probably
much more nuanced and unique, but this retail stuff has been done many times over just in
different markets.
Well, it's just a hot area of interest.
So at one point, someone who does a bunch of data analytics told me some like four big
areas and one of the biggest areas is the consumer.
So what's driving the consumer?
A lot of people are just guessing, but like I said, people are starting to uncover it,
but I think it's all interrelated.
So my current job here at General Motors, I'm starting a new position in July 15th and
I'm going over to the customer experience team.
So I'm knowing I'm going to be doing a lot of work in this space as far as customer analytics
and consumer behavior and that type of thing.
So what I'm learning there, I'm sure it could be applicable or what I'll be doing there,
some of it can be applicable to this space as well.
So yeah, should be interesting.
So one thing that I've noticed, I think a lot of these places, they're places that compete
on price, right?
Because when you're driving down, when you're driving to the grocery store, every other
block there's a sign spinner, $3 grams, $1 grams.
They're more sign spinners than billboards.
So there's that.
And then I also, I talked to someone and they were saying a big driver now is like the higher
THC, which is probably not the low price stuff.
I don't know.
Again, this is not something I study a whole lot, but I think that's kind of like there's
two sort of markets.
There's the people who are price sensitive and there are the people that want their thunderbird
of marijuana.
That's a great analogy.
Yeah.
I mean, to that point in this oval here, I think because of the median income distribution,
I think it's going to be interesting to see what kind of association rules we get with,
let's say this point here and this point over here, the difference kind of product associations
that might come out of this, right?
Are they more of the upscale market that you're talking about or is this more of the thunderbird
market over here?
I don't know, but yeah, we'll definitely get a chance to look at it.
Hey, I had a question on your data.
So just from the basics, are unemployment benefits included as a source of income in
this data?
I don't know.
This is a good question.
This median income data came from the government census website and I'm not exactly sure how
they calculated the median income, but that's a good question and I'm glad you asked it
because I should have a sense of how that data was developed.
It's not common sense because I mean, I browse Reddit a lot.
So just speaking to your point about THC, I mean, unemployment and even just Medicaid
benefits are something that can just allow somebody to still use cannabis as a method
of treatment for their pain or whatever, but high percent THC, at least in the state that
I live is not necessarily the most expensively priced of cannabis flowers that is.
But yeah, anyway, so unemployment can make that happen.
I just don't know if 40,000 is even achievable on unemployment benefits in that area, but
it could be almost achievable here because of our special circumstances.
Yeah, that's interesting.
Heather you're in Colorado, right?
I'm in Maryland.
Oh my gosh, I was not even close.
I thought for some reason you're in Colorado, so you're in Maryland.
Okay.
That's okay.
Yeah, I mean, I was so percent percent wise.
I mean, I can get like usually the higher percentage THC I can get from a particular
carrier, and that's usually the cheapest, believe it or not.
But yeah, we have way overpriced stuff for no reason, like 18%, 22% THC, come on, $65
for an eighth.
Yeah, you're going to get the hustling, right?
That's just going to be people trying to maximize their profits.
That's going to happen regardless.
That's interesting to hear about the price points though in Maryland.
I'll look to see if there's any Maryland data because that's just, I mean, it's anecdotal,
but it sounds like quite different than what's going on in Washington or Oregon or California.
So yeah.
So good observation.
That's why it's important to have these types of meetings because especially in this market,
right?
In different states and how it's evolving differently, it really is interesting to get
those perspectives.
Well, it's wild because I mean, I mean, what other good is, well, I'm sure there's others,
but you know, there's like different state markets and there's some overlap, like so
maybe like an equipment producer in Colorado will sell to people all over the country.
Right? There's a lot of, you know, little medium sized businesses all over and the different
regulations obviously have an effect on the market outcome.
So it's, I'm interested to see, okay, how many suppliers, how many producers are there
in Maryland?
Because that could be an interesting data point is, so that's something, that's a reason
that a lot of people were attracted to Oklahoma.
It was the, it's the highest number of cultivators per capita.
Wow.
So all like the equipment producers, like all the equipment manufacturers, we're all
interested in serving the Oklahoma market because you've got some, I think there's 7,000
licensees.
I forget if there's a role of cultivation, but there's thousands of cultivation licenses.
So they all need soil, fertilizer, pots.
So accountants, you know, you name it.
So they, so there's a lot of demand for services.
Yeah.
And that's, I was going to say, that's hard to believe.
I mean, I feel like either Southern Oregon or Northern California must have the most
producers.
