They're going to make a bunch of TensorFlow announcements.
You are absolutely correct.
So I thought that was kicking off yesterday.
I'd be happy to hear about it if you attended yesterday and then I'm okay with ending a
little early today.
I'm actually in the process of moving.
So I'll pack up and read it.
But Charles, did you attend the Google I.O. yesterday by any chance?
Yes, yeah I did.
You know they showed off their quantum computer lab and they're working on like this conversational
AI where you can converse with objects.
They have a conversation with a paper airplane and with the planet Pluto and stuff.
It was pretty cool.
It was interesting stuff.
I know what I'd ask a paper airplane.
And you said today they're doing stuff with TensorFlow.
I didn't realize Google was behind TensorFlow.
Have you used any machine learning libraries before or anything like that?
Yeah I use TensorFlow all the time.
So I know they made some Firebase announcements yesterday but after like three hours of conference
I can't remember exactly what they were.
They are doing some Firebase presentations today or tomorrow I think.
So I'll have to check in maybe after the fact.
But yeah I'd love to hear about some of the work you're doing with TensorFlow sometime
because it looks like there's good uses but I've never pinpointed down a particularly
good use.
But I haven't tried.
I did a project that located trees for like for a carbon office and for applications like
help to calculate carbon offsets.
And then I did one with the detected like fire extinguishers and AEDs and stairwells
for like for helping people find exits or emergency equipment during some sort of emergency
in high-rise buildings.
Oh that sounds really interesting.
Yeah that was a cool project.
Is that something that so I didn't know if you're are you more like freelance with your
projects or do you work for directly for a company?
I'm a freelancer.
That's cool.
So you get to see a lot of different cool things coming your way.
Yeah.
Let's see.
And then how about you Paul?
I heard from you a little bit this past week and so it sounds like you were making some
progress.
Yeah.
So thanks for that.
I appreciate kind of directing me a little bit there on where to look.
So I took about four different data sets.
I took about 30 or 40,000 records out of each of those.
And then I just threw with my subsets I just threw those into access joined the tables
together per the joins that you were talking about.
And Charles sorry just to fill you in.
So I was looking to do looking at some of the products to get familiar with the different
types of product categories.
Actually I could probably share my screen.
Let's see.
I don't know which I've not used this before.
So let me see.
I can share a tab, a window.
I'll try to share a window here.
I don't know if you can see this.
Let me know if you can see this.
Okay.
Yeah.
Okay.
So very simple.
I thought it would be much quicker if I just took my subsets into access.
So what I was doing was just tying the sales items to inventories and then inventory types
because what I'm interested in looking at is just to get familiar with the different
types of products so I could potentially do my market basket analysis on the different
sales products.
So I joined those together and I don't know if you guys use access much.
I don't use it very often but I just thought this would be a quick and easy way to do some
things.
Just exploratory type stuff.
So you can once you tie your tables together you can drag any of the fields down into this
screen on the bottom and then you can just essentially run the results and look at the
resulting data set.
And so here I've got I just pulled in sales item ID and name and then here I've got the
intermediate product type I guess and then I've got the actual inventory name.
So one of the things that I've noticed on here at least for the work I wouldn't mind
doing is intermediate type is obviously very high level and then you've got these kind
of inventory names but I think these inventory names could actually be subgrouped.
So if I have something called CBD capsules times 30 there might be in here CBD capsules
times 60 which really doesn't it may be too granular.
So maybe there's some other maybe just CBD capsules right.
So I think there might be some subsetting that I might be able to do that would make
my analysis a little more meaningful towards not so granular.
So as we were talking about last time with market basket analysis if you buy peanut butter
and jelly you might buy bread that's kind of the idea.
Well you don't necessarily want to know if you're buying Jif peanut butter or Skippy
peanut butter or you may that may be of interest to somebody but I think for what I want to
do that may be too granular so for CBD capsules times 30 and CBD capsules times 60 that's
probably too granular for what I'm looking at.
