SiteSync Exhibitor Demo: IIoT Made Easy With SiteSync and Ignition

SiteSync leverages the LoRaWAN sensor connectivity technology to allow industrial users to bring stranded assets and manual measurements into a central source of truth for data visualization, alarming, and advanced AI analysis all powered by the Ignition Platform. SiteSync enables field users to deploy IIoT sensors with the same ease of commercial IoT systems via preconfigured devices and QR codes so that these Digital Transformation initiatives can be implemented at scale. In addition to LoRaWAN sensors, SiteSync recognizes that many end users have thousands of HART compatible sensors and the additional HART data is another stranded asset that can be used for Digital Transformation. SiteSync will introduce a new asset management tool focused on HART sensors all powered through the Ignition platform.

Transcript:

00:00
Sarah Sonnier: Hi everyone. My name is Sarah. I'm here with SiteSync, and today we're gonna be talking about bringing stranded data into Ignition. Gimme a sec; my clicker's not working. There we go. Wrong way. So I'm Sarah; I'm a data scientist. I am also the lead developer of SiteSync. SiteSync is an Ignition... An easy way to get IIoT data into Ignition. And I don't meet a lot of data scientists in this field, so I'm gonna tell you a little bit about what I do.

00:36
Sarah Sonnier: Data scientists bring data in from different sources. They bring it together; they model it so that it is clean and usable to make insights out of. So I can create reports, dashboards, do machine learning, and send it off to my end user, who is gonna make actionable decisions off of it. But you can see from this donut chart, a lot of the time that I'm spending is not doing the fun stuff of data science. It's not doing that modeling; it's not... Or predictive modeling. It's not doing machine learning or making those reports. A lot of the time I'm spending is collecting data and cleaning it, which is less than glamorous, especially in the IIoT field. I specifically work with IIoT in industrial data. This data is very disparate. It is everywhere. It can come from multiple different systems. It can come in many different formats. So a lot of my time is spent wrangling this data so that my end users can get value out of it.

01:34
Sarah Sonnier: And so this is a data science hierarchy of needs. If you're familiar with Maslow's Hierarchy of Needs, you can't reach self-actualization or be your best self unless you have a strong foundation. Same thing applies in data. If you don't have a strong foundation of where this data's coming from, is it contextible? Am I reliably getting it? Is it the same measurement every time? If you don't have secure pipelines or trusted ways where you're getting that data from one platform to another, and if it's not easy to model clean, normalize, you're gonna have a hard time doing machine learning, doing reporting, and getting the value out of your data.

02:16
Sarah Sonnier: The whole reason we collect data is to be able to tell what's going on in a process and to be able to make our processes better. So if you don't have a really nice and strong base of your platform collecting that data, the value is diminished. This is what I look like often as I'm struggling with the bottom of my pyramid because I am having to go out there and actually go out to do that collection process. As someone who is in a predictive field, I would never have been able to guess how many times I would've had to wire a terminal block, mount something on a DIN rail, assemble a edge computer to be able to get the data that I need to be able to do this analysis for my end users. It's shocking to me because this data can be tricky to get, especially stuff on the edge.

03:02
Sarah Sonnier: So, you need somewhere that this data is easy to process. Ignition is my favorite data platform of choice. Whenever I have a request, I have someone come in, and they say, Hey, I have a problem. How do we tackle it? My answer is always, Can we do it in Ignition? And usually the answer is yes. The reason I like to do my data projects in Ignition specifically is because it helps me deal with the tough pieces of this pyramid. So the collection, modeling what will go down it, but it helps me take care of a lot of it so that I can handle the stuff at the top. So Ignition Perspective is the top layer. That is where we can do reporting, visualization really flexibly, where you come in and show my end user, Hey, this is going on in your process right now. This is what it was looking like three weeks ago. It's a really easy way for me to quickly take the data from the source and show it to my end user.

04:04
Sarah Sonnier: Then we have modeling. In the data science world, you make models or objects of what you're trying to show, report on, and do machine learning on. Same thing comes natively in Ignition through UDTs. UDTs let you model the process, the instrument, the asset that you are tracking. The fact that it is built in here and I can do transforms, I can do many different things at that level, where that data's coming from is huge. I can context that data, where it comes from, and as it gets sent off to other systems, that context is priceless.