Well, what my hypothesis is, is, let's take California, for example, I bet you it's hard
to get a foothold there because you're going to be competing with people who are well established
and may have incredibly large operations.
So and they're probably competitive.
So you know, it's going to be tough to set up shop thing, but other places, you know,
it may be easier to enter.
And then what I'm also interested to see is, okay, like what's the persistent rate?
So you know, just because there's a lot of licenses now in Oklahoma, it may balance out
in a couple of years.
So you know, it may reach an equilibrium.
So we'll see.
Heather in Maryland, what's the status?
Is it just medical marijuana that's legal or is it?
Correct.
Yeah.
And that's completely political as far as I can see.
That is so we have these, you know, we have a number of licenses that can be distributed
and some of the those with the heavier pockets are still doing, you know, trying to enforce
higher prices.
And it's just silly.
But anyway, so yeah, medical only.
I'm a medical card holder.
So people just the generals.
I mean, I don't want to say general, but I would say the majority view, at least on Maryland
Reddit or cannabis, is that they just they want legalization.
So if they don't have college park cops tasing people just because they have cannabis on
the word they vape, you know, that still is happening.
But looking from the business standpoint, they don't want to disturb anything because
their favorite dispensary is doing fine or their their host of strains that they use
is is consistent and they just don't want to rock the boat.
So it's a it's a double edged sword over here.
So I don't think legalization is going to happen, even though we had Virginia.
Can you imagine Virginia legal like they're looking to I thought that that was like on
like happening right now.
I'm like, OK, so we're never going to do that.
Never.
I got a feeling that maybe as people from Maryland might start crossing state lines
to go get better deals, it'll it'll even out eventually.
I don't think they can hold on to that control for as long as they would like.
Yeah.
I mean, I wish you know, but go ahead, please.
It just shows you the market forces, right?
Because, you know, like you said, people just go to Virginia, then, you know, Maryland's
going to realize they're missing out on some tax revenue and then they may change some
things at that point.
Yeah.
Well, incredibly interesting.
Well, should we take 10 minutes or so and just look at the laboratory information management
system so far?
Be great.
I'm excited.
All right.
So just keep in mind that we're still working on a bit of the functionality.
But let me get this spun up real quick and then I'll just give it a quick demo.
Okay.
Thanks.
I'll just go ahead and share my screen and we'll just get this spun up.
So, it's signed into a mock account real quick.
Create an account.
Her graphics are very professional.
First things first, we'll just get this account just set up with an organization real quick.
So, everybody can just use it for free and then if they need integration with the state traceability system, then we can help them out with that.
Really, I'll just sort of run you through the main data points here that the laboratory collects.
So, everything really evolves around your analyses.
So, this is where you're going to, you're basically just going to do, you're essentially going to have your cannabinoid analysis and you'll add analytes.
So, analytes are the compounds that you test in your analysis.
So, for example, you know, you may have, you know, PHC.
You know, there's not going to be a limit to that.
So, the LOD is the lowest order of detection that the lab can actually detect.
So, for example, it may just be, you know, 0.01%.
But it's just going to be the anything that they measure that's lower than 0.01%.
They're going to report as non-detected.
Because the way, you know, science works is, you know, you're always trying to, you know, disprove a negative.
So, you know, you can, you know, you can prove that there's something there, but, you know, you can never disprove a negative.
So, it's always got to be, you know, non-detected.
So, you can't, you know, firmly, you can't, you know, 100, you can't 100% say that there's no THC whatsoever, but you didn't, you didn't detect them.
So, LOQ is the lowest order of quantification.
So, this is what they can actually quantify.
So, this you would expect to be slightly above your LOD.
So, anything above the LOQ, they can report to the client.
So, they can just put that on their certificate.
And that's the official measurement.
If it's between the LOQ and the LOD, it's technically an estimate.
So, they have detected something, but they can't quantify it.
So, different laboratories will approach this differently.
Some will report it as a non-detect.
Others will report it as an estimate.
Here in Oklahoma, I believe they actually have it regulated that they have to put it on their certificate as less than the LOQ.
The analytes.
And then just a couple of formalities.
You know, you'll have areas where you keep things in your contacts to keep track of your clients that you test for.
Then, of course, you need scientific instruments.
And so, these are what are going to be doing the analysis.
And so, what's critical is they'll be spitting out data to a certain place in your network.
So, typically, you'll specify your data path.
So, it'll typically be a path to your instrument's data.
And then this will be collected intermittently to stock up your measurements.