So I may end up subsetting these into different groups based on what I see here for part of
the analysis but so that's kind of what I was doing just as a quick and dirty way of
just to get familiar with what's there and I want to continue building out the tables
pulling them in taking a sample and then just pulling into and start creating this kind
of primary key map that I could give you guys and actually give you a whole copy of this
if you're interested in it but to develop this map of the tables because just having
this kind of map of visual representation in one place just gets people oriented to
what's there quickly and I know that you know I'm a visual person so if I see something
like this it's going to stick with them a lot longer if I try to look at a bunch of
different code samples and see where all the relationships are but another thing I was
going to ask both of you this question now with the work that you've done with this data
in the past have you considered maybe taking those large tables and upload it into like
something like Amazon web services or something like that to where you could actually do your
Python development work on your desktop but then tie into the data from a cloud provider.
Good question so I have made some of the lab results available through Firebase actually
like Charles was mentioning so that's basically Google's shot at you know the AWS so that's
Google's version of AWS essentially.
I see okay.
So I think it's possible for some of the smaller data sets and that would be awesome.
Some of the larger ones like sales I think it just may be too costly to put them in the
cloud so I've got a friend David Busby who runs a company OpenTHC and essentially we've
got a new member but essentially what he does is he just stores all the data on his computer
and just runs it as an SQL or SQL access.
Hello Ivan, welcome to the Cannabis Data Science Meetup Group.
I think you're muted Ivan.
Well Ivan we're talking about some leaf data sets and how to access it.
So essentially I can point you in the direction of it but David's you know tried to make some
of the data sort of accessible through SQL queries so mostly like aggregation queries
like totals by day.
Am I still with you guys or am I lagging?
Okay great.
Yeah I mean I thought of like putting it on like a local MySQL database or some other
open database locally but yeah all those cloud services are spendy and this is a lot of data.
It looks like me have lost Paul but I can't explain when he gets back.
What was the second part of his question?
There was the putting it on the cloud but what was the first part of his question do
you remember?
Wasn't it about like accessing it through like SQL or some database queries?
Well I don't know when Paul gets back we'll have to ask him but you know I like the work
he did there by just you know organizing the data in the access so that would be nice to
add the rest of the tables because as we all agree you know the first rule about data science
is you know look at the data.
Well I guess just real quick do you just want to see a like a dummy variable regression
I ran or what are some of the things you're working on Jarvis?
Usually I've been working on this Kaggle competition to classify radio signals as being like extraterrestrial
or just noise like you know like the example they use is like the Voyager like the signals
that they get from the Voyager and like how to detect that from just like space noise.
So I've been kind of spending the last week doing that I haven't really worked on cannabis
data science since last week but I will get back into it.
Although when did you say you were working on this past week again?
It's a Kaggle competition they have it's to classify radio signals as like and you try
and figure out if it's just like space noise or if it's like a if it's like a signal coming
from a space probe or from extraterrestrials or but they use like the Voyager you know
like they like the example they give you like give you example signals from Voyager and
what they look like against space noise and how to filter that signal out.
So do you know all about the spacing on the names but all you know that the tests for
white noise like the stationarity tests is that something that you know about or?
No I'm just kind of exploring it right now I'm just trying to learn stuff.
Well for time series forecasting like some of the forecasting I showed you the the Arema
and the VAR models if you're really doing that rigorously before you start forecasting
you essentially want to make sure your series is what they call stationary so it's essentially
white noise so there's no apparent trends or you know cyclical behavior or anything
like that.
So typically you know when you get the data set and you're going to forecast it you know
you'd often maybe take the natural log and maybe even the first difference to get it
into growth rates and the whole point of that is to take you know when you look at most
series right they're just this trend like so they can you know bias your forecasts so
what you do is you basically just difference it maybe even a couple times and that's why
it's called a ream a reema that's the eye the integration or the difference and so basically
what you're trying to do is you're trying to make the data look like white noise because
it's just a theoretical forecasting and one of the assumptions is that you have white
noise.
So long story short there's a whole bunch of statistical tests you can do to test if
the data is white noise and so that could be useful in your situation.
Thanks.
Hi guys sorry my wife dropped her calculator on her surge detector that turned off our
modem so or not a modem but you know our router so bizarre anyway sorry about that.