04:35
Sarah Sonnier: Ignition is flexible. I'm able to do it in a bunch of different ways. By it, I mean go through and host it and move different ways. I can pull in data different ways. Flexibility is priceless to me when I have a bunch of different requests from different end users, and they're trying to all do different things, but the end goal that they're trying to get is to get that value out of their data. And finally, Ignition is open, meaning I can pull data in from anywhere really. I can pull it in from a SQL server database. I can pull it on OPC UA, MQTT, IoT devices. The fact that I can have one place where I can pull all my data into, I can flexibly deal with it, I can model it and visualize and export it for my end user. It's huge. And that makes my job as a data scientist so much easier. So when my job as a data scientist is easier, that makes me a happy data scientist. I can spend less time down here trying to figure out how am I getting my data in, when is it being measured, and spend more time doing analysis and delivering value for my end users.

05:39
Sarah Sonnier: So we're in Industry 4.0; we're moving into Industry 4.0, and the promise of Industry 4.0 is you can bring a bunch of... Capture more data. We can capture more data than ever. We can store it cheaply, but, and we can do that analysis, but we need to have the tools in place to be able to capture that. Something that is driving Industry 4.0 is we can measure more things than ever for cheaper than ever, which is really cool. Ignition is a great platform for Industry 4.0. You can come in; you can do your analysis. Countermeasure would be alarming and alerting. You can do responses in Ignition, and because it is so open and flexible, you're able to capture as many events as possible. It's scalable and structurable. SiteSync comes in, and it helps you capture more events and more insights than ever through IIoT and gathering stranded assets in Ignition. So we're bringing that data in and we expose it to you in your Ignition platform. Once it's in Ignition, you can do whatever you like with it, which is a beautiful thing. The speed that this is increasing at is crazy. The amount of data that's being generated is... It's hard to fathom.

06:53
Sarah Sonnier: So who is SiteSync? SiteSync is an IIoT Ignition module that helps bring stranded data into Ignition. We got our start, as many good Inductive Automation stories do, through Arlen Nipper. Arlen brought us a yoga gala, sushi sensor, and he was asking for help, how to deploy it at an end site. As we were helping Arlen, we went through and we realized this really wasn't gonna be scalable. It was really tough to get these data into a platform, and one of the things that we found was there were a bunch of different platforms and a bunch of different places this data could go. So, for example, some vendors have clouds that they wanna do the analysis on. That's fine and good if you're doing residential IoT or commercial IoT. But if you're dealing with data at the control layer, cloud is kind of a no-go.

07:47
Sarah Sonnier: And if you're gonna send data up to the cloud, it's probably not gonna come back down to the person at the cloud that actually needs that data. It's gonna come to people like me doing analysis, but it's not gonna be actionable for that person in the field. The other thing is this data would traditionally go to a traditional system like a DCS. But this data, this IIoT data, the insights, it doesn't behave like traditional instruments. You've got a lot of data. It comes up in a JSON format. There's a lot of attributes, and it doesn't check in at the rate that a traditional instrument would. It's not a continuous readings. So storing it in a DCS, it doesn't always make sense or rarely makes sense because it's not the same kind of data. This data is stuff about your process where the stuff in the DCS is the process. We're telling that this temperature is what's happening. But you could do supplemental measurements, and that's where you can get that value out of IoT. So this data needed a home. Where are you gonna put this data, especially in the industrial side?

08:51
Sarah Sonnier: So, SiteSync and Ignition is the home for your industrial data. It comes in; it's a good place where you can bring it in, marry it in with other parts of your process. Ignition is a great end platform for your data to come through. So we wanna create a nice landing space for the stranded data, the dark data, to have a nice place where it can be modeled. It's flexible, it's open, we can pull in anything we want, and be able to realize that value if that's through sending it to another platform through Sparkplug. If that is doing visualizations and dashboards, Ignition is a great place for this third kind of data.

09:30
Sarah Sonnier: I'm gonna talk a little bit about IoT, the trends; as you can see, it is steadily going up. There are 18 billion IoT devices installed today. It's a crazy number, and the number's crazy because it's really cheap, and it's really easy to get these measurements. These are way easier to install than a traditional instrument. A traditional instrument's probably gonna be around a hundred thousand dollars from specking it out to the actual install to bring it to your historian, where this is probably 1% of that cost to be able to install at one point and bring it somewhere, which is attractive. But as this velocity increases, you need to have a place where you can capture this data and get the value of the data. If we're just going into a data lake, that's nice, but how can we marry that data into other things about your process? Get that context to be able to deliver the value. We can see here that cellular is one of the biggest players in this. We're seeing a lot of cellular-enabled sensors. Another one is this LPWAN group of sensors that's NBIoT and LoRaWAN, both Grade 4 industrial applications.