So, you know, there's just a few things here.
For example, people will need to keep the instrument maintained and calibrated.
Instrument is technically a piece of inventory.
But, you know, of course, labs have many pieces of inventory.
So, we need to keep those well managed.
But back to the instruments.
So, you've got your instruments performing your analyses and your instruments generate measurements.
So, your measurements will be for a specific sample.
And they'll be for a specific analyte.
So, this will be your measurement for THC.
And what's going to happen is the instrument will measure your THC.
It may measure it in 600 parts per million.
You've weighed out, you know, let's say half a gram of cannabis.
And you've now diluted it by a certain factor.
The standard method is a dilution factor of 10.
So, you've now done your analyst measurement.
And you've recorded your instrument measurement.
You now apply the analyte's formula to get your final results.
So, your results will be your, you know, I mean, you need to add a label here.
But, you know, your results will be your 20% for your THC.
Or, you know, maybe your 0.3% if you're growing hemp.
You've got your results.
And then these are all for your samples.
And so, basically, the final piece that's needed is to generate a certificate for these results.
So, that's the final piece of business logic that I need to finish.
And so, basically, what you'll do when you, you know, click, you know, create certificate is it will then get the sample data points.
So, that will get, you know, your sample name, your sample ID.
It will get the contact information, so that way you can put your contact's address, city, and information on their certificate.
And then it may even grab some project variables.
So, a project is a group of samples sent in at a specific time by the client.
And so, then this may have a transfer ID.
So, for example, in Oklahoma, all samples need to be recorded.
So, whenever a client sends you a transfer, there will be transfer data.
And so, this will just be the organization that it's coming from, as well as information about the transporter.
So, these are all the data points that the laboratory works with.
And so, I've basically just put together a simple, what they call a CRUD in the software world, which is create, read, update, and delete.
So, now the, you know, lab could, you know, create, read, update, and delete data for all of these data points.
So, it's not a complete lens.
Like I said, there's still a lot of business logic that needs to be written.
For example, the next thing that really needs to be tackled is a COA generation mechanism.
But we're, you know, well on our way to having something functional that labs can use.
And, you know, there's traceability support.
So, that way, if a client's integrated with metric, they can see their packages, their lab tests, their locations, transfers and settings.
I've written the logic for, say, processors and producers and potentially retailers to also use the platform, primarily just to send samples in for testing.
That functionality got put on the back burner because at the moment I'm just trying to finish everything needed for the, you know, for the labs.
That's nice work.
Oh, thanks Paul.
Just kind of putting it together bit by bit.
So, you know, it's that way. It's primarily targeted towards laboratories that are operating in states that are using metric.
Ideally, do you have a question?
Looking at from the perspective of an outsider going through this, and I'm sure you guys have probably already thought about this, but, you know, there's obviously
quite a lot of effort that lab techs will have to do to, you know, make the data entry stuff in here.
Have you thought about kind of like creating menus that are pre-populated templates they can use over and over again?
Excellent, excellent question. And good suggestion.
I have thought about doing essentially what we call worksheets. So having exactly templates or worksheets where, say, you know, a client or say an analyst creates a project.
You know, ideally, like you said, some of these things can be pre-populated and perhaps you could even, you know, print off a form, whether that's you actually printed off or whether it's just a digital form.
So that way, that way, yeah, analysts can use worksheets because a lot of laboratories are already familiar with using worksheets. A lot of analysts are familiar with using worksheets.
So it is a worthwhile mechanism to collect data. And so I think it is worthwhile to include some sort of worksheet creation, some sort of worksheet generation tool that can also, you know, feed data back into the system.
That's really cool. Good work, guys.
And I want to give a shout out to Charles, who's really done the heavy lifting to actually write the routines that parse the instrument data.
So when you're building out a project like this, there's what I like to, I consider it three different aspects. So in the software world, they might call it model view controller, whatever you want to call it.
Basically, I call it there's your user interface. Then you've got your business logic and then you need something to glue them together. And then I call that the API.
So the user interface is coming together. Like I said, there still needs to be some, you know, interesting features here to create certificates.
That shouldn't be too hard to do.
And so the business logic is a lot of it is getting written. So here, analytics is a Python module.
And there's a LIMS module in there.
And there, Charles has begun to write the business logic for collecting results from instruments.
So for example, we're collecting results from Agilent BCs to do procedural solvents and terpenes. And Charles is written routine to import heavy metals.
And so just to show you what we're actually parsing here.