We lost you there for a second so but to bounce back to your question so I put some of the
lab results in firebase which is you know google's version of AWS just to sort of make
those accessible through an API I still need to do a bit more work there.
The reason I haven't really done it with sales or inventory is I just think that cost may
be exorbitant and so at OpenTHC what they've done is they basically just store it on like
a server and then they just do like SQL queries to just do aggregates to do daily totals or
your total sales by licensee by money so that's sort of the most accessible the data could
get honestly I think there's potential if you thought of a you know a cost-effective
way to store the data and to essentially serve it up through an API or some other means for
people to consume it.
I think there's a lot of people that would like that data because yes you can do the
public records request but that's you know a pain and I think that that's a barrier to
entry for a lot of people.
Yeah and I would think that I mean it sounds like you've already done your homework in this
area but I mean for the stuff that you want to do with your business right you are going
to have something to where you can append updated data to a centralized location right
on a regular basis I would guess and yeah I guess the key phrase there that you said
is cost-effective.
So with the OpenTHC project do they currently have all the data you were able to procure
is that loaded up into their database?
Yes essentially so actually David's the one who shared the data set with me so they have
it there it's a work in development so it may not always be like easily accessible so
to speak but David's done some work to you know make it so you can just get like a JSON
dump I'm not 100% sure if I'll have to after this call I'll double check with David and
actually share his contact information with you.
Oh thank you.
Because that's essentially what he's done is just put all this data on his server and
he's trying to make it accessible through an API.
Okay.
But like I said yeah the current state of things but.
Yeah I know I could like for these four tables that you walked me through I would you know
in order to do the complete market basket analysis I would have to be able to either
take all those tables and put them in a location through like you know Firebase or something
I may have to end up paying for it I don't know but I'll need to get that yeah I'll need
to get that data somewhere altogether that I could query subsets or just make general
queries against but it sounds like you know if David is agreeable to it then maybe I could
pull query data from his data set.
Yes so I'll get you in touch with him you may have to kind of what's the phrase I don't
know be the squeaky wheel or something but you know just to kind of you know you just
to tell him like okay you know because he's a his whole philosophy is you know not to
do a lot of development until there's like an actual need for it right so you don't want
to be.
Yeah oh yeah I wouldn't want to burden him with anything I again you know however I can
get the data for this project this school project in one place I guess that's the main
driver for me and if it was temporary I could set something up temporary and if you know
but again you know it comes down to the cost effectiveness of it I definitely can't afford
to stand the whole thing up for a bunch of people to bang against it but you know we'll
see.
Well yeah I'll get you in touch because like I said he's basically just serving it from
like his box at his office and I think he's just got it wired up essentially through an
API.
Yeah under the table kind of thing.
Well I think it's basically the type of thing where you basically just hit his URL with
a dot JSON and you'll get a huge data dump.
I'll get you in touch with him because that's how I got a hold of a lot of the data myself.
Okay well thank you for that.
Are you using Windows or Mac or Linux?
Windows.
Windows okay you know there's you could use WAMP which is Windows, Apache, MySQL, PHP
but you could run like a local WAMP server on your PC even you can even run it on like
a laptop and then have like an external drive and have MySQL database running and you know
and you know just you know get some cheap external drive and put it on there.
That's a good idea.
Yeah so do one large download of the data put on it an external drive and then you're
saying run WAMP against that external drive.
Yeah or I mean you could probably even use you could probably even write you could probably
put an access database on that external drive.
Yeah unfortunately there's a two gig limit on access.
Oh okay.
Yeah yeah yeah that's one of the things.
Yeah maybe WAMP.
Okay.
Yeah WAMP is free and then there's like a WAMP probe out you probably don't need that
so.
Okay I'll check that out that sounds like a good alternative too.
Yeah.
Yeah okay.
No thanks for yeah thanks for all the advice on that.
So I guess like what type of I'm just wondering like so what would like an example query be
just so I can start thinking about like how you how you need to approach the data.
Yeah I mean just this initial foray into the access database I mean really it's just a
very simple straightforward selects you know certain fields from and then joining on the
four tables on the primary keys so very straightforward simple query and that's really it.