10:44
Sarah Sonnier: Because we have all this data and we're getting all this processes... Because we're measuring so much data about these processes, we need a good place to hold it, store it, and analyze it. Otherwise, what's the point of gathering it? Data is valuable, and we're able to measure things we were never able to measure before. It's just doing that learning curve of how do we bring it all together. As a data scientist, this is very exciting to me that I can get more data about my process, and I can deliver more insights. I can say, Hey, something's going wrong here. Where previously it was kind of a black box.

11:21
Sarah Sonnier: So I wanna go over four different use cases from end users who are deploying LoRaWAN and IOT devices and how Ignition is helping them with their use cases. So back to our pyramid, we're gonna start at the bottom. The core thing is Ignition is open, meaning I can pull any kind of data that I want into my Ignition environment. What we're looking at right here is a corrosion monitoring sensor. This corrosion monitoring sensor takes a measurement once, maybe twice, a day, and it just measures the thickness of a pipe. It's pretty cool. Traditionally, you would measure corrosion by going around and doing operator rounds, taking a measurement to go off to a system. We had a customer install these on their pipes, and they were able to consistently get trendable data, meaning they were able to take a sample at the same time every day at the same exact location.

12:15
Sarah Sonnier: Which is huge in the world of data science because if I don't know exactly how that measurement was taken, can I trust it? If I see one is significantly different than another, was it a different operator? Was it a different day? Like, is it a different time of day? Like, how can I tell? By being able to standardize those measurements that are being taken, you're able to trend it, and being able to trend it is huge. This customer found that they had an erosion problem happening. It was slight, but they were able to see that after a cleaning happened on the pipe, the pipe got thinner. So they were able to come in and see, Hey, something happened between Monday and Tuesday. What happened? They brought in data from their other processes into Ignition, and they were able to easily see that, hey, I know exactly what happened between Monday and Tuesday. We had a pipe cleaning. They would never have been able to put all of that together without something like this, an IoT sensor. We have so many use cases like this where just starting to do monitoring, even if just a little bit of monitoring, is so much more consistent than doing traditionally polling it and being able to consistently take those measurements means that we can take better insights off of it.

13:33
Sarah Sonnier: Ignition is flexible, so really flexible, which is awesome. We had an end user here, and he was trying to monitor the power usage of different buildings in his campus. So he came to us and he is like, Hey, can we do this? Sure, absolutely. Working with an internal team with him to get this deployed, he wanted to do all on-prem, all on the edge. He said, "Okay, great." So we started building his application to be able to do an analysis to say, "Hey, how much power am I using every 15 minutes with a delta doing this calculation on it?" And he comes to us later he says, "Hey, actually the team that I was working with, they've lost. They've been reallocated to another process. I don't think I'm gonna do the project anymore." And we were able to flex, take all of the project, the logic, everything that we had built for him, and put it into a cloud application, which let him continue to gather his data. It's a little bit different 'cause this is not industrial data, but the flexibility of Ignition is huge for me because I don't like doing double work. I was able to just bring that data straight into another platform, another Ignition one, and he was ready to go within than 30 minutes, which was awesome.

14:42
Sarah Sonnier: Because he's now able to get his data, he was able to see as they closed a building on his campus, the power usage goes significantly down, which was really cool. He was able to see it in real time. He was also able to see, like as people came into a building, their power usage throughout the day; you could see it drop off exactly at 4:30. It was crazy. The flexibility lets me deliver to my end users the request and what they're trying to do. So they just wanna know what's going on in my process, and I can say yes with Ignition, which is awesome.

15:18
Sarah Sonnier: The next one is modeling. So in data science, modeling is very important. It means that I have a repeatable object that I can always use every single time. I can also make changes to my object and apply to everybody. This its object-oriented; as a programmer, I love this. So this is an MCC cabinet, and I'm able to pull in data from multiple different sources. Let me back up. This is the MCC cabinet over here, and there's a little sensor inside of it that it's able to measure the temperature, humidity, and light within that, which is great. But what happens if the room gets hotter? We could say, "Oh, we can alarm when it gets hot inside, but if the AC goes out in the building, we're gonna get a lot of alerts." So what we ended up doing for this customer was being able to do a temperature delta. We were able to measure the ambient temperature within the room and do a calculation to say, Hey, is my cabinet significantly hot, or is my room significantly hot? Being able to use UDTs to model what this cabinet looks like, being able to alert an alarm right there, and apply it to hundreds of MCCs is huge. It's a great time saver, but it also gives me a consistent format that I can do my analysis on. I can do automation on and I'm a huge fan of UDTs.

