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Revit Data Exchange for MEP Workflows

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Description

The MEP industry has experienced a swift adoption of various digital tools, resulting in a growing demand for data exchange between diverse software applications. However, the fragmented nature of the ecosystem often leads to inconsistencies and errors in data transfer, making it difficult to maintain a seamless MEP workflow. This course aims to explore how you can maximize Revit software's Data Exchange feature to overcome the challenge of data curation and sharing. We'll delve into various use cases, including exchanging grids and levels, identifying and selecting elements from specific data exchanges incorporated into your model, transferring central plant models from Inventor software to Revit, and working with room data by using Microsoft Power Automate. You'll learn how to create and share data exchanges and how to consume data exchanges that have been created and shared by users of both Autodesk and non-Autodesk software.

Key Learnings

  • Discover how Revit software's Data Exchange feature can overcome these challenges.
  • Learn how to create and share data exchanges in Revit with both Autodesk and non-Autodesk applications.
  • Explore various use cases for data exchange in Revit for MEP workflows.
  • Learn how to use Microsoft Power Automate to streamline data transfer processes in Revit.

Speakers

  • Avatar for Sean Fruin
    Sean Fruin
    Sean Fruin is a Mechanical Engineer and Mechanical Applications Product Owner at IMEG, a full-service engineering firm with over 60 offices throughout the US. He is fascinated with automation and exploring computational design solutions for MEP design. He has had the opportunity to learn many aspects of the design industry, working in manufacturing as an MEP designer and consulting for General Contracting around the globe, specializing in BIM Management and Autodesk Revit development. Sean is living his dream, playing with the latest technologies, acquiring the knowledge to innovate, improving efficiency, and sharing his insights with the AEC community.
  • Avatar for Brian Nickel
    Brian Nickel
    My name is Brian D. Nickel. I am a graduate of Montana State University’s Graduate School of Architecture. I have been an educator for three years at Gallatin College in Bozeman, Montana. I have taught remotely from Boise, Idaho for two years through Microsoft Teams. We leverage VR technology to assist remote learning with Autodesk products. I am passionate and energetic about the use of AEC Technology and educating our future emerging AEC workforce. I have attended several national conferences where I have been a speaker, advocate, and collaborator with our industry. One of my core design principles is a belief that design can only have an impact through immense collaboration with the architecture, engineering, and construction (AEC) industry. The industry can be more successful by working through the design together and breaking free from individual silos. I have completed my NCARB Architectural Experience Program Requirements and beginning to study for licensure. A quote that has defined my career path and that I reflect on every day from Jack Smith, FAIA, my thesis advisor at Montana State University’s Graduate School of Architecture, “Don’t become a tool to the tool.”
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    Transcript

    SEAN FRUIN: What's up, everybody? And thanks for joining us to Revit Data Exchange for MEP Workflows. And Brian, thanks for being my co-speaker on this.

    BRIAN NICKEL: Thanks, Sean, for having me.

    SEAN FRUIN: So our tools can do really incredible things. We're doing some really cool stuff with fabrication workflows and everything. Here we got some Dynamo. We have all these different softwares. And the interchange of softwares has been a challenge. And the data in our industry has been a challenge. I'm sure you've ran into that, right Brian?

    BRIAN NICKEL: Yeah, often.

    SEAN FRUIN: Right, so maybe-- all right, we can really do some again really cool things with automation. I like to call it the triforce. So you need data, computation, knowledge-- all those things need to come together to be able to do really powerful stuff. But with these data issues, we're kind of like a frustrated Gandalf where he has two of the triforces but missing that third piece of data. So let's see how we can fix that.

    There's kind of been an evolution of our suckiness with data, right? So we have the paper age. My first job had-- my boss had a whole bunch of papers on the desk. He was very organized but his desk did look like this. And wasn't really good for automation. Brian, you want to talk about the desktop age and your struggles there?

    BRIAN NICKEL: Yeah, so in my first career job I got into an office and my desktop looked exactly like this from the previous employee that used to work there. And I had to clean it all up. And it sucked. It was horrible. And then we made it into-- we kind of moved into this internet age where now everything lives in Dropbox or Google Cloud or OneDrive. And now we've just taken all that desktop data and moved all that chaos into the cloud.

    SEAN FRUIN: Right. And this has kind of led to technical struggles, right? So we have these bulky files that we're trying to move around. And it's hard. This makes us end up being in silos, right? And then the versioning issues that always pop up with these big bulky files because it's hard to share them. There's also the people aspect. If you want to touch on that, Brian. I know we have struggles there.

    BRIAN NICKEL: Yeah, we often get little cooperation. There's major skill gaps in terms of just communicating design work and communicating projects between different skill gaps. Whether-- Sean went to school for mechanical engineering, I went to school for architecture. So I have some skill gaps to Sean. And then of course--

    SEAN FRUIN: I definitely didn't learn about databases, right?

    BRIAN NICKEL: Exactly.

    SEAN FRUIN: Or how to share, right, so the coding aspect too that's necessary right now to move this stuff between.

    BRIAN NICKEL: And some major security and IP concerns. Where now that we've got everything into the web, we're all worried about our data. And we want to make sure that we're protected.

    SEAN FRUIN: And you had one of those in one of our examples, you had a little IP issue. But things are looking better, right? So most recently, we've seen a lot of geometry interoperability stuff with Revit. I've actually been able to-- I got scanned at a conference in like 2019, I think. And just in 2023 Revit, I was able to actually to get my statue of myself. I look pretty good in dynamo gray there. I finally able to get it in.

    We have the Parameter Service, a cloud service, that's helping with the shared text files of Parameters. Hey, let's put all this in the cloud. And then we can get rid of those versioning issues and make that a little bit easier. And then the one that we're going to talk about today is Data Exchange. So again, another cloud service. And all this is setting us up for hopefully less suckiness in the future.

    So that's what we're going to talk about today is Data Exchange about MEP Workflows. So I'm Sean Fruin. I'm a mechanical engineer turned programmer turned product owner now at IMEG.

    BRIAN NICKEL: I'm Brian Nickel. I'm the CEO of Allied BIM. I also am a adjunct faculty at Montana State University, Bozeman, Montana. I'm based in Boise, Idaho. And it's nice to be here today.

    SEAN FRUIN: Right, so our agenda is going to be we're going to focus on the MEP engineer. An MEP engineer has all these touch points with architects, over on the left, VDC people up at the top, contractors to the right, and the bottom two people out in the field and fabricators. We're going to explore different workflows, different softwares and how we connect all those.

    We're going to try to use mostly The Shodan's Tower that's shipped with Revit 2024. And just do an exploration of how this new service can help. A lot of the stuff is in beta. So we're going to get shocked a little bit. There's a little bit of just beta, new stuff. So be wary of that. To be exact though, we're going to look at Data Exchange is 101. We're going to look at some Power Automate Workflows and how we can connect Revit with our Microsoft tools.

    We're going to do around trip from Revit to Inventor and back. We're going to look at some automated coordination workflows, a fun little generative design workflow, Revit to fabrication with Allied BIM. And then we're going to look at the big picture and where the industry is headed with all this.

    BRIAN NICKEL: Some of our learning objectives today is to discover that Data Exchanges can help us overcome our data challenges. We're also going to learn some of the many ways that we can create and share Data Exchanges. We're going to explore various use cases for the Data Exchange module in MEP workflows. We're also going to learn how to use the Data Exchange to bridge the Revit model and Microsoft tools gaps.