I would I think I'm definitely going to have to like I say break down those or create some
subcategories of products and create a lookup table but really it's the query would be very
straightforward very simple of course it's going to bring back a lot of data but yeah
it's all very simple stuff.
Question I guess with the market ballot basket analysis like what's like the like the measure
so is it like the proportion of items?
We were talking about that last time let me see if I can find something real quick to
share with you guys online and maybe I can talk through it real quick.
Well I gotta run I'll see you guys week.
Charles definitely enjoy the IO conference tell me how what's new with tensorflow because
I think there's some good uses for it that I'm missing out on so I'd love to get filled
in by using it.
Okay cool all right see ya.
See ya Charles.
Hey Keith I'm just going to look something up and maybe I can run you through some of
the it's very actually very simple stuff and I'm but simple sometimes can be good so.
That's actually analytics is like that's our one of our philosophies is you know simple
is better than complex.
Absolutely maybe this will help I might have to jump around on a bunch of stuff but let
me share this with you.
Where are we here where is the meetup?
Sure this I'm going to bring this one over.
Okay so.
This you this looks fun.
Oh yeah I was hang gliding.
Yeah get my.
Did you get a good thrill for that for sure.
Yeah no I have an aviation background so I've always been interested in that kind of stuff.
Cool.
I just I just pulled this up is just so happened to come up here but um let's see what would
be a good way to just start off with this here maybe.
So with all the different combinations of products that you could have you have this
is the left hand side or right hand side or what's called an antecedent and a consequent.
And so when you have these antecedents and consequences you come up with these association
rules.
So here got ice cream and soda.
You can also have a right hand side rule that has more than just soda in it.
It could have several different things.
So you say what's the relationship between ice cream and this group of products.
Right so you get that relationship as well.
But the most simplistic measure is what you're talking about it's called support and it's
just proportionality.
So how many how of all the combinations you can have through an entire product set what
proportion of this rule this one at the top is it appears in the entire possible set of
combinations.
So here our support is point zero seven percent.
Okay.
And this is not unusual because there's so many different permutations of the association
rules.
So we get these small percentages but that's normal.
So here you can just say that support is like popularity.
So this ice cream and soda is a pretty popular combination.
I'm going to go over here to lift and then I forgot the calculation maybe it's in here
but there's another measurement called lift and lift is really you can think of as the
strength of association between these two elements in the rule.
It's the strength of association between ice cream and soda.
So really if you look at think of this as a two dimensional plot where you have support
on your y axis lift on your x axis what you want is a product that has high support in
other words very popular and the association between those two items in your rule set is
also very strong.
So you're looking for in a x y axis you're looking for those data points in the upper
right hand quadrant.
So they're they're popular and there's a strong association.
Those are the two main those are the two main metrics that I would be using.
And I forgot what the confidence is for and there's actually there's go ahead.
Can you see what lift was for again what's the capturing lift is like the strength of
association between the items in the rule set.
So ice cream and soda are strongly associated.
So you want a rule that is high in support which is very popular but has high lift as
well.
The association of the rules are very strong.
So you can count on that as a very popular strong rule and that's you know if you get
several of these different rules together you can start thinking about product placement
right.
Ice cream and soda.
So exactly so you could have high lift but low support so you could have someone just
a couple people get topicals and capsules together every time but there's only like
a small number of people that do that.
So that would be high lift but low support.
Yeah and what you find is you get a situation where in your X Y axis you in the upper right
hand quadrant is where you want their association to be.
But what typically happens is hardly anything is up in the right hand upper right hand quadrant
is almost always empty and you get kind of this trade off between support and lift.
And so there's a lot of human decision making that has to go into this.
You know obviously you have to bring a lot of sales context into it right.
Your subject matter expertise of the market and utilize this information based on let's
say your circumstances.
So let's say we shared this information with somebody you know it's running a store and
you share their metrics for their particular store and you can say look there's a lot of
customers that are buying these products together.
Why not change your arrangement in the store to where you're placing them side by side.
Or maybe you see a relationship that has very strong lift.
They're highly associated with each other but they're not very popular.