So let's take a look at some of the test data.
This is sort of a typical data file that would be spit. We're not spit. This is a typical data file that would be generated by an Agilent instrument.
So, or maybe for legacy sake, things don't change much. So between the different years and different versions, this is generally what you would expect.
And, you know, that's not a bad thing. So maybe they did the design their data files that way.
So that way, you know, scientists can continue to collect these with the data collection routines that they may have written a long time ago. And in the laboratory, it's important to not break things that are working, especially if they're being used in production to do analysis.
So long story short, data files have not changed much over the years. And so sheet one, you generally just have some of the sample identifiers.
This is where you really get your sample name. And then you have many tabs here that are related to, you know, your actual instrument.
I'm just curious. I mean, how many instruments and their associated data files do you think you'll have to accommodate?
Well, we're trying to do six off of the bat. So that would let me get this right. So that would be residual solvents, herpes, cannabinoids, pesticides, heavy metals, and micro toxins.
So you can screen for micro toxins through the LCMS. The pesticides are also through the LCMS.
So those data collection routines are similar, but they have different compounds. And so I'll show you the similarity here. So this is a data file for residual solvents.
So if you look here on the compound sheet, you'll see the name. And I just put in fictitious amounts.
But these names are your analytes, so propane.
If you look at the propane data,
you'll see that the workbook is quite similar. In fact, it's laid out the exact same, except on the compound sheet, we now have different compounds.
So we now have terpenes. So when we import these in, importing in terpenes and residual solvents, the logic is the same.
We just have to control for the analyte names. So we just have to match the analyte names up appropriately.
The data files aren't quite the same. So as Charles found out, the heavy metals looks quite different than the rest.
And then the pesticides and micro toxins will also look different than the rest.
And we are tentatively going to add a seventh, which would be importing microbials.
So that would be when you screen for microbial contaminants, you do it through QPCR and QPCR can generate a data file.
I've never parsed one of these before, but
centrally, it's just an Excel file. And we're basically going to need to read this in, apply a good bit of logic, especially to the micro, and then you get this data stored.
So to answer your question, we're starting off with a handful and we're sort of going to add more as laboratories demand them.
But we figured we could start with the high demand instruments. And so I would like to add some Shemazdu instruments.
I've met a couple of contacts from Shemazdu at CannaCon, so I'm trying to work that out.
We've got some Agilent examples. So depending on the model of someone's Agilent HPLC or Agilent GC,
they can import their data thanks to Charles's diligent work writing the data collection functions.
Now is really just time for really the glue plus a little bit of automation.
So really, we've got the user interface finished, the business logic. Charles has finished a good chunk.
I still need to write a little bit to create certificates, but that's on the agenda.
And then we'll essentially leverage an API. And so all this API does is facilitates use of the business logic,
either through the user interface or potentially programmatically if somebody wanted to make requests to the Canlytics API.
So I learned a principle, sort of the API design. And so really, I just broke this whole project up into,
OK, what are the data models that people need to keep track of?
And so then each data model is essentially an API endpoint. And I don't think this one's implemented yet.
But basically, it's just going to allow people to get, update or create and delete data.
And so you can now interface with, well, once I finish the API, you'll be able to interface with all of these data models.
Each one will have a handful of, more than a handful of data points.
And then we can also leverage all of the business logic.
And so it's almost a fully functional lens at that point. Well, I don't think it's fully functional at that point.
But then the cherry on top is you can add a little bit of automation. So that way you can,
say you can write a script that will use the Canlytics module, collect data from your instrument and upload the data through the API.
And then you can look at it in your user interface. So it's got all the pieces to be an incredible system.
Just needs a couple finishing touches. And so this coming week, I will be touching up the API.
And hopefully it'll be fully functional by the next time we speak.
So last week, we promised to get the instrument parsing done. That was done.
Thanks, most solely to Charles, because I've been, like I said, I've been just trying to get this user interface up.
I just can't thank Charles enough because now we can read in the data.
And next week we can get this thing generating certificates.
That's pretty cool. So this would be offered as a web service?
Exactly. So I originally was building it out as a desktop app.
And then I realized that really the only desktop functionality you need is to collect data from the instruments.
So eventually I figured, okay, why not just make it into like a web app and then just have a small little either script.
I'm going to start off with a script, but I may like to have just almost a tiny little standalone desktop app.
But all it does is collect results from your scientific instruments.
Sure, that could be a good offering.
So one of the things that hurts me while you're talking through this is if it's a web service.
So the angle of privacy, right?