    SEAN FRUIN: Let's first off talk about what is Data Exchange 101. Kind of peek in there, remove the fog that's around it.

    BRIAN NICKEL: It's a file.

    SEAN FRUIN: So it's a-- no, it's a database.

    BRIAN NICKEL: It's a Data Exchange.

    SEAN FRUIN: Right, so a exchange-- it's not a file. It is kind of a database. But it's really this way to write to and from databases. It's all hosted on the AEC cloud. So it's kind of in this interim in between. Autodesk's definition is this, right? So the idea is to capture parts of our models to be able to share those parts across many apps. And then because we can really control what we're sharing, there's a lot of trust and we get rid of some of those IEP issues.

    So here's a little Data Exchange action. So this is a Shodan tower that we put together. You can see that we have a whole bunch of data exchanges on here through the things. Up there I just did the versioning. So again, we're able to see versioning, when stuff's updated. Here's our loads model, brought up in the clouds. So maybe I can share this. Maybe the architect does the loads. And they can share that with the MEP guy.

    Maybe they do a simpler model of their architecture model to be able to do this. So it's really interesting to think how different parts can go into this one kind of file up there. And this is our fire rated walls from the thing and then our conduit. So we're going to use all these Data Exchange and look at how to create them and how consume them throughout the presentation.

    BRIAN NICKEL: There's lots of flavors to Data Connectors, and Sean's going to bring up all these flavors. So you can see that on the left we've got like an ice cream cone with a bunch of different flavors from Power BI, Microsoft Automate, Rhino, Inventor, Dynamo, and Revit. Each one of these have their own Data Exchange connector. And it can synchronize right up into the Data Exchange module.

    SEAN FRUIN: Right, so kind of the autonomy of Data Exchange. So a connector can either write information or it can read information. And not all can do both. Most of them can do both. So Revit, Rhino, Inventor, Dynamo it can read and write. But then we kind of have these ones Power Automate and Power BI it can only read stuff. But what this gives us is Endless Swim Lanes. You can talk about this, Brian.

    BRIAN NICKEL: Yeah we see these as Endless Swim Lanes. So back to the initial diagram that Sean showed at the beginning, we've got the architect, we've got the designer, contractor, field foreman. And everybody's initiating in their own swim lane one of these data connectors-- whether that's a Revit data connector, a Dynamo, Power BI, et cetera. So these can read and write back to other stakeholders in the project.

    SEAN FRUIN: Yeah, so setting up, there's a lot of pre-checks that you have to go. It's one of the hardest parts that I've found setting this up. Not that it's hard, it's just you've got to go through a checklist, right? So on the Autodesk Construction Cloud Hub, you got to make sure that you have work sharing files up there, especially with Revit. You need to have the apps included by an admin person. You need have the right permissions. And then you need to make sure you have the licensing too write.

    There's an asterisk there because it's only to write. Actually, somebody without a license can consume the Data Exchange or read it. And then for the apps, there's another set of checklists. So you have to make sure that you download the App. So this is downloading Dynamo sandbox, downloading Rhino, downloading Inventor. You have to make sure you have the license, obviously, for those.

    And then you have to make sure you download the connector through the Autodesk App Store for this. So again, like I said, I kind of hit some problems doing all this. This is just from the development team. This is in the handout if you really want to go in the detail, but this is really for the beta ones.

    Right, so first thing that came up is I do not have admin access. So I had to reach out to my BIM manager and his first question was, hey, I don't know about this? Is this going to cost tokens? As of right now it does not cost any tokens. But we don't really know what going to do in the future. It's kind of where we're going with this cloud stuff, right?

    But I told him no tokens right now. OK. So as you can see, you can go into your AC Account and see if he's have been added by somebody, and see if you have access to them.

    BRIAN NICKEL: So we had an example of this come up between Sean and I. So Sean and I kind of experienced an actual problem. I asked Sean to add me to a project. And when he added me to the project, the project was not found. So this was kind of our first little road bump in using it. And then Sean's going to add how we communicated with Autodesk team to make this work for us.

    SEAN FRUIN: Yeah, so it's actually really easy, right? So you can add just people that have an AC Account to the hub. You can see a lot of people put work into this presentation to make it happen. A lot of people from the Autodesk team, so thank you. And then we have Brian there and everything. So again, just adding people there. Then there's downloading the connectors. So here's just a quick Google search for the App Store. You can get in there.

    For the beta ones, it's not quite as simple, as we'll see. So it's a quick Data Exchange. Here they all are. I go to it. Oh, open the link. But that didn't do anything. Oh no. A little bit investigation you can find, oh, here's the link to get to the community feedback portal. So sign up for that. And then you get in there. And there's all the executable files for the connectors. Oop, next one.

    What other problems did we have? Oh yeah, so free trial, right? So I have the AC Collection. I do not have access to Inventor. So I had to give free trial. It works for Inventor and Rhino if you're just trying to explore a little bit and see what's possible. So yeah, so now let's dive into some of the workflows. So the first one is this Revit to Microsoft tools workflow.

    So problems, right? Senior engineer, hey, will you open up Revit? Nope, they back away. They don't want anything to do with Revit. So we're going to see how maybe we can get that data out of Revit and then there. Another fun workflow is this cat and mouse game that we play with trying to keep coordination between rooms and spaces. It's not fun. Maybe we can have some alerts there.

    And then finally, right, I wanted to look at, hey, can we pull a whole bunch of information, standard, clean, data out? Because it's always been a problem to get that clean data. You try to pull information from a whole bunch of projects. The more projects you pull out, the smellier it gets. So here I just want to highlight some special abilities. I don't know if anyone's played Stratego, if you have, you probably know what I'm talking about. If you haven't, you're probably like, what?

    Stratego's just a fun game. It's like capture the flag where you bombs. But then you have these tokens, that's like 1 through 10. But some of those tokens have special abilities just like some of our connectors have special abilities. So the Dynamo one you can really be specific with what you're trying to write to a Data Exchange. The Power Automate just goes out to everything, right? So you have the ability to really connect to endless possibilities there.

    And then the Power BI one's really cool because it's a really fast way to see what's in your Data Exchange, and to be able to actually work with that Data Exchange. So in this workflow, we're going to start with the architects going to do their modeling and their Revit model. They're also going to do the energy model. Might be a little bit of a stretch, but we're going to run with it.

    They're going to use a 3D view with the Revit connector to make a Data Exchange for rooms and a Data Exchange for the energy analysis stuff. We can also have our VEC guy make spaces with the Dynamo connector. And then we're going to send that out to Power BI and Power Automate to Teams and Excel. So with the Power Automate, they actually make it really easy with some templates to get started. And you can also make it really advanced if you wanted.

    So yeah, here's our first little example. So if you do a little Google search, you can get to this actual Microsoft page. But then here's all the template examples of the template. So we're going to go ahead and look at the Microsoft Teams notification. So the idea of this one is, hey, if the architect updates their rooms, I'll get a notification in Teams. And be able to address that change and look at it.

    And I think this can go a lot further. So here you can see I'm connected to those. So I'm good to go there. And it's pretty easy just filling out as long as everything looks good and connected. You can see it's just a dropdown of my hub, my folders. And then the actual folder that I'm looking for exchanges. And then I can go to my flow bot where I can pick a channel, set a message.

    I don't think you need to put recipient. Maybe let's just do a channel that way you don't need a recipient. So again, a lot of different options in here to really customize what you want. And you can build on top of this layering different things. So we're going to go into the mechanical discipline one. We're going to say just general. So Teams channel, and then we can write a little message like, hey, architects updating those silly rooms again, right?