They don't have as much support but maybe if you take those two items and display them
next to each other you may actually influence their popularity.
So there's a lot of marketing kind of decisions that you could make out of this.
You could change the arrangement of a store layout.
You could actually have promotional coupons or whatever you know sales of the week or
month or all kinds of different ideas that you could implement within a shopping setting.
And that's that's and this has been around for a very long time and there's other variants
of this approach.
But I thought well gosh you know this is a new data set and there might be some opportunities.
I mean if you said you were talking about producing white papers and things like this
but from a analytics perspective maybe this is a service that you could offer to some
of your customers or maybe you know maybe there's some I don't know maybe there's some
sort of inventory type stuff you could do between the people that hold inventory and
the things that they're going to try to sell to different retail outlets.
I don't know there's lots of ways to think about this.
This is brilliant Paul and like I loved your idea because it's essentially you're taking
this you know this model this marketing technique analysis that's been around for a long time
and like you said probably I mean maybe either no one or very few people has actually done
this with Canvas.
Yeah yeah it's like most things in life right.
I mean here you are you know tell me about your story and your journey right which is
really cool.
I mean you've got a developer's background but you found yourself in a completely new
industry in some of the lab work right and so it's that combination of interesting intersection
points which provides opportunity.
I don't think I'm not a big believer in swinging for the fences and home runs.
I think those are freaks of nature for the most part but if you position yourself right
in the right kind of circumstances you can develop opportunities just by connecting disparate
dots and it's kind of like where you are right so and I thought well gosh you know this is
a again very standard old technique but hey let's apply it in a different environment.
It's a good idea and I like your idea about putting it out there for people to use so
what came to mind was essentially almost like a like an online calculator where I don't
know maybe you know the company could put in their license or what they're trying to
look at but and then they could do this market basket analysis for that licensee.
Exactly yeah they could do for the licensee.
They could have this online calculator that that may make suggestions for cross promotional
products.
There could be all sorts of different services that you could offer electronically and sure
is better to develop things and dealing with electronic products like this as opposed to
tangible ones right.
There's so much less oh there's a lot less overhead in developing a business that way
if you just deal with the information so yeah that could be some services this could act
as a service to for folks out there right cross promotional couponing or whatever but
yeah so that's that's the intent of this thing again it's just a matter of me being able
to get all the data that I need that's accessible that's going to probably be the most work
I would think because after I've got that the actual algorithm is pretty simple to run
and then we can generate a lot of different kinds of charts and that's when it starts
getting fun when you can just explore right.
Like if let's say you wanted to do a regional market basket analysis right maybe there's
certain parts of the state that I don't know they might have certain products that caters
their demographic I don't know.
I think that could that could be interesting because that is actually something I just
heard word of mouth was like I think the company was talking about like in Illinois or something
how in different towns there they actually sell different products so.
Okay yeah that seems to make sense right I mean you definitely have regional products
in anything yeah so but you know it's great that you're doing this because having having
as you well know having the data centralized in an accessible way for people to get to
is is key to everything right and if you can in a systematic way you know it sounds like
that's what kind of the open THC project is trying to do too but to be able to have that
all together one spot puts you like right in the hub of everything you know and as a
facilitator and as an enabler you know to empower people to do this type of work I mean
it's a great place to be.
