- Selling IIoT for End users, take zero.
(clapping)
Yeah, actually a little side bar.
So I had two great conversations this week, like legit.
One was with a company based in Ireland that's just like us.
And then another one was with a company
that makes an IIoT platform.
It's an open-source platform actually.
They have an enterprise solution.
But what they primarily do is DevOps.
Which is basically, they make the infrastructure
and the solution that OEM, software OEMs,
would use to go to market.
So I develop an IIoT platform,
taking data from the edge and getting it into
a data-lake and then how do I consume it?
These guys do primarily time-series data.
They built the solution
and then they sell that whole solution, white-label,
to other companies who use it as their backend.
That's DevOps.
So they're basically selling their solution
to companies who are gonna go resell that solution
with their own brand name on it.
Which is a lot of what Kepware used to do.
That was originally how Kepware got started.
They wrote OPC drivers and then they sold it
and it was rebranded, white-labeled.
They were in charge of the technology
but their brand was really DevOp.
Now they go direct to market.
So this company, that I admit this is InfluxData
they do time-series data, they basically,
and we're gonna do a whole video on InfluxData.
But I had this amazing conversation with these guys.
They asked a question about historians.
And so this is a good example.
So you have your cloud solution,
you have your ERP, you have your MES,
I mean everyone's gonna be experts at the automation stack
when they get done watching all of our videos.
And then you have PLC/HMI.
So one of the features of SCADA is historian.
So you have SCADA, you have process control,
you have alarms, what other features do you have in SCADA?
You have notification, you have reporting.
What else, what other functionality does SCADA--
- [Student] Certain type of database applications
like recipe management--
- Right, so we could do recipe.
Which by the way, most people think that's up here,
it's not, it's a SCADA functionality.
Why, because recipe management,
you're right back to the PLC.
MES never writes, most people don't know that.
They don't know that MES
never actually writes to the system.
MES is supposed to just consume.
So recipe management is actually in the SCADA layer.
So what you've got is historian, control, alarms,
recipe management, reporting, and notification right?
You have other stuff as well, you have HMI functionality.
All the human-machine interface stuff.
So historian's a really important component
of the SCADA system, right?
Why?
Why does historical data matter?
- [Student] It's what happened.
- It's your DVR right?
It's your, I can combine pens together
to make decisions based on what happened at the moment.
I had a failure, I can go back, look at my historian.
At 7:01, what were the values
of all these various data points?
So we were talking to Influx.
Influx asks the question, "Walker, who do you think,
"which historian do you thinks gonna live,
"is gonna be the historian
"that everyone's using 10 years from now?"
And I said, "None of them."
You won't have a historian.
Why do you need a historian?
Why do I need a separate application
that historizes my data when my goal
is to take every single data point
from everywhere in my organization
and ultimately get it here,
into a data lake.
What is a data lake?
- [Student] All of everything.
- It's all of everything.
It's just every single data point there, right?
So this is everything.
This is everything stored.
So that's the history of everything.
I'm also gonna have a unified name space.
This is everything current state, 'kay?
I'm gonna have a unified name space,
that's everything current state,
now I'm gonna have a data lake
that's everything stored.
So, what is SCADA's role gonna be 10 years
from today or 20 years from today
when this is our architecture?
That is, I have a location,
what information gets stored in the data lake?
- [Student] All the history?
- Of what?
- [Student] Of your sensors and your production runs.
- What else?
- [Student] Environmental variables.
- Anything else?
- [Student] The name of who's working the shift.
- Right now, where do you go if--
- [Student] Everything from every layer of the stack.
- Everything from every layer of the stack.
That's the difference.
The data lake has everything.
Right now-- (muffled speaking)
Right now MES has just the OEE, history.
The ERP has our accounting, history.
MES will have things like our warehouse management system
and what was our inventory at given times.
Right now, all of those things have their own data stores.
They have their own data models.
What's the reason that we can't
write machine learning algorithms
that are comparing our ERP data and our MES data?
- [Student] It's data silos.
- The data's completely siloed
and what is a data silo?
It's not that they're in separate locations,
who gives a crap, we can connect them together,
it's that they're in completely different formats.
Someone created a data model specific to that MES system
and all MES data is not stored the same way.
I mean look at Sepasoft's MES modules.
The OEE 1.0 data model, that is the backend,
is completely different from the OEE 2.0 data model.
You can't even take two MES data models
from the same company and compare the data together.
Now imagine that I have three different MES systems,
because of mergers and acquisitions.
How do I compare the data of all my plants together?
The answer is, a unified name space for real time data,
and a data lake for everything else.
What's the advantage of a data lake?
Does the data format matter
when you dump data into a data lake?
No it doesn't.
Why, because we have this piece of the stack
that's got the machine learning and AI algorithms.
So there are, if you want to know the value of AI,
these algorithms can consume undefined data
and establish relationships.
That's literally what the algorithms do.
(laughing)
The algorithms, you know what machine learning means?
The algorithm learns the process.
So when you deploy a machine learning solution,
what you are doing is plugging undefined data
into one of your selection of maybe 20 or 30
different machine learning algorithms.
Whether it's Bayesian or any of the other
artificial intelligence algorithms.
And that program learns all the context
it needs about that data.
Then you can deploy analytics to the edge if you want to.
What's gonna happen with our stack?
This is why Rockwell's doomed,
unless they change the way--
Rockwell wants a unified stack, right?