    So here we have it inside of Teams, right? So in this one, zigma 24 folder. Those damn architects changed the rooms again. And then, of course, a fun little emoji just to add some emotion there. Make it fun. And this next one we got the Autodesk.

    BRIAN NICKEL: What's really nice about these data connectors is just how easy they are to set up. So there's no programming really involved. It's just setting up basically routines based on your projects and your parameters.

    SEAN FRUIN: Yeah, I'd kind of compare it to scripting, right?

    BRIAN NICKEL: Yep, it's a lot like Dynamo scripting, just a little bit different. It's more setting up a flow or a workflow.

    SEAN FRUIN: So here we have setting up some with the Excel. So getting our information into Excel. Maybe?

    BRIAN NICKEL: There it goes.

    SEAN FRUIN: Yeah, so there it goes. A lot of different options here. Here we can start to say, hey, update rows. Make new rows. A lot of different options. Again, this is one of those ways that we could extract information and write it to a database. So there's a lot of options to do it in not just Excel, that's not really a great database. But you can also set up ones to do it in other databases.

    So here we can see it on the right then updates coming in. Yeah, so again, endless possibilities. Really cool stuff. You can dig into the details a little bit more through the handout if you want to know exactly what to point and click at. So this one's new, new and this is the Power Automate workflow. The team is working on this right now I think to get it ready for AU And Out.

    But this is really cool. So this is an example. I didn't put this one together. This is from the Dynamo Team. But here they're pulling in the geometry from that sample model, you can put color codings on it. So I'm really excited for this one for senior engineers. I see setting up standard templates or standard dashboards, right, that you could see pulling out the same stuff. So really cool stuff.

    We were experimenting a little bit. So this is the energy model that I did. There's a whole class, by the way, about creating the energy models for the show and tower. So I'd check out that class if you're into that. But then once that's done, we can pull this in and let a senior engineer review it. So, again, so much cool stuff possible. And again, infinite possibilities. These are probably not even all the apps. It's all the ones that could possibly fit on the screen.

    Yeah, I don't know, if you're an expert at Power Automate, it's pretty cool. Right, so again, outcomes of the ability to connect these. I think you could start to set up workflows to pull out a whole bunch of information from multiple projects. And then put that clean information, and you could have it clean through just View Templates and stuff, so you know exactly what you're pulling out, right? And we can start to pull out it a whole bunch of models, which is huge for the future of automation.

    We can get that information to a senior engineer just through a dashboard. And then we saw that we can set up those notifications to have some collaboration between our architects and engineers. So if they make an update, at least we get notified, trigger the right people.

    Our next workflow is all about the Revit and Inventor Workflow. So here we have Prefab Mechanical Plant Workflow. Brian, you want to hit on the problems here?

    BRIAN NICKEL: Yeah, so on the problems here, essentially, we look at Revit as being the magnifying glass, and we look at Inventor being more of the microscope. So like microscopic level of detail versus higher level of detail or lower level of detail. In this picture, we've got the guy using the wrong tool to inflate the tire. He's trying to blow that tire up and it's the wrong tool.

    And so this exercise is more or less understanding what is the right tool, and which tool we should be working with, and how we can connect to that. So one of the ways that we're communicating is that, oh, we can't get to the nuts and bolts inside of Revit like we can in Inventor. And so with these Data Connectors and these Data Exchanges, we can actually now leverage the Inventor model inside of our Revit model to where we can get a more fabricatable level of detail. And Sean's going to dive into the solution for this.

    SEAN FRUIN: Right, of course, we've had that geometry problem where it can't be translated. Like I said, even with the geometry interoperability with Revit, and with Data Exchange, yes, we can now.

    BRIAN NICKEL: We can actually communicate now. We have the swim lanes. So everything's in harmony.

    SEAN FRUIN: Yeah, so in this exploration, the idea is, hey, the mechanical engineer is going to have-- he has a mechanical plant. And we want to go to the fabricator to make some pre-fabricated solution, right? So we're going to send the boundary of the mechanical room and the incoming services, send that up to AECC. And then the contractor can pull that information in, do their m and then they can make the data exchange, so we can coordinate that back in.

    The exact data flow kind of more detailed. So again, we make a view with just the walls and the incoming services that we want, make a data exchange, they consume that exchange inside of Inventor where you can see the walls. Pump our assembly in there, create that, send it back for coordination. So yeah, this is actually a pump I found on the internet. Again, I'm not an expert at Inventor. So bear with me a little bit.

    But here we are making the Data Exchange. So with a 3D view I'm able to crop just to the stuff I want. You can see, I have those two incoming services. I go up to Collaborate, Data Exchange, Create my Data Exchange. Name it whatever you want. There's some abilities to put other filters in here, but we don't need to do that because we did that all through the 3D view itself.

    So this does take a little bit of time to upload. I've actually sped this up a little bit too, just so we're not watching the wheel. But there we have it. We have our Data Exchange loaded. So now let's go to Inventor. So here in Inventor, we can start a new project by actually opening up a Data Exchange. If you got to Open down here, you can see Load Data Exchange.

    Again, make sure your pre-checks are in. Do I have access to the Hub? Do I have access to all this stuff? Go down to there's my Mechanical Room. I'm going to load that into Inventor. And here we have that geometry from Revit in our Inventor. So now we know what we're constrained by. This is a pretty big constraint. We can also dive into the parameters and see some stuff that comes across from Revit. I believe on the Roadmap is to add more ability to do this more Data Exchange rather than a geometry exchange. I would say the Data Exchange right now is more focused on geometry than the granular parameter data.

    BRIAN NICKEL: Think of this as the baseline of the initial set that they're offering us in terms of connections. And obviously, with this being in beta, some of the stuff is limited. But it will become more robust in time.

    SEAN FRUIN: Yeah and speaking of which, actually I don't have the exact video, I'll show you why in a second. But here we are adding some granular data through Inventor that would come across in Revit. I turned off the walls already, but we're able to get this pump in the location that it would fit in that room. It's actually a boiler assembly. Cool stuff there.

    There were quite a bit of lessons learned here. So again, I didn't get all my ducks in a row before starting this, right? So I didn't have the interoperability tools there. So we got a frustrated duck, he's wandering on his own.

    BRIAN NICKEL: We got an angry duck there.

    SEAN FRUIN: Yeah, make sure your stuff's up. Don't get too many angry ducks. My trial actually ended, so the Inventor trial is pretty darn quick. So that was a problem. But I was told from an old chef that I worked with, there is no such thing as problems, only solutions. So I was able to go through a back door, right? So I used the Rhino Data Exchange to get this stuff back into Revit.

    Another thing I learned with pulling everything into Inventor is units matter. So the units of this was in millimeters. You pull in inches, you can see that's not going to fit in that room. So yeah, gotta pay attention to those units. Also one of the times I did it, I actually pulled in 256 generic models. So again, this isn't ideal, right? This is actually the problem that we have when we try to go down to the nuts and bolts. So there's stuff you can do to make this one solid piece of geometry and you're not dealing with this.

    BRIAN NICKEL: Yeah Revit doesn't like 256 components. It creates a lot of project bloat.

    SEAN FRUIN: Right, it's really hard to move. I was trying to move it, too, and it did not want to move. This is actually the example from Autodesk where they just show this. I want to show this to show it's possible. I know I ran into some issues. But this is kind of their version. So here you can see the Data Exchange. I'll skip it a little bit. Yeah, this is all about fabricating a ladder, or a set of stairs, right?