Because exactly because that's that is it was originally for labs but really anyone
can do this industry that's analytics is philosophy is that if that it can be accessible it's
just going to make everyone's lives better and you know business is going to run better
absolutely like I mean technically the data is there but I mean as you've found out and
as I know it's not easy to work with you've got these giant TSVs that are hard to parse
and they're too big to open and so yeah I think this is going to be a good project to
work on and I think there's going to be some obviously you're you know the last week I've
had to look at the data the little bit I've seen to your point it is yeah you just have
to know what's going on in there to use it the only way to do that is to just to suffer
the slings and arrows or map it out you know and then and share that with people but yeah
no it's such an interesting space that what's so exciting about it is and kudos to you is
that to get in there you know as early as you can and be some of the first people in
there because that's where the low-hanging fruit is and this is an example of low-hanging
fruit in my opinion right it's something that we can do pretty easily after we get to the
data it's pretty easily and start playing around results and once we get this set up
you could do the same thing and I'm going to be using R because I'm just more proficient
in R but you know for your perspective you could use you know Python or what's it called
what's the data science package is it SciPy or I can't remember what it's called but you
can do you could yeah there's a couple stats models but it yeah I'm a big fan of it's you
know your your weapon of choice so yeah exactly yeah so I mean you could do that you could
do the equivalent thing of what I'm trying to do if just by you know using some simple
Python models and get the same results and play around with it make maybe make those
results like you're saying you know or make it accessible through an API where people
can just run some inputs and get some outputs that might be the beginning of it you know
this is an exploratory experience right just just trying to figure out how to get to the
data and what we can pull out of the data and whatever you learn from it you know that
kind of kind of inform you on directions you want to take as well I think I think it's
worth pursuing because the data is there everybody wants it and it's just just slightly out of
reach of everybody and then this is the amount of work you have to put into what you've already
done right is you know through your API calls and through the the dictionary object that
you sent me where you see those relationships that's the key those relationships of how
that everything's tied together that's the key and it takes a lot of work to untangle
that and create that comprehensive picture and that's that's the barrier to entry well
I can help there because it just took a lot of banging my head against the wall so this
was you know I purchased from the lab results and so we had all these lab results that needed
to be posted and then like you know unhappy clients that are saying well where are our
lab results so right but it's it's just a lot there but like I said there's a
couple like key fields that kind of connect everything together so you know if you know
what you're looking for it's not that overwhelming so yeah I think it's worth putting together
because like you said I mean just retailers alone would just have so much value plus you've
got cultivators processors the labs independent researchers you know the government body yeah
and there's one one one sorry to interrupt you but there's my last class which was on
optimization there's models that you can develop for transportation right like optimizing optimizing
routes right so if you went to a company and they if you could find out I haven't seen
the transportation data but if it has point a to point b routes in it you can create optimization
patterns for these companies where they're spending less time on the road in fact that's
one of the most interesting that's one of the areas that I find the most interesting
so when I worked at the laboratory maybe a quarter of it was posting the lab results
three quarters of it was dealing with inventory transfers so that's primarily we were receiving
them and so like you said it's a scheduling problem so scheduling problem yes so at labs
they send out couriers so you'd send out two or three couriers they do I don't know three
to three to ten stops a piece you know like you said you can you can model all of that
so you can do there's the the traveling salesman problem with one driver and then the vehicle
routing problem which is multiple drivers and I think that's actually like a classic
optimization problem because I don't think it's like it's obviously not close-ended and
you often have to just do a brute force approach where you just calculate the distance between
all the routes yeah yeah some of the things that we're using and we cover this stuff so
fast in class that I just barely got through it with the skin of my teeth but we were using
like genetic algorithms to kind of hone in on on some of the most likely answers and
we did we actually went over the traveling salesman problem several times you know so
yeah and maybe you know maybe that's not such a great example because you can buy off-the-shelf
software for scheduling but again that you could you could still offer that as a service
and based on your experience it sounds like it's a legit problem but you have to remind
yourself like I mean you don't have access to Amazon's data you know you don't have access
to the UPS's data where they're zipping all over the place but here we actually have time
stamped transfers they have the route so you have point A point B we left at time A well
it's estimated we left at time A estimated arrival at time B so I mean there's just interesting
like you said there's low-hanging fruit so you can just do summary statistics like how
many miles were driven yeah like like that would be just an interesting data point so