0:16:35.4
Sarah Sonnier: Modeling makes data science possible. This one's a fun one. So SiteSync has a Perspective project that you can do. You can look at your asset health on, you can deploy devices on, you can get a little diagnostics, and I had a customer that was deploying these. These are manual valve position sensors, and you have to calibrate them. There's a couple different ways to do it, and all of them are a little bit tricky. It's not an easy... It's not like installing a Ring doorbell. It's a little bit more complicated. So I had a customer, and they were deploying 300 of these at a site, and they called me up, and they said, "Hey, kind of having a hard time with this calibration process. Do you think that we could add this to where we're doing this onboarding?" So in SiteSync, you can onboard these devices into your Ignition environment.

17:28
Sarah Sonnier: And I said, thought about for a second, and I was like, "Sure, I think we could do that." They said, "Okay, well, we're gonna go to lunch. Like, let me know how's it going after lunch." And I was able to pull it together pretty quickly, and I was able to allow these users to calibrate in the field as they were going. And so I tested it out on my side;it all looked good. And then I get a call; I sit in the cube, and I get a call from the front desk, and they're like, "Someone from the field is calling." And I was like, "Okay." And it was an instrument tech, and they said, "Hey, I see a new button on the interface of Perspective; can I click it?" I was like, "Sure." And so we together were able to calibrate this valve within like an hour or so of that request coming in, which is crazy.

18:11
Sarah Sonnier: And the valve, the instrument tech was so excited. He said, this makes my life so much easier. I don't have to fuss with another app. I don't have to do this calibration process. You're able to just push this right to my Ignition project, right to my app. No crazy update process, just ready to go. He's like, "That is huge." This end user was able to install all 300 of these by themselves without hiring a third-party contractor. Saved them something like $30,000 and gave them the confidence to go out and deploy their own IoT sensors to monitor their processes. To be able to flex and quickly apply changes to my interfaces to give updates. And they wanna say, Hey, can I see the calibration status on the same page? Absolutely. So this is what we ended up building.

19:02
Sarah Sonnier: They're able to come in and see, Hey, what's my current configuration? and very easily configure these in the field. Being able to flex with my customer and being able to meet their needs makes me a happy data scientist because I can help them, and that makes me happy.

19:20
Sarah Sonnier: So we're talking a little bit about LoRaWAN and the Perspective side. I wanna show you... And we talked about how many devices are out there; something of like 40 billion IoT devices are projected to be installed by 2030. To be able to get to a scale like that, to be able to capture your data, you need to be able to easily onboard in a normalized fashion so you can know exactly this is what my device is, this is what it's measuring, here's how it's modeled. If you're gonna deploy a large fleet of these, anything, it needs to be standardized. I don't know if you've ever impaired at a project where someone started Modbus mapping one way and then started Modbus mapping another way. We don't want that at the IoT scale because there's so many devices, there's so much data. We need a strong foundation to be able to capture that. So this is... Oh. This is a video. I'm gonna get it.

20:28
Sarah Sonnier: This is a video of someone provisioning a device in SiteSync. So this is a Perspective-based project. It's using the native Perspective app. I'm able to quickly get in all of these device keys through scanning a QR code. We can talk about how complicated this is at the booth. It's very complicated, but I'm able to quickly onboard a sensor into your Ignition system, context it by giving information about where it goes, where it's installed, and over here, it flashed, and it showed that that device was instantly added to your tag provider as a UDT. It's that fast to bring a sensor on, have it contexted in its correct format, and then we can quickly see data come in through. It's about a minute from launching this to getting data in, and that's how fast you can add devices and add measurements to your Ignition system.

21:21
Sarah Sonnier: There we go. We've done a lot of different LoRaWAN projects. We've worked with a lot of different companies that had different configurations. Because Ignition is so flexible, we're able to do it at any scale. Whatever you're looking for, if it is at an all-in-one edge gateway where you can come in and jam everything on one machine, if it's a traditional Ignition server, if it's something like an enterprise deployment, we are able to help you bring value to your customers by bringing that IIoT data into Ignition. Once it's in Ignition, that's where it becomes fun. So I've been talking about IIoT data; I've been talking about LoRaWAN data. I'm gonna shift gears for a second.