    BRIAN NICKEL: Staircase.

    SEAN FRUIN: Yeah, so then if we jump ahead a little bit, you can see that we can actually get this stuff into Revit, and you can start to see the parameters. So it's possible. I just kind of fell short. Do you want to cover the outcomes on this one, Brian?

    BRIAN NICKEL: Yeah, so on the outcomes, once we get everything in a fluid state, like once we've got all of our Data Exchanges set up in the proper way that we want to send and receive data, it's kind of like operating a speedboat. It's very efficient. It's very fast. Number two, we can eliminate this telephone game or string and tin cans for communication. We can streamline the communication to where it's a direct data connection.

    And then number three is that we have all the tools in the kitchen. So we've got multiple ovens for that, basically.

    SEAN FRUIN: Yeah, all the pots and pans, all the--

    BRIAN NICKEL: We've got everything organized

    SEAN FRUIN: It's like we're working in a professional kitchen.

    BRIAN NICKEL: Yep, we are working with a professional set of tools.

    SEAN FRUIN: So our next one is about the Life Safety Dampers at Fire Rated Walls. Pretty easy workflow. Pretty straightforward. Find intersections of fire rated walls. So hey, architect, can I get this fire rated walls please? So what do they hit?

    BRIAN NICKEL: Then we're suddenly in a Where's Waldo approach. And we didn't--

    SEAN FRUIN: Literally like playing Where's Waldo.

    BRIAN NICKEL: Yeah, we didn't realize like-- if you've ever been through this experience, you know that looking for the fire rated walls is not always the easiest thing to do. You have to go through your project and dissect it.

    SEAN FRUIN: I'm glad I was trained on this when I was in elementary school with Where's Waldo on how to find spot through a complicated floor plan and find those dashed lines.

    BRIAN NICKEL: Yeah.

    SEAN FRUIN: So that was fun.

    BRIAN NICKEL: We're kind of bouncing down the slide and tumbling down the slide.

    SEAN FRUIN: Well, this is the next way, right? So the next way is-- here, here's my whole Revit file. So it goes all right for a while. Brian just said, hey, you're in Dallas, too, right? So that slides also 105 degrees.

    BRIAN NICKEL: Yeah.

    SEAN FRUIN: So the analogy here if we dig a little bit deeper, the architect is doing all their work. They stuff it in a Revit model. That Revit model gets shared. It gets linked into an MEP model with a whole bunch of other models-- structural, electrical, blah, blah, blah. But then we have to dissect that model, I guess.

    BRIAN NICKEL: Untangle it.

    SEAN FRUIN: It's really complicated.

    BRIAN NICKEL: You have to detangle it.

    SEAN FRUIN: Yes, detangle it like the Christmas lights. This gets really complicated, with design options, phasing. At IMEG we're actually building a whole app just to deal with this problem. So yeah, there's got to be a better way, right?

    BRIAN NICKEL: We saw in the beginning kind of like the spaghetti bowl, right? Just a tangled mess. And now we're kind of taking one piece of pasta or one strand of this process out and streamlining it. Because we want to go down a cool slide. We don't want to go down a bumpy slide.

    SEAN FRUIN: A slide that my three-year-old can ride down.

    BRIAN NICKEL: Yeah.

    SEAN FRUIN: Yeah, so hey architect, give me fire-rated walls. Sure. Here you go through a Data Exchange. So that's the solution we're going to look at today. Pretty straightforward. They still do all their stuff in Revit, build their model, but then they have View Templates ready to go. So it's really easy to make a Data Exchange. That gets loaded up on AEC. We pull in our ducks. And then we're able to do a little Dynamo scripting to place those fire-rated walls.

    So inside the model, you can see that we already have that stuff in there. So it's already color coded. We have our fire-rated walls ready to go. The information already there. We can go quickly create a Data Exchange with just this information. Again, same thing. Really easy. Go through that. And then the HVAC model.

    BRIAN NICKEL: Oh here. I'll click it.

    SEAN FRUIN: Yeah, so we can open up Dynamo then within our HVAC model. Here's the Dynamo graph. So that part in gray would be a whole bunch of stuff pulling in links. Dynamo doesn't work great with links, right? Instead we can just say, hey, load Data Exchange. So again, this very familiar window that's keeping popping up everywhere will come up.

    Here we can just, again, go down to our right thing. We have the Fire-rated Walls Data Exchange. We'll pull that in. Here's actually a nice thing where you deal with that units problem. So here we're in Revit, so it is feet. We're in American imperial units, right? So feet to feet. It does a little bit of thinking. But then you can see that the geometry comes.

    There was some data cleanup I needed to do, actually. So something that's kind of weird about the Data Exchange right now is it pulls in some stuff that you wouldn't expect it to. So it pulls in the levels. So I did some cleaning up there. But then we can see with just a little bit of cleaning, we have all of our fire-rated walls. And hopefully, that just becomes a little bit easier with some iterations on these tools.

    In the blue, we have actually collecting all the information. Just some Dynamo advice here I guess, I like building definitions now. And then output of a definition is a dictionary with all the information required to do this computation workflow. It's a really clean way. You can actually hide nulls and stuff.

    If you want to look at the code down to the thing, actually I'll share this with the presentation files. But then you can see we have all the lines that generate it's the locations of the ducks, all our fire-rate walls. We find out where they intersect. And then we end up in this model putting I think like 12 dampers in where there's intersections between those fire-rated walls.

    BRIAN NICKEL: And what's nice about this is once you've got the Dynamo script set up and saved, this is reusable. So Sean's gone in and actually set up all of the information required to where you've got steps 1 through 6. And now someone else can just go into that file and understand the logic of what that Dynamo script is doing, as well.

    SEAN FRUIN: Will you do the next slide, Brian? I think I lost it.

    BRIAN NICKEL: Oh yeah, sure.

    SEAN FRUIN: Oh there we go. Yeah, so here's just, again, a still of it. So keep on going. So the outcomes of this, right?

    BRIAN NICKEL: Actually I want to go back to this slide. Yeah, I want to go back to this slide for just one second because we kind of glossed over it real quick. What's kind of nice about this is once we get all of our data exchanges set up, we can sit back and smoke cigarettes and drink whiskey. Because now everything's all set up for us. Like we don't have to re-set up anything or deal with that tangled nightmare anymore. It's kind of creating a more assembly line approach with all of our data.

    SEAN FRUIN: Right, cleaner data, better automation.

    BRIAN NICKEL: Yep.

    SEAN FRUIN: So for next one, this is kind of a fun example, this is the Generative Design Exchange. The goal here is to build a robot that can start to hit a hole in one using the Genetic Algorithm. So we'll dig in that. But really the problem comes from a story. So I'm not very good at golf. Last time I played golf, I had my friend's very expensive tapered driver and I hit the ground. And it went 50ft out into the-- it went farther than the ball. And I had to stop everybody and go get it. I've not held a golf club since.

    So that was kind in the back of my mind. So then I go with Brian, right? And we're in line after built-- I don't know what year-- but we're at Disneyland enjoying our day together. And Brian's--

    BRIAN NICKEL: It was July of '22. July of '22.

    SEAN FRUIN: Exactly, July of '22. And he was like, hey, check out this really cool but top secret model that I got. And it was just really cool that you could see it on his phone. We ended up riding the ride. And nothing was said about it. But then I got invited and honored to speak here at Autodesk 2023. So I was like, hey dude, can I get that really top secret, cool Revit model that you--

    BRIAN NICKEL: No, Sean. No, Sean. It's top secret. You're not allowed to use it.