like like yeah like how many and then you can just estimate like you know how much gas
they're spending or who knows but there are transportation companies and so I don't know
the particular questions to ask off the top of my head but I think it's a yet another
case where you've got a unique data set that you just don't find in other industries and
yeah I mean to your point yeah I mean to your point with you know the travel salesman stuff
and the miles driven summary statistics I'm sure there's probably insurance companies
would like to know that who are insuring these these transporters right they mean they may
want to know more have more detailed information about that there's so many ways that I mean
you know I may be overly optimistic I'm an optimistic person by nature but I mean just
just be ready because it's looking from the outside view and for the short time that I've
known you it seems like there's going to be a lot of opportunities coming your way and
you're probably going to have to like figure out how to spend your time on the most important
ones but yeah no it just there's tons of opportunity here and I know there's other people that
are jumping into this well other people that are companies I mean I've you know it was
the name that analytics company there's several of them but there's one that stood out to
me and if I heard their name I remember it but the two big ones are like new frontier
data and I kept on seeing Google I kept on seeing Google searches but I remember it but
I mean obviously there's people that are you know ahead and in this space but you know
the the devil's in the details and you're really getting deep into the Washington State
data you're familiar with it I can see a time in the future where there's probably going
to be some big money players that are going to want to consolidate all the different states
so they can get that you know 360 view of what's going on and that's where the I mean
that's where the the big money I would think would be a big opportunity but I could see
that happen in one day where you know if you're doing pretty well with analytics I could see
somebody that's got like well-financed that might knock on your door and go hey how much
do you want to sell analytics for you know I could see that happening it doesn't fit
in any industry automotive was like this at the turn of the last century and and any you
know IT different companies you start off with like tons of little companies and then
there's the consolidation phase so yeah I could see that happening and I think you're
right so essentially what you've got now is a shortage of data analytics so everyone needs
it there's a couple players providing it but they're they can't provide enough they're
trying they're trying their best and they are producing a lot but like you said there's
so many avenues like if you've got somebody looking at I mean they're looking at like
demographics but you know you're looking at market back market basket analysis that's
those are two different analyses then if you toss in you know logistics that's a whole
other ballpark yeah and that's these are all huge areas right they're all huge segments
of this whole process there's what's great about the seat to sale process is it's just
an immense supply chain really supply chain right and there's just so much opportunity
in that space so I was going to ask you based on your your history in the labs have you
ever come across people that propagate from it's basically from a cell cutting from a
plant and they grow them in like kind of like is it Algar and like at least Petri dishes
it's like string good do you mean like like clones so yeah things of the plant well not
a snipping of the plant and then you just prop you know propagate it in some roots nutrients
but I've heard that some people can actually take cell samples from the plants from like
some place certain place on the stem put them in a Petri dish with this Algar kind of growing
medium and then the cells will grow and also a plant will start to grow and the idea is
that any kind of viruses and other types of things are left behind in this process because
it's done in a sterile environment so you can maintain your strain without bringing
all the baggage along I guess that's kind of I don't know if you've heard of that I
just I stumbled on to and I was like huh sounds interesting that's that's an incredibly interesting
technique I haven't heard of that but like you said I think I wouldn't be surprised if
people are doing that because people are getting quite innovative they're also a little secretive
about those things so I think if someone yeah was using like that technique they may not
be telling everybody I at the lab we primarily had people just asking questions about like
you know different extraction techniques so that's well a lot of like sort of like the
scientists who were doing the chemists they're looking for the the most efficient cost-efficient
and highest yield extraction techniques okay that's the processors and I actually haven't
heard too much about the cultivators like I said I think it may be because they're kind
of guarding these secrets because from what I've heard it's like growing knowledge is
actually kind of rare so you have a lot of people kind of seeking like a like a head
grower like a head cultivator and like I guess everybody you know claims that they're like
an expert sure it's really it's really tough to you know find people that actually know
what they're doing so yeah yeah and I know that here in Michigan there's such a large
black market that's starting to now come into the sunlight you get a lot of more legit businesses
that are starting to bubble up and it's right now in the last 18 months it's been crazy
just talking with my brother-in-law who's involved in the business what little I know
about it I've heard through him and you get so many facilities that are popping up like
mushrooms now and some of these people are pretty well financed to get into this but
the businesses so let me give you a little bit of background on southeast Michigan the
most the largest community that's getting involved with this they're called it's called