22:11
Sarah Sonnier: I'm gonna talk about another kind of data. It's a stranded data more or less, but it's not IIoT; it's actually kind of older but has a lot of value. Data is data. So I wanna talk about HART. HART is an Highway Addressable Remote Transducer, which doesn't mean a whole lot to me, but what I do know about this is it is... Runs on a four to 20 current loop. It is the largest industrial protocol period. It is huge. It has an install base of 40 million devices. Devices are critical instruments in the field.

22:54
Sarah Sonnier: That 40 million is significantly smaller than the 18 billion or 40 billion IIoT devices. There's a reason for that. These are critical measurements that exist already in your process. IIoT, it's easy to pop a couple of temperature sensors out there and figure what's going on. This is the temperature transmitter; this is the valve position sensor. Something about HART, though, is these are smart instruments, meaning you're pulling a measurement out of it. A primary variable, if you will. This measuring how open or closed my valve is. But these devices have up to 240 variables within them that are able to tell you about your process, what's going on, maintenance, when was it calibrated and it's all out there in the field. But because of existing data infrastructure, the data's not really being polled. It's kind of in the same scenario of IIoT. Like, where does this data go? It doesn't really go into a DCS; it's not a primary variable, but it is interesting information about your process, and it really isn't pulled into a layer that can be analyzed easily. Well, in legacy systems. I'm sure that there are newer systems that are much easier to pull this out of.

24:12
Sarah Sonnier: So we had an end user come to us. The end user was using our LoRaWAN Ignition module, and he asked, he said, "Hey, like, I can see what the value is in this. I have a problem. Could we take a look and see if we could eliminate this process?" This process was he had to go into an asset management system, or his team did, poll a CSV of every single valve. When you have hundreds of valves, that is a huge, monotonous, tedious task to be able to poll to get the status of everything in your process. And he said, "There's gotta be a better way to do it." And I agree. If your process is tedious, if it means a human has to go out and do that download, it's likely something will get missed or it could get pushed off for a more pressing task. He asked, "Could we bring this data into Ignition so we could do that alerting and alarming? We can pull it easily; we can send it off to other systems easily." And I said, "Sure," because Ignition is flexible, it is open, it's easily modelable. We can absolutely do it.

25:19
Sarah Sonnier: So currently, as I mentioned earlier, you're typically bringing in one or two variables into your control system. That's just because you don't wanna clog up your DCS. You don't have the resources to pull it in. And honestly, PV, or the primary variable, is what you're trying to bring in. This is, in my case, a valve position sensor. And that is PV is how open or closed it is. But because there are 240 HART variables, you're leaving 90% of the data of your process in the field.

25:54
Sarah Sonnier: This is data that is huge for preventative maintenance. This is data that you already have; you're generating it; it's in assets that you already own. We're just not pulling it into a system that's easy to do predictive maintenance. As a data scientist, being able to get values like this and being able to quickly alert an alarm and say, "Hey, I think this might need attention; we might need to order something." Being able to give that insight to my end user is huge. I can quickly... Like this is a gold mine for me, being able to deliver those insights. IIoT is like that for me because I can quickly get new measurements. These are measurements that already exist that I just can't get.

26:37
Sarah Sonnier: So we ended up building a HartSync. This is something in beta. We're in active development, and it's a way to easily get that data stranded out in the field into your Ignition system. We're modeling; get it into UDTs based on what kind of device it is. So we're able to speak HART. We're able to come in and see exactly what's happening in your loop. If you're interested, we would love to talk to you about the beta group or what features you would like to see happening within this. The other thing is I would love to talk about different hardware architectures, 'cause I'm seeing a lot of different end users have different hardware architectures. So what does that look like for you today? If you're not pulling in hard data or I would love to also talk about what would that look like. Would you be interested in something like that? So please come see me. I'm in a booth out there. We could talk about this. This is huge for me because you're able to come in, you can get status, you can do requests, and you can talk to assets you already have.

27:40
Sarah Sonnier: And it's like that old commercial: it's like, It's my money; I need it now. This is your data; let's go get it. So bringing it into that home where you can do your predictive maintenance and everything, like that, is the value. It could be amazing.