    SEAN FRUIN: No, I just want the I just want the tee and hole locations, Brian. We can do it with the Data Exchange.

    BRIAN NICKEL: And I'm like, what's a Data Exchange? Like, I have no idea what this is. And so he sends me a link. Yeah, he sends me a link. And I go in and I explore it. And I realize, wow, I can actually share a model without actually sharing the model. He just he's just extracting data from my model, right? And was like, let me think about it. And then I went and looked at it. And I said, sure. You know what? You can Data Exchange and tap in and get the locations and some data from my top secret model. But you can't have my top secret model.

    SEAN FRUIN: Right. So the data workflow here is Brian has his top secret Revit model. He was able to build a little Dynamo script to put his own families at the tee load and hole locations. Create a Data Exchange with that. Put it up on the cloud. I was able then to take that, consume it, and build this awesome fun little algorithm.

    BRIAN NICKEL: Without stealing my top secret model, which was the best part.

    SEAN FRUIN: So the whole idea here is we bring in that Data Exchange. There's also a cool extra bonus tip in here about sharing models. So if Generative Design, there's this data remember node. And it actually caches like geometry information inside the Dynamo script. So I was able to even get rid of the Data Exchange nodes. And all the information is stored inside this remember node.

    And within that remember node, we have our tee locations and our hole locations. And then we have three variables-- the exit velocity, the club, which actually says what the angle that the ball is leaving at, and then the body rotation. Then really the math for this thing is just projectile motion. We measure the distance. And then there's a feedback loop that looks at that.

    And what's really cool is it's actually like a 4D problem. So here's the demo. So we have the flag. Here's the flagpole, one of them in the Data Exchange. Viewer, again, on AECC. But then you can see we just have these data remember nodes where all that information was cached. Here we have our inputs. You can just go around and blindly guess what it is. You might get a good shot like this. You also might get a shot that looks like that chip at the beginning.

    It's almost like a little bit of a video game, right? A little bit under Windows '95, but hey. So that one right there was the shot of-- or that one is the shot of the GIF.

    BRIAN NICKEL: So Sean, so what you're essentially saying is once we get the mathematics correct I'll be able to shoot a hole in one.

    SEAN FRUIN: Right. Yeah. We have that example, too, I think right here.

    BRIAN NICKEL: Yep.

    SEAN FRUIN: Yeah, so here we're going to create a Generative Design study. You can see, we pick our club. We pick our tee location, so one of 60 different locations. And it's going to adapt to that location in the hole that you send. Here we're creating the thing. We don't get very close on this one, but it's running through a whole bunch of iterations. I can see we're 87ft away.

    So let's redo that with more generations, and see if we get a little bit closer. What I thought was really cool about this is the physics is like real, right? Because the geometry from your model is real. It was built in Revit to scale. So it just works. It's really cool in that sense. So I did here is you could do any type of computation by simply pulling out some of the information with the Data Exchange.

    So here if we increase the population to even 20, let's do more generations and add a little bit more randomness to the seed. You can see that we get pretty close. I was surprised to see that we actually didn't nail it. But I think that's just the steps that the sliders are on. We can't find that perfect combination of exit velocity, angle, and rotation. So maybe if we were to make the steps a little bit smaller, it would actually converge on a hole in one. But, you know.

    Yeah and then this is the random one doing a whole bunch. I will share that with you guys. So our outcomes here is we did get a bot that could start to hit a hole in one. A little bit more refinement would be great. Outcome number two is Brian was able to give me just the information he wanted to give, or could give because of IP reasons. So he was shielded from a wonderful lawsuit.

    And then you guys, outcome number 3 is I'm able to share that workflow with you guys. So that's in the downloadable files if you want to play around with it. See if you can tinker with it to get a hole in one. Or just make it be a video game.

    BRIAN NICKEL: And think just on a positive note, what was really nice about this example was just showing a fun way to use a Data Exchange using Dynamo. And a real world example of being able to protect your data from giving something that you didn't want to give to somebody.

    SEAN FRUIN: Exactly. Those IP issues. One of the bummers here is the idea that you couldn't run this through the Dynamo player. So feedback to the team, that'd be really nice to do. So anybody can get in there and run something really easily. And this goes back to the fire damper example too, right? To make it just a little easier so no one has to-- some people get scared of the Dynamo.

    BRIAN NICKEL: Yeah, so now we're going to dive into-- I'm going to go back one slide because it just advanced-- this is explaining Revit Conduit to Fabrication with Allied BIM. So what we're seeing up here on the screen is a CNC tube bender. In our industry, for conduit, typically, we're dealing with hand vendors, electric benders, and a hydraulic benders for bending conduit. And that takes about 15 minutes to an hour and a half to run the mathematics and to get everything staged up in order to fabricate pipe work.

    In this example, you can see that by connecting this machine we can now bend conduit in about 7 to 45 seconds. So as we step into this, we're going to recognize a problem. The problem goes back to the initial example that Sean showed where we've got this spaghetti nightmare of a mess of information. We've got the general contractor and the architect exchanging RFIs from plan sets, the models going through different disciplines, and there's no real clear trajectory or clear path out to the contractor, which costs a lot of money when we're building buildings.

    We can connect it into an assembly line for tagging and then ultimately shipping out to the job site, as well. By creating a data flow example here, and getting the data into a usable format, Forge allows us to actually extract and dissect the model to provide instructions on how to build and fabricate this detail. So we kind of see this as a building information model going into a cloud information model, which we'll talk about a little bit further as we advance down.

    This is an example of our software. So a user has taken basically an electrical model. They've published it into fabrication connected, which is our APS plug-in. They can dissect down into each of the assemblies. And we can see down in the lower right-hand corner that we're getting the total number of bends, the sizes, and the total lengths.

    One of the problems inside of Revit is that conduits are broken out as conduit fittings and conduits. So there's two categories inside of Revit. However, when a fabricator is fabricating, there's really only one category. Am I taking a 10 foot stick or a 20 foot stick when I'm actually building this thing? And so what our software does is it takes the data from the model through the Data Exchange and through APS, it allows us to create a new category on top of the Revit model to where we can actually get the accurate lengths and counts for what we need to fabricate.

    What I'm doing here is I'm storing all of the sizes in the web. So all of the sizes of our vendors are mapped here. We can run design automation, which allows us to extract the XYZ data of all the tube that's in the model. It's going to generate the category of Allied Conduits, which is going to give us the ability to drill in and export this data out to a CNC vendor.

    So, essentially, what we've done is we've ran the calculations on the model, we've extracted it, and we can actually do 3D conduit marking where we can actually visualize and plan layouts in a 3D environment. We can provide bend instructions on where they need to bend. We can visualize the three dimensional spools, and we can extract all the lengths and sizes that are required when we run this.

    And I believe this is looping now, so I'm going to advance to the next slide. So in here what we're doing is we're leveraging total stations and reality capture. So this is Bailey out in the field. Bailey works for Williams up in Bozeman, Montana. She's taking the data for all the points of the penetrations in a plumbing model. And she's laying out sleeves on a deck. Typically, if anybody's done layout before, this takes about four weeks if you're pulling a tape measure from each side of the building. It's also not very accurate.