So different labs that buy your product, they're probably going to want insurances for privacy and their own data and that sort of thing.
So I imagine you've probably done a little bit thinking about that.
But I know at General Motors with the OnStar app where we track vehicle telemetry, the customer has to opt in for that.
And all the data we collect is anonymous, right?
We can't tell who owns the vehicle or any of that kind of stuff.
So we respect the other privacy.
But at the same time, we get to aggregate that data and use it for other things to help customers out.
So, you know, just have to think about for this service, right?
I mean, there's going to be tons of value in aggregated data behind the scenes, but it probably would have to be anonymized in some way.
You hit on an interesting point here is so, you know, obviously labs.
They want to keep everything private.
And so, of course, you know, we respect everybody's data with our privacy policy.
However, if a lab wants to set up their very own analytics implementation so that way they can leverage all of the functionality of analytics and they have oversight over their own database,
then essentially, I know analytics is open source and I've written quite detailed instructions here on how you can get analytics up and running.
Essentially in looking for the you can get analytics up and running in a Docker container.
So anywhere you can run a Docker container, you can run can live.
So I'm running it just in the cloud, just to offer it as a web service to anyone in the world.
And more is, you know, so this is a kind of platform and it's almost an example of yes, this can be done.
We're doing it. You can use ours if you would like.
And if labs have the know how and a lot do because a lot do have technical staff, a lot are building out their own limbs.
So we've provided this so that labs can spin this up and then they can serve it from their they could either serve it from their own cloud instance.
They could serve it from an on site server.
You know, you name it.
It can be done. And so basically we were providing something, you know, something for everybody. So, you know, the labs that want to do it themselves.
We're providing the infrastructure for them to do that.
And in the guide, if they need a little support, we can provide some support.
Very cool. And then exactly.
And then for people who need just the cookie cutter solution, they just need something that works out of the box.
Then we're, you know, hosting our own implementation at console dot analytics dot com.
That way. You don't have to have technical staff to get off the ground.
You can get started in five minutes. You can collect all your data.
Then you can either import your data or say you're ready to go. You're ready to start your own implementation.
You can export all of your data, import it into your new implementation, and then you're off to the races.
So that's great. So you can onboard smaller outfits and as they grow, you can accommodate their growth.
Exactly. And so we're sort of leaving, you know, the door open for some some new additions.
So, for example, I have planned but have not yet implemented in invoicing, you know, an invoicing endpoint.
That way you can keep track of your invoices or your projects and, you know, your samples.
That way you can send invoices to your clients.
I thought that one was not mission critical.
So I pulled that one through the time being, but that one can easily be added in into the future.
And so as analytics starts working with more and more laboratories, hopefully we can find out what each laboratory's unique needs are.
So that way we can start improving the platform, adding more and more features, adding more and more data points and making testing as simple and easy as possible.
Definitely. You can see you guys put a lot of thought into it. It's pretty neat.
Do you have any what they call mean viable product, right?
Do you have anybody that's kind of testing this for you yet or getting feedback on it?
Yes. So we're working with a laboratory here in Tulsa, essentially just doing early stage feedback, just finding out, OK, you know, what data points do you need?
What's your ideal workflow?
So exactly. So we're trying to exactly get the minimal viable product out.
And really the last step. Well, there's two last steps that are needed.
One, Charles has written the logic to.
To parse the instrument data. Now I just need to write a script to automatically upload those measurements so that way, you know, that way people don't have to sit here and tediously type in measurements.
Those are just going to be flowing automatically from the instrument.
And then the other final piece is actually generating the certificate.
So.
These the certificate of analysis.
Is the product that laboratories are selling essentially they are they're in the service industry, right?
They're providing the analysis service. How do you know the analysis was done?
So we've got a certificate of analysis, though.
Ultimately, the certificate is what signifies that everything's been done and the certificate needs all of the data points on there and it needs to be generated in a simple and easy way and delivered to the client.
We've approached this as we just need to collect all of the data points as simply and easily as possible.
So now I'll in the coming week be writing a COA generation routine and I'll put this in the analytics module.
And basically what that's going to do is it's just going to take a template, whether that's a word in Excel.
Probably start with those two. So just in a word or an Excel template and use like Jenga style. So Jenga would just say, oh, you know.
Project ID goes in between like two squiggly lines, two curly brackets.
So we'll do some Jenga style templates.
And then actually generate a certificate. So that would be your PDF saying that the analysis was done.
So that is the final step and to generate to do our, you know, our minimal viable product.
