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Implementing a Facilities Data Spec with Autodesk Tandem

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说明

Firms use different facilities data standards for classifying building data and parameters used in the modeling process. Autodesk Tandem technology focuses on normalizing the data to meet owner requirements. As data is captured or created during a project, it will be normalized into the digital twin via classification schemes specified by the facility owner. Autodesk Tandem is establishing a workflow to specify the project's operational and data requirements in collaboration with the facility owner. This helps ensure that data is captured seamlessly during design and construction while the project team verifies completeness at each milestone. This enables a highly accurate digital twin that can be commissioned and handed over in an easily repeatable way. This class will focus on the specific data an owner needs to operate and maintain a building. We’ll discuss the workflows and questions to ask owner’s facility teams for the successful implementation of Autodesk Tandem.

主要学习内容

  • Learn the critical questions to ask owners’ facilities teams to focus on essential asset data needed for implementation.
  • Learn about how to simplify the handover process.
  • Learn about how to implement asset data requirements, establish responsibilities, and set dates for the data deliverable.
  • Learn about establishing standards for a connected building.

讲师

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Transcript

MARK MERGENSCHROER: Well, hello, everyone. Welcome to Autodesk University. I'm really looking forward to this class, looking forward to being together again. It ought to be a lot of fun.

Today, we're going to talk about implementing a facilities data spec with Autodesk Tandem, the new Digital Twin platform that Autodesk has, and we're going to talk about how to get data into that. I'm Martin Mergenschroer. I'm the customer success manager for Autodesk Tandem.

I really look forward to this class and seeing all the questions, being able to answer all the questions, and actually, show you what tandem can do for you as a Digital Twin. So let's start with our Safe Harbor Statement. Most of you have seen this.

We're going to be forward looking in our conversations today, so some of this may or may not show up in our software. What we are trying to do today is give you a good overview of where Tandem is going. So I am Mark Mergenschroer. I've been in-- most of you probably have sat through my classes before.

I've been at Autodesk three and a half years now. I've been in the industry for 27. I really focus on the owner's experience, and new technologies, and really, trying to educate owners on the Digital twin and what we can do for them to help them operate. My new role that I started this year is leading pre-sales opportunities with owners and operators, speaking at owner events, and developing case studies. And most of all, as most of you all know, Caleb's dad, my son is pretty special to me. He is the motivation for my passion in this industry, and I just love what I do.

I love being able to work with you all and everybody in this new venture of Digital Twins for me. So what we're going to-- our agenda for today is Digital Twin data with the opportunities and challenges of a Digital Twin, Digital Twin handover, and talking about the right data, and then a facilities data template with actionable insight. And we're going to show you a little bit about Tandem, the facility template, and the results that we can get out of the facility template.

So hopefully, we'll be able to touch on all of these to be able to get a good understanding of what's going on and just show you how Tandem is going to be brought into the industry and how we're going to turn a Digital Twin into an actionable Twin with actionable insights. So our objectives today is to learn questions to ask how to simplify the handover, how to make it smarter, understand how to implement the data, and even set dates for deliverables. And then I'm going to show you a workflow for a connected building, because we all look at incomplete data there.

We all get binders today delivered. We get we get thumb drives. But what if we took these binders, and really changed the way we thought, and actually turned this into a Digital Twin, taking all of our documentation, making it usable, making it smarter for the owner, so that they can better understand their data that they're receiving on a new building and then make it actionable?

So with this digital handover, Tandem is focus on normalizing the data for the owner. We want to figure out how to classify the data, so that we can transfer this data from architects, engineers, consultants, contractors, project managers, and hand that over seamlessly to a building owner, whether that's specifying it as part of a facility's data spec, as part of the building commissioning spec, or as part of the architectural engineering specifications. We want to be able to normalize that data, classify it, and then specify it for the owner's data and what the owner is going to require that's needed for operations. And as part of that, we want to make it a repeatable process.

We want the data requirements in collaboration with the facility owners, with the operations team. So a lot of times, we talk to the planning, design, and construction team early on in a project. But we don't talk to the facilities team to find out exactly what they need.

I was at a conference in Boston earlier this year, and I asked that question. I go-- it was at health care facility engineers. And I go, do you get talked to by the planning, design construction team and ask for your parameters, what you need to operate? And they said no.