    In this example, she's using the data from the model to actually lay out points. And she can lay out 850 plus locations in about four hours now using a GPS unit. So the time that this has eliminated by bringing in the robots for layout and the reality capture scan has expedited her layout process in this position. The next step is showing a real world example of us running the software from a building information model out to a machine.

    So in the picture on the top right or even top left where you're seeing the orange and blue saw, that's a fully automatic up cut saw that we can download all the linear lists of lengths onto that machine. It will dynamically pack and optimize all of the routines to eliminate the amount of waste on a project. So we can actually put all of our shortest material up front and longest material at the back. And it will process and cut from the model to the machine.

    Once it's been processed, on the top left, you can see that they're cutting and binning them up per assembly. Just like what we saw in that conduit model. There then, in the image in the center, prefabricating it on a table. And then in the third image on the bottom right, they're loading it up on a flatbed trailer and shipping it to the job site. On the image on the bottom left, that's the delivered materials.

    So you can see that the subcontractor has now been able to deliver all the materials before the floor joists is in. Let me tell you, when that floor joist is in place, some poor person, like poor like it's sad that somebody has to deal with this, but they have to crawl in and out of the crawl space to grab a fitting and to cut pipe. Can you imagine just like four feet across space, digging it, and having to go through and whittle yourself through the mud.

    The beauty is now we can actually prefab and stage it and ship it, so that some poor bastard doesn't have to go through the crawl space anymore to find a fitting. So this is just a real world example of how we're leveraging building information model out to a machine to optimize materials for construction. We're trying to eliminate 40% of what traditionally is wasted per build. 40% is due to just trade labor, trade shortages.

    So some of the outcomes of this. We're able to improve collaboration. We're able to increase the operational efficiency of the project, in terms of reducing and mitigating waste and increasing production. Instead of doing 10 assemblies a day, we can now do 20 to 80 assemblies a day. We've had customers or partners of ours that have basically built new warehouses to store more material because they're processing more material.

    On the image on the right, please scan that QR code. That QR code will open up an example of the model of the conduit that we went through. We envision this as being a way of communicating instructions to somebody without having to have an iPhone app. This allows us to improve the quality of design that we're producing. It allows us to reduce our environmental impact in terms of waste.

    We have pictures and data of 40% of building materials going into the garbage can because of just bad practice. There is no data informing the installation. So this allows us to streamline our Data Exchange. And it allows Allied to diversify our service offerings. We started in the mechanical and plumbing space. Now we're starting to work in the electrical space.

    So oh yeah, your backgrounds there. There you go. Yeah, there's the model on Sean's phone. So you can see that we're going to be getting things like bend instructions in there. We're able to communicate data off of a web browser without having to use a bunch of different apps is kind of the key point.

    SEAN FRUIN: I thought it was really cool, too, Autodesk was all about outcomes this year. And if we go back to those outcomes, I think this workflow with going from BIM model to some type of Data Exchange Forge, but soon to be Autodesk Platform Services and all that, it literally touches every single one of Autodesk's outcomes that they wanted to talk about this year. So improve collaboration, obviously.

    Increased-- you were talking about the efficiency gain, right? Better quality. I find it interesting too is like to do this though we have to get better at modeling, right?

    BRIAN NICKEL: Right. That's a really critical point too, Sean, that you bring up on the modeling for fabrication. Is that back in the previous example with the prefabbed Inventor room and understanding which tool is right for the job. What we're starting to realize is we can use the baseline of the model to inform decisions on the web. So we can start to blow the lid off the design model in a cloud information model.

    And this is what we see as one piece of Autodesk's future, right? And there's a big puzzle here because we've got all this software that's out there. But how are we leveraging the tools to inform the outcome of our production? Because someone still needs to be able to build. Someone still needs to be able to install this. What impact is that going to have using these tools to increase our production in the field and decrease our waste? It leaves a much more sustainable future for construction.

    SEAN FRUIN: And I just point out, it's about garbage in, garbage out. But if we take the time to actually model stuff in the 3D metaverse environment, right? Yes, it takes more time to get that modeling done perfectly, but look at what we could get on the other side of that.

    BRIAN NICKEL: Exactly.

    SEAN FRUIN: Yeah, it'll be interesting. Yeah, so let's dive into the how Data Exchange is just one piece of this bigger puzzle with Autodesk.

    BRIAN NICKEL: Yeah, so Data In BIM is not working. That's what we're kind of understanding here. Is that we're basically doing all this in our own individual silos. We've got this spaghetti nightmare of a mess of models throughout the industry that are being shared. And we're really just generating plan output. We're just getting permitted drawings.

    And one of the questions that I've been liking to pose lately is, if NASA can go model to machine in the 1980s, why hasn't the construction industry gone model to machine in 2023? And we all understand we need a permit set of buildings. But why are we modeling? What is the point of modeling if we're not going to use the model in the field? If we're not going to use the model for production, right?

    And so I think there is a wave coming where much like what NASA did the construction industry will experience that at some point in our future. And think Autodesk is enabling us to do that with these tools. So that's kind of my future outlook in terms of this. But Sean, what do you want to add to that?

    SEAN FRUIN: I'm just going to the next slide. Because I think Data Exchange gives us this middle ground of an idea. So what is that, right? What is this kind of way that we can write stuff and share it a little bit easier than either of these ways? A little bit more that's for automation, right?

    BRIAN NICKEL: Yeah, and Sean and I actually had a lot of conversations about this. And what we believe the answer is really a cloud information model. So it's this idea of basically taking all of these individual siloed data pieces from the BIM model, publishing it into the web, and using the cloud information model to do more. We're no longer limited by the design tool. We're now able to use additional tools at our arsenal to get the job done better. We can build it better.

    SEAN FRUIN: Yeah, I think we can go on to the next slide and we can articulate that even better. So fun. Right when I got this class selected, I saw the wonderful CEO on TV. And he was talking about after he got done with the Stock Exchange stuff, he was talking about AI and the opportunities of AI. But he really talked about how data is their first step in trying to get there, right? Again, back to the triforce idea, without data our AI doesn't do anything. And sharing data has been a huge challenge. So yeah, check out that one on CNBC if you want.

    BRIAN NICKEL: Yeah and think one additional note here real quick is that at AU 2022, Autodesk emphasized its commitment to unlocking data to dramatically improve collaboration. And so this highlights the recent-- the proof in the pudding is that the Data Exchange environment that we explored at in the beginning of this class is really proving that Autodesk is on this journey. And is doing something about it.

    SEAN FRUIN: Right and how Autodesk, I mean, how Data Exchange is just one piece of the puzzle. I don't know if you do a lot of work with Bird Tools. Awesome dude, Mesh. He's been my co-speaker before. But I had a conversation with him when I first got this class selected and we were talking. And this pretty much what he said. To summarize, there's been this talk from IOS about decentralized open data. And what does that really mean?

    That means that we're moving from elements in all of our information not being in these big files again, but rather than being in a database. And the Data Exchange is really the first step. Because it is the tool that lets us read and write from that cloud information model.

    BRIAN NICKEL: Yep. And we're seeing this in our software company through APS. APS has given us the ability--

    SEAN FRUIN: Right, there is also Speckle. Autodesk is not alone in this adventure. They're not the only ones with this idea. I think Speckle and Hypar, if you want to look into them, they are also pushing this idea of this cloud information model. And what it means to work in a cloud environment easily where you can pull in data from multiple sources and have this shared data interoperability environment. It's just really crucial.

    And again, it's a good opportunity for startups, people like you Brian at Allied BIM, even engineers like me like how can we leverage this stuff.