the Chaldean community so there's a large middle eastern population here in southeast
Michigan like for example my wife is Jordanian right so there's a lot of a lot of people
from the middle east here they came to the auto to work in automotive industry and what's
happened is following generations they're more of like a like a merchant class of people
where they do they have a lot of restaurants gas stations this sort of thing so they've
a lot of these folks are well financed have their own businesses and saw this as an opportunity
to jump in so the Chaldean community owns the vast majority of the operations in southeast
Michigan and but here's the thing they don't have the level of sophistication of the things
that you and I are talking about they would have no idea about this type of thing and
as these businesses become more and more mainstream and legit businesses which that's where it's
all going there's going to be for the things that you're talking about like making things
easier for people with compliance there's going to be a lot of opportunity I think and
you probably already you know you've already experienced it with based on what you've told
me so that's happening here it's going to happen everywhere right so it's just a matter
of again low hanging fruit which things seem to be the most important exactly because like
you said there's you know there's entrepreneurs business owners they're trying to set up shop
and yeah they need people that they they have a bit of experience and they have some knowledge
to share just you just your knowledge on your lab expertise I bet you could probably hang
a shingle out and make your and people in southeast Michigan would want to consult with
you I guarantee it go ahead well what would they want to consult about do you think I
think the most basic question how do I pass the tests right what things should I be looking
for yeah how do I set my operations where I can get my certifications done I mean that's
the most basic basic question right so and you could advise them on well I have this
five step plan or whatever it is right based on your experience these are the things that
people mostly do not pass on and the reasons why that you could for an hour of consultation
you could charge a thousand dollars or something right it's not it wouldn't be unusual to charge
those types of fees I don't think because these people a lot of these people have the
money to do that so you know just think about it just think about it I mean it might be
an extra another revenue stream for you or something and if I could help you I'll let
you know I could I could talk to my brother-in-law's and put it out there that there's a lab expert
that I know and if if anybody you know if he's looking for consultation for specific
questions he's a good person to talk to definitely I could definitely help out so there's always
some of the things I know about I've seen I've seen my fair share of failures I I know
I know the pain points of the producers and processors and generally can help just make
things go a little easier and then the sugar on top is it also back it up with data and
you can actually look at say Washington data and actually show them okay you know these
are the failure rates in Washington maybe these are the failure rates in California
you know this is this is about what you should you can expect right and you could say these
are the failure rates and these are the reasons why they did fail and this is what you could
do to mitigate those risks really yeah total consulting gig yeah that's brilliant Paul
you're got a lot of ideas well I mean you just yeah you're in the right spot man I mean
it's a good good spot to be in I think but yeah I mean I'll talk to my brother-in-law
and feel a feel him out if you know what the demand would be for something like this and
then next time we'll talk I'll just kind of hit you up or I'll just send you a note or
whatever to find out if it seems like it would be a viable thing and then you could say well
if it's something I want to do what kind of evidence presentations you know other kinds
of things that I could use as consulting information and maybe you could do a test run you could
do test run with my brother-in-law or something and say if you're trying to get yeah I mean
then it's a no harm no foul and you can fumble your way through it and totally bomb it it
won't matter you're just learning so maybe okay that could be a good experience because
I could hear what you know what questions he has to ask and yeah do my best to answer
yeah exactly so maybe that's something we could do just to yeah we should we should
do that even if it was a totally informal and I got you guys together just to chat for
half an hour you would learn about the southeast michigan market and he would learn from you
on things that he could do from so he's got a what do you call it a caregiver grow operation
is what he's got so and I know he wants to grow and literally grow his operation and
get some licensing but in order to do that he's got to learn about how to get you know
passed from the state so it could be a mutually beneficial thing so that's something to think
about definitely I'm all aboard because that's what I'm here to do is help and share my knowledge
with other people in the industry and so that they can yeah they can run their businesses
better so I'm absolutely and don't don't forget you know if you know if this starts if these
different areas start to become promising for you make sure you get paid for it definitely
that's the first thing yeah exactly so but anyway all right I'm gonna have to probably
let's bolt here yes I've got a full day ahead so thank you Paul yeah awesome ideas yeah
it's always awesome talking with you and I'll stay in touch because I think what you've
lined out in Michigan is promising and we've got a lot of promising work ahead with this
data set yeah it sounds right it sounds really fun I'm looking forward to it and good luck
with your move today definitely Paul thank you all right have an awesome day I'll talk
to you later Keegan take care bye bye