So I asked the facility engineers, do you talk to the planning, design construction team inside your organization? And they're like, yes, we do, but we don't get our data. So what we're trying to do is work with everybody's team, the AEC industry, the owner industry, and come up with a new approach, so that we can start verifying the completeness of the data and making it repeatable.

So we want a highly accurate, repeatable process for the Digital Twins for Autodesk Tandem, and with that, we're going to get rich data insights. The owners is going to get specific data that they have requested, and this is the data they need to operate. It's not the data that we've given them in the past. This is a specific look at the data that owners need.

So during this class, or during this session, we're going to discuss workflows and questions to ask a facility owners team, or even a planning, design, and construction team as we bring them all together, so that we can successfully implement Autodesk Tandem on a project. And this all turns into handover. If we organize the handover, we're going to be able to start operations earlier than ever before.

Most of you have known me for the Arkansas Children's Hospital project, where we turned over data 92 days before they ever took ownership of that hospital. But during the ASHE, the conference in Boston I mentioned earlier, I asked the question to this classroom, about 250 people in the classroom. 34% said that they got their O&M's between zero and six months, 47% between six and 12 months, and 12 to 18 months was not 19%. So 66% of the owners in their buildings was over six months before they got their close out data.

Now, I know there's some legal issues here tied to payment, but we as a industry need to start looking. Our buildings are more technical. We've got more IoT, and we've got more smart items, smart things in it. We need to deliver these, our close out data, earlier than ever before to make sure that we get these buildings fine tuned for their operational efficiency.

So that's what we're going to talk about today. We're going to, hopefully, understand a little bit more about the Digital Twin process and in some of our opportunities and challenges. So the digital transformation is really data is becoming the key to our industry. It's becoming an asset into itself.

We've got the chillers. We've got the boilers. We've got the generators. But the data that is tied to those is becoming ever so valuable, and with this technology, it's becoming an unprecedented utilization of the data.

So what we're trying to do is to provide greater insights, so that we can make better decisions, so that owner can make better decisions about their building. And as Tandem has grown over the last year, and what we're seeing, who we're talking to, we are seeing that Digital Twins are being placed at the heart of business strategies. And really, people are starting to try to leverage this data in different ways before, and they're trying to innovate, and they're trying to grow their business. And we want to be able to help you do that.

So our Digital Twin is a digital replica of the built asset with a dynamic reflection of its physical self, with bidirectional connections between the physical and digital enable them to possess the operational and behavioral readiness necessary to simulate, predict, and inform decisions based on real world conditions. So we want to take the building, our delivery process, our hand over, and start using it for real world conditions in a easy, simplified, smart way. I believe we can do that as we start looking at who needs this data for the life cycle of the building.

We do planning, design, and construction really well. But what about building conditioning? What about all of that valuable data that a building commissioning agent hands over? What about space and planning? What about utilization of the building?

The building program, is it being utilized to its fullest extent? Predictive, planned, on demand maintenance, all of this can tie to make for better efficiencies in the building with the correct data, and then something we're going to start talking about in Tandem is data streams, performance monitoring and tuning that building with the actual automation systems data tied to the Digital Twin. And with that, that's going to give us more knowledge about our building and more knowledge about our campuses, our collection of buildings, our portfolio management, which, in turn, is going to help us better plan our portfolios. I got a mouthful there.

So then, in turn, it's going to help us decide better procurement options. Do we need to replace that boiler four years earlier for maximum operating efficiency? Those type of decisions are what we're looking for, and as the Digital Twin matures, we're going to be looking at the base level of a descriptive Twin, which is a digital replica. And then, as we move through informative and operational Twins, the sensor data comes into play, connecting that data up, and then predictives, we're going to be able to leverage all of the descriptive, all of the informative to make better insights.

Then the comprehensive, we're going to add simulation to the process and what if scenarios, and then autonomous, the ability to learn on behalf of the users. This is where we're looking at-- this is what Verdantix published in June of 2020. And Autodesk is following this business value and this Digital Twin maturity matrix. So hopefully, with what we're doing here, I love this image of where we've got a room.

We've got a ticket here that's too hot in that room. We've got a warning here that our pump has failed, and therefore, we know to go look at that air handling unit. Because all of our data is tied together from this digital handover, and it's connected to the operations systems. And that is going to allow us to improve efficiency, and hopefully, reduce cost in the long run. Because everything's tied together.