    BRIAN NICKEL: Yeah, and I think what's great about this is that the tools become authors into one central database. So we're leveraging the Autodesk platform services currently for our software company. And all of the data that our users are using are generally Revit models, Inventor models, or Power BI scripts and things like that. And it's all going into one shared database where we can start to use and inform model data to drive fabrication in our process.

    And others can use data, just like the other previous five examples that we went through in the class, each swim lane is now clear and open for us to navigate through. So that's really the beauty of this is that the tools are becoming authors for one centralized database. And what we really want you to do is just to imagine the possibilities, right? Imagine the possibilities.

    SEAN FRUIN: Yeah, so this was a cool workflow. Not done by me but again done by the wonderful platform services team. But this one shows you of the Shodan Tower. It actually has a different facade on here. So the geometry from the Shodan Tower was brought into Rhino. They were able to build this different type of facade through some type of algorithm. And then bring that back into Revit.

    What I imagine laying on top of this is we're able to then leverage it to do quickly do like load programs. So like the mechanical engineer can then go and run loads. Hey, what does this do to energy? So we're able to share this information and have pieces of the model just in the cloud, and pull what we need, and iterate a lot faster, than again sharing the bulky files. It's a really cool opportunity I think.

    BRIAN NICKEL: I really like this example too because we've seen a lot of glass companies, like aluminum window and door companies, basically take models like this and generate it. And then they're leveraging tools like what we showed for mechanical, electrical, and plumbing. But they're using it for the facade systems. And they're prefabricating and actually manufacturing directly out of the Revit model, the initial design, loading it into a more rigorous program for calculating their bill of materials. And then leveraging that to drive fabrication in their facilities.

    SEAN FRUIN: Imagine even getting costs then, right? So like, hey, how much does this facade design cost? Then you could really optimize for-- and then go to the building owner, right? Look it. We have this cloud information model. Option one cost. We already talked to our fabricator. He has it. Oh, you want to tweak this a little bit? OK, quickly go to the fabrication, you're like with your guys' tool, does a lot of calculations to get to that cost point at the end.

    BRIAN NICKEL: Well that's one of the other big realizations here as well is that a lot of our data is generally exported from the model, and it's not being connected from the model. And so the beauty of these data exchanges and the way that this software is working now is we can actually digitally connect the model data to another database instead of exporting it and detaching it and creating all these independent silos.

    So that's something that's really, really critical here. And Sean put together this really great example of how cloud information modeling is out for the MEP Track. So Sean, why don't you go in and explain some of these classes.

    SEAN FRUIN: Yeah, so there's a lot of classes this year at AU. And I just encourage you guys to think about, as you see these different workflows and everything, how could this maybe work with Data Exchange? What information is coming into these workflows? What information is going out? So we have one on just the loads, doing loads within Revit with the systems analysis.

    One on getting flow, so a lot of calculation stuff, getting flow through fabricated parts. Pretty crucial for sizing. One on just doing a quick analytical electrical load. There's a couple of third party softwares to even do engineering stuff, calculations, and all that. Kind of managing, again, this idea of cloud databases there. Creating your own customizable database. And then David Butts, the all-star, is just looking at how do these pieces fit together just within Revit. And then there's a class, too, on actually creating data exchanges with the SDK. So something more of like what Brian's doing.

    BRIAN NICKEL: So what are the rippling effects of all this really is one of the key things. And Sean, why don't you dive into the rippling effects.

    SEAN FRUIN: Yeah, I'll just say, again, as we're going through this class and I'm thinking about all this, what does that mean for your workflows? Is it different? For example, the fire-rated walls. How is that workflow different? What does it involve to set up those Data Exchanges? What does this mean for communication? Then you get to the communication part, it's like what does this do to contracts to execution plans?

    Is a BIM-- is a kickoff meeting now setting up a whole bunch of Data Exchanges? Just having that conversation and quickly setting them up, right? That conversation with the fire-rated walls. And then Brian I got to give you the last one because you said it best.

    BRIAN NICKEL: Yeah, with the dollars. Are you are you talking with the dollars? Yeah, so one of the big questions is, how much does all this cost? As we're looking through this we mentioned that the Data Exchanges are free. But I was talking to a really good-- I'm going to do a shout out to Amy Marks. She's a big colleague of mine and we know her originally she was with Autodesk as the queen of prefab.

    She and I were having a conversation about what impact do all these tools have on our industry? And the way she said it that really resonated with me was that Autodesk is becoming much like the iPhone in regards to the apps, right? Apple has an Apple store where all these apps are available. And so now all these developers are coming in and you can build an app for the Apple App Store, but the iPhone is what runs it, right?

    And so that's what we're seeing with Autodesk. Users are coming in with solutions just like ourselves, and we're writing applications to solve problems in our industry. And we're just marketing those on the App Store, right? And that's really the beauty of this. And we think there's more of that to come.

    SEAN FRUIN: So again, there is more to come, at least from my perspective with Data Exchange even. So I just became a product owner. The best thing that product owner needs is feedback. So I have given some feedback to them, and a lot more to come. But they really need to get it from the community. So please try these out, hop on the forum. This is Philip from-- he's the product owner over there for Platform Services. So he's looking for feedback. A lot more to come. A lot more iteration.

    So exciting times to shape the future, this next evolution. So again, I hope this next evolution isn't so suckiness, right? It doesn't suck as bad. And I'm going to call it The Interoperability Age. We have the Cloud Age but we weren't really able to exchange data. This next one is the next level where we're able to exchange data easily. And what does that do, right? And hopefully with that, we can conquer this Data Exchange. Get that third piece of the triforce. And really conquer our workflow flows and take over the world.

    BRIAN NICKEL: And Sean and I just want to thank everybody for your time today. And we're really excited this year for AU. And so we wish everybody a safe and happy AU. And thank you very much. You can contact and reach any of us on LinkedIn, as well as through the Autodesk app as well. So thank you.

    SEAN FRUIN: Yes, thanks, guys.

    ______
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    We use Dynatrace to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Dynatrace Privacy Policy
    Khoros
    We use Khoros to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Khoros Privacy Policy
    Launch Darkly
    We use Launch Darkly to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Launch Darkly Privacy Policy
    New Relic
    We use New Relic to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. New Relic Privacy Policy
    Salesforce Live Agent
    We use Salesforce Live Agent to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Salesforce Live Agent Privacy Policy
    Wistia
    We use Wistia to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Wistia Privacy Policy
    Tealium
    We use Tealium to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Tealium Privacy Policy
    Upsellit
    We use Upsellit to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Upsellit Privacy Policy
    CJ Affiliates
    We use CJ Affiliates to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. CJ Affiliates Privacy Policy
    Commission Factory
    We use Commission Factory to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Commission Factory Privacy Policy
    Google Analytics (Strictly Necessary)
    We use Google Analytics (Strictly Necessary) to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Google Analytics (Strictly Necessary) Privacy Policy
    Typepad Stats
    We use Typepad Stats to collect data about your behaviour on our sites. This may include pages you’ve visited. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our platform to provide the most relevant content. This allows us to enhance your overall user experience. Typepad Stats Privacy Policy
    Geo Targetly
    We use Geo Targetly to direct website visitors to the most appropriate web page and/or serve tailored content based on their location. Geo Targetly uses the IP address of a website visitor to determine the approximate location of the visitor’s device. This helps ensure that the visitor views content in their (most likely) local language.Geo Targetly Privacy Policy
    SpeedCurve
    We use SpeedCurve to monitor and measure the performance of your website experience by measuring web page load times as well as the responsiveness of subsequent elements such as images, scripts, and text.SpeedCurve Privacy Policy
    Qualified
    Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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    Improve your experience – allows us to show you what is relevant to you