Because so once you can take all of the data points, you can collect your measurements and then you can generate your certificate.
And then you've done the laboratory test from start to finish. You've received your transfer, created your project, created your samples, added your analysis, tested for all the analytes, collected the measurements.
And then you issue your COA and then that will just be essentially a URL where your client, it'll be a private secure URL that only they will know.
And so then they can get their certificate and that's what they're looking for. So that's what the producers and processors need because they need that certificate to be able to wholesale their products to the retailers.
So that's what it's all about is generating the certificate. So we're almost there. And like I said, I would have loved to have this finished today, but Rome wasn't built in a day.
So we're going to have to keep tinkering on this through this next week.
It always takes longer than you think.
Well, congratulations. I don't know who else you've shown this to, but it's really, really cool.
And even from somebody who doesn't really know about the lab world, just looking at your navigation bar on the left hand side and where you just kind of walk through that, it's almost self documenting what it does.
I mean, I don't know what an analyte is, but I can see the process. Right.
So that's that's cool from a user's perspective. Of course, these are going to be lab people. They're going to know some of these things already.
But it's just neat that it's kind of all the self documenting the process.
Exactly. Because I mean, that's the whole philosophy here at CanLytics is we think that cannabis testing should be simple and easy.
It just there's no need for it to be complicated. It's just you're just doing analyses.
You know, you're going to have to take some measurements and record these.
But, you know, it doesn't have to be complicated.
So, yeah, so, you know, so we put in a lot of thought, thought about, OK, what are all the data models you need?
What are the key data points? And we've put together something that we hope is simple for people to use.
But now we need to get people using it or in this laboratory more so that way we can just keep keep improving.
So, so it's come a long way. But hopefully we're providing value to laboratories.
So. But.
But let's go ahead and start wrapping up here, because I see that we've gone quite a bit over.
But thank you for your enthusiasm, because it's exciting.
It's been a long time coming. But I think we're finally close to a solution that laboratories will get an enormous amount of value out.
Yes, it's cool to see something.
You've talked about it briefly before. I know Charles has been working on you with it, but it's nice to see the idea take form and something that you can share.
And I know you guys must be pretty proud of that.
I have an appreciation for the amount of work that it took to pull that together.
So good job on that. Exactly.
And it's just one of those things we just keep keep working on it piece by piece.
And so, you know, we just have a clear vision and we just always know what the next step is.
And so, for example, this past week, it was parsing that data and Charles played an instrumental role.
Pun intended. And so.
It's coming together. So and we're just going to keep at it and keep trying to help people.
But I'm going to have to have to drop off here in a second.
But I started to cut you off there, Keegan, but I will reach out to you probably Thursday ish evening just to firm up things for Friday morning.
Yes. And I have to go back. Go ahead. Oh, no.
I bought the tickets to Canna con for me and my brother-in-law, who will be coming.
You'll meet him as well. And I think I only bought it.
Is there like there's there's like speakers, right?
There's the convention floor and then there's like these break off speaking events as well.
Yes. And so if you can, I would recommend getting the like they're used to call them seminars.
They're they're a bit extra. So they are the more expensive ticket.
But I would recommend that at least for Friday.
So typically like the seminars first thing on Friday are the best.
So OK, I do like I'll be out of state till probably about one thirty.
And then I'll have to leave at that point. But we're going to get there as early as we possibly can.
Oh, yes. And like I said, it's not it's not critical.
And in fact, I think they may record them and I'll have I'll have notes and may record if they let me.
So it's not critical. But I I just like to people usually present their latest research or ideas.
So I just I think it's a good way to keep your ear to the ground.
Yeah, absolutely. Cool. OK, well, I do I do have to drop.
So it's a good talk to everybody. We stayed way late today.
So thank everybody for coming. And I hope you learned a bit.
And next week we all touch base and maybe get back into the data since that's been neglected a little bit.
Right. Well, when you're in Detroit, now get some pizza, really good pizza, the best pizza.
Well, if Keegan can break away at lunchtime, my brother and I, we're going to take him somewhere good to eat.
So maybe I'll end up being a Chicago, I mean, Chicago style slap in the face, Detroit style pizza.
Yeah, I mean, West Coast pizza is horrible. You really haven't had pizza until you've had pizza in Detroit.
Well, I think it'll be worth a worth having fun with some pizza.
So all right. But well, it was awesome.
And then until next week, stay productive, everybody.
OK, good talk to everybody. Take care. Thank you. Bye. Bye.