28:00
Sarah Sonnier: In summary, Ignition and SiteSync equals your data has a home. We are able to bring in stranded assets, data about your process, data that doesn't belong anywhere else but can be easily and effectively married to other pieces in your process. And you can easily make insightful reports. You can make decisions off of it. If we go back down that pyramid, I'm able to collect it. I'm able to flexibly collect it so multiple different places. I'm able to model it, and I'm able to visualize it easily. When all of those are taken care of in Ignition for me, I'm able to do the fun stuff of machine learning, doing reporting, and whatever crazy dashboard request my boss comes up with because he's always got one. But yeah, this is super impactful. This is gonna take your end user from having to do all of that to being able to just get that value out of the data. And yeah. Thank you so much, and if we have any questions, I'll just take them.

29:09
Audience Member 1: I guess we'll just bark out the questions.

29:09
Sarah Sonnier: Sure.

29:10
Audience Member 1: Do you look at an IO-Link master or anything with, yeah, basically I/O or... Yeah, with... Yeah.

29:21
Sarah Sonnier: So I'm a data scientist. I am accidentally in this hardware space. Please do come talk to me about the booth with someone who can answer that question. But unfortunately I can answer your data questions and your data accessory questions. I can hear you.

29:39
Audience Member 1: Well, yeah. I'm sure everyone else can hear me, probably.

29:48
Audience Member 2: I have a question.

29:49
Sarah Sonnier: Yeah.

29:54
Sarah Sonnier: The Things Network?

29:54
Audience Member 2: Yeah. With your product? Sorry.

29:55
Sarah Sonnier: Yes, absolutely.

29:57
Audience Member 2: And how does that work?

30:00
Sarah Sonnier: We have API integrations into all of the major LoRaWAN network servers. So we're able to quickly sync devices both to Ignition and your LoRaWAN network server.

30:07
Audience Member 2: Thanks.

30:10
Sarah Sonnier: Yeah.

30:23
Audience Member 3: Is it being ready to use the HartSync?

30:32
Sarah Sonnier: So HartSync is a new product. We are in beta with it. We're active development. So it's still being worked on. Do you have any, like, questions, comments, concerns?

30:39
Audience Member 3: Is it related to Y HARTs for pH?

30:44
Sarah Sonnier: It could be. We are getting requests for Y HART, and I would love to talk more about those use cases. Right now it is for traditional loops. So we're going through a mux, maybe a modem on the control loop, and being able to forward that data off.

31:00
Audience Member 3: Okay.

31:00
Sarah Sonnier: And primarily what I'm offering is a way to pull that into Ignition. I don't really understand... I don't... Not that I don't understand, but I don't know all the configurations that could happen to get that data there.

31:08
Audience Member 3: Ah, okay. Cool. Thank you.

31:12
Sarah Sonnier: Thanks.

31:18
Audience Member 4: To follow up on the HART stuff, so is that a... It's in beta right now, but this is a separate module similar to SiteSync functionally.

31:26
Sarah Sonnier: Yes. Functionally, very similar, where the goal is to get that stranded data into Ignition as modeled. It's different in that it's a totally different protocol, but yes, same idea. It's a module you can install wherever you wanna do it. Edge, standard, wherever you wanna do it.

31:42
Audience Member 5: Thank you.

31:46
Sarah Sonnier: One more question.

31:48
Audience Member 6: How are you bridging the hardware gap on the analog interface that's going to the HART device to capture multiple devices, because a lot of controllers will be able to integrate in that and then provide it up to whatever DCS or SCADA system you have? How are you guys bridging that hardware gap?

32:08
Sarah Sonnier: I'm using a mux at this point, but I do wanna talk about what that looks like for other pieces. Essentially, if I can get access to that HART data, that's what I care about: getting that data to me, that's another person.

32:22
Audience Member 7: So, I guess good job on the HART module. This is Karthik here, so, but...

32:31
Sarah Sonnier: Hi Karthik.

32:32
Audience Member 7: Hello. So wanted to ask you, I know we are gonna capture the data here, but have you thought about how you're gonna integrate the data like you do for your lower network data, right?

32:47
Sarah Sonnier: Integrate, meaning sending it off to other places? Yeah, that's a built-in function of Ignition, which is awesome. So Ignition has a... As a open... And I really focused on the open bringing data in, but it's really open to bringing data out. So you can use Cirrus Link Sparkplug transmission to send data out. You could do API integrations out, you could sync it to a historian, your own database. The possibilities are pretty much limitless, which is what makes Ignition a great data platform. I'm really flexible and able to meet my client's request 'cause they're always changing. Thank you.