    Google Optimize
    We use Google Optimize to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Google Optimize Privacy Policy
    ClickTale
    We use ClickTale to better understand where you may encounter difficulties with our sites. We use session recording to help us see how you interact with our sites, including any elements on our pages. Your Personally Identifiable Information is masked and is not collected. ClickTale Privacy Policy
    OneSignal
    We use OneSignal to deploy digital advertising on sites supported by OneSignal. Ads are based on both OneSignal data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that OneSignal has collected from you. We use the data that we provide to OneSignal to better customize your digital advertising experience and present you with more relevant ads. OneSignal Privacy Policy
    Optimizely
    We use Optimizely to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Optimizely Privacy Policy
    Amplitude
    We use Amplitude to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Amplitude Privacy Policy
    Snowplow
    We use Snowplow to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Snowplow Privacy Policy
    UserVoice
    We use UserVoice to collect data about your behaviour on our sites. This may include pages you’ve visited. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our platform to provide the most relevant content. This allows us to enhance your overall user experience. UserVoice Privacy Policy
    Clearbit
    Clearbit allows real-time data enrichment to provide a personalized and relevant experience to our customers. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID.Clearbit Privacy Policy
    YouTube
    YouTube is a video sharing platform which allows users to view and share embedded videos on our websites. YouTube provides viewership metrics on video performance. YouTube Privacy Policy

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    Customize your advertising – permits us to offer targeted advertising to you

    Adobe Analytics
    We use Adobe Analytics to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Adobe Analytics Privacy Policy
    Google Analytics (Web Analytics)
    We use Google Analytics (Web Analytics) to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Google Analytics (Web Analytics) Privacy Policy
    AdWords
    We use AdWords to deploy digital advertising on sites supported by AdWords. Ads are based on both AdWords data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AdWords has collected from you. We use the data that we provide to AdWords to better customize your digital advertising experience and present you with more relevant ads. AdWords Privacy Policy
    Marketo
    We use Marketo to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. We may combine this data with data collected from other sources to offer you improved sales or customer service experiences, as well as more relevant content based on advanced analytics processing. Marketo Privacy Policy
    Doubleclick
    We use Doubleclick to deploy digital advertising on sites supported by Doubleclick. Ads are based on both Doubleclick data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Doubleclick has collected from you. We use the data that we provide to Doubleclick to better customize your digital advertising experience and present you with more relevant ads. Doubleclick Privacy Policy
    HubSpot
    We use HubSpot to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. HubSpot Privacy Policy
    Twitter
    We use Twitter to deploy digital advertising on sites supported by Twitter. Ads are based on both Twitter data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Twitter has collected from you. We use the data that we provide to Twitter to better customize your digital advertising experience and present you with more relevant ads. Twitter Privacy Policy
    Facebook
    We use Facebook to deploy digital advertising on sites supported by Facebook. Ads are based on both Facebook data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Facebook has collected from you. We use the data that we provide to Facebook to better customize your digital advertising experience and present you with more relevant ads. Facebook Privacy Policy
    LinkedIn
    We use LinkedIn to deploy digital advertising on sites supported by LinkedIn. Ads are based on both LinkedIn data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that LinkedIn has collected from you. We use the data that we provide to LinkedIn to better customize your digital advertising experience and present you with more relevant ads. LinkedIn Privacy Policy
    Yahoo! Japan
    We use Yahoo! Japan to deploy digital advertising on sites supported by Yahoo! Japan. Ads are based on both Yahoo! Japan data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Yahoo! Japan has collected from you. We use the data that we provide to Yahoo! Japan to better customize your digital advertising experience and present you with more relevant ads. Yahoo! Japan Privacy Policy
    Naver
    We use Naver to deploy digital advertising on sites supported by Naver. Ads are based on both Naver data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Naver has collected from you. We use the data that we provide to Naver to better customize your digital advertising experience and present you with more relevant ads. Naver Privacy Policy
    Quantcast
    We use Quantcast to deploy digital advertising on sites supported by Quantcast. Ads are based on both Quantcast data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Quantcast has collected from you. We use the data that we provide to Quantcast to better customize your digital advertising experience and present you with more relevant ads. Quantcast Privacy Policy
    Call Tracking
    We use Call Tracking to provide customized phone numbers for our campaigns. This gives you faster access to our agents and helps us more accurately evaluate our performance. We may collect data about your behavior on our sites based on the phone number provided. Call Tracking Privacy Policy
    Wunderkind
    We use Wunderkind to deploy digital advertising on sites supported by Wunderkind. Ads are based on both Wunderkind data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Wunderkind has collected from you. We use the data that we provide to Wunderkind to better customize your digital advertising experience and present you with more relevant ads. Wunderkind Privacy Policy
    ADC Media
    We use ADC Media to deploy digital advertising on sites supported by ADC Media. Ads are based on both ADC Media data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that ADC Media has collected from you. We use the data that we provide to ADC Media to better customize your digital advertising experience and present you with more relevant ads. ADC Media Privacy Policy
    AgrantSEM
    We use AgrantSEM to deploy digital advertising on sites supported by AgrantSEM. Ads are based on both AgrantSEM data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AgrantSEM has collected from you. We use the data that we provide to AgrantSEM to better customize your digital advertising experience and present you with more relevant ads. AgrantSEM Privacy Policy
    Bidtellect
    We use Bidtellect to deploy digital advertising on sites supported by Bidtellect. Ads are based on both Bidtellect data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bidtellect has collected from you. We use the data that we provide to Bidtellect to better customize your digital advertising experience and present you with more relevant ads. Bidtellect Privacy Policy
    Bing
    We use Bing to deploy digital advertising on sites supported by Bing. Ads are based on both Bing data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bing has collected from you. We use the data that we provide to Bing to better customize your digital advertising experience and present you with more relevant ads. Bing Privacy Policy
    G2Crowd
    We use G2Crowd to deploy digital advertising on sites supported by G2Crowd. Ads are based on both G2Crowd data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that G2Crowd has collected from you. We use the data that we provide to G2Crowd to better customize your digital advertising experience and present you with more relevant ads. G2Crowd Privacy Policy
    NMPI Display
    We use NMPI Display to deploy digital advertising on sites supported by NMPI Display. Ads are based on both NMPI Display data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that NMPI Display has collected from you. We use the data that we provide to NMPI Display to better customize your digital advertising experience and present you with more relevant ads. NMPI Display Privacy Policy
    VK
    We use VK to deploy digital advertising on sites supported by VK. Ads are based on both VK data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that VK has collected from you. We use the data that we provide to VK to better customize your digital advertising experience and present you with more relevant ads. VK Privacy Policy
    Adobe Target
    We use Adobe Target to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Adobe Target Privacy Policy
    Google Analytics (Advertising)
    We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
    Trendkite
    We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
    Hotjar
    We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
    6 Sense
    We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
    Terminus
    We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
    StackAdapt
    We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
    The Trade Desk
    We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
    RollWorks
    We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

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    Your experience. Your choice.

    We care about your privacy. The data we collect helps us understand how you use our products, what information you might be interested in, and what we can improve to make your engagement with Autodesk more rewarding.

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    Explore the benefits of a customized experience by managing your privacy settings for this site or visit our Privacy Statement to learn more about your options.