In talking to facility techs, they search for data five hours out of their day and only get to work for three hours. If we could change that, if we could flip those numbers around, how much cost, or how much could we save? But better yet, how much more could that tech do on maintaining the building, and what value can be placed on that? That's really a huge number there.

So specifying the right data with the Digital Twin Handover, I did this class in 2019, one of the best classes I've ever had at Autodesk University, and the discussion was about what data is needed, what data is required, who specifies that data, what are some questions I might ask, what are some samples that I can have to talk about. What we're going to talk about right here is things to make you think, things to make the architect engineer think, things to make the contractor think, things to make that building commissioning agent think, and also, as an owner, what do I want and to make them start looking at the process, so that we can start establishing a facilities data spec, a specification of information that will make our building smarter, our handover smarter, and hopefully, a better user experience for our owners. So what we're going to talk about, smart workflows.

I started working in BIM in 2006. I gave my first BIM to FM presentation in 2008, and we've talked about workflows and looking at workflows. Sometimes, I think we may have over complicated things. If we could back off and just look at what an owner needs for a handover, yes, we have to legally document things in a will. We have to legally document our design, and construction, and our handover. But what if we went that extra step and actually had a deliverable for operations through a smart workflow?

And what I mean by this is our industry, not all cases, but in most cases, our industry looks at this as one asset when, in turn, this is three assets. Because it's got three different asset IDs. So dashes and zeros make a difference, because in the world of owners, this would be three pumps. In the world that we're working in right now, in the data that we're seeing coming from models, this is three pumps.

We've got to work on our data nomenclature. We've got to work on our how we procure this data, how we look at this data. This is great data, the classification, manufacturer, model, serial number. And if you'll notice here, the one on the left over here is a Bell and Gossett pump. The one right here is a Taco pump, but we're looking at these as the same.

So let's start looking at the data and start cleaning it up. So what I really want to focus on is rich data insights to enable a smart workflow for operations, so that we can look at specific data that an owner needs. And it's actually physically specified by the owner's team. So it could be an addendum to a BIM execution plan. It could be in the specs of the design and construction team. It could be put upfront by the owner as part of their requirements, so there is a lot of different ways to handle this as long as we know the specific data for operations.

And then that data becomes more manageable, and it's not an overwhelming, unusable amount of data. Because one of the things in talking to facility owners over the years is they get handed a 900-megbyte O and M manual for 2,500 assets. That manual is, basically, worthless for them, because they've got to go cut that thing up and start making smaller ONA manuals to attach to the assets and the CMMS.

So we need to start looking and figuring out ways to help these facilities teams get this smarter building up and operating in its fullest potential, and we do this with specified data that's manageable and that's attainable, data that we can easily capture and know that that's the data that the owner wants captured. So that becomes the relevant data for turnover, and then what makes this all come together is it's trackable. It's used for insights.

As our picture here on the left, it's used for temperature sensors inside of our office building, so that we can see what's going on in this building. As you can see here in this building, the rooms against the windows, a little warmer than the interior rooms. This is the type of data we want to be able to see, track, and make better decisions about our building.

So I get asked the question, well, who is this for? Who are we trying to touch here? In a lot of cases, we have focused on the engineering and maintenance aspect of the workflows, and then we have focused on the consultants and contractors. But what we need to start doing is focusing on this middle, as well, because all of this needs the exact same data that the engineering and construction teams need.

So what are the questions we need to ask? For all of those, those stakeholders, for all of those owners, what is the use case for the data? What are we going to track? The who, what, when, and how, and why is what we want to ask for the data.

What is the model authoring tool? Hopefully, it's Revit. Is the asset being uniquely tagged? That goes back to that [? pump, ?] PnP-01, PnP-1. Where will the data be stored? What systems do you technically want to connect to the Digital Twin? What parameters?

Who's going to manage that Digital Twin? Is that going to be a Digital Twin consultant? Is that going to be the owner? Is that going to be the general contractor, a FM consultant?

We're starting to see a lot of different people being able to manage these Digital Twins. We need to know these questions and many more. This is just to try to get you thinking about what you need to ask and what we need to be looking for, for facilities data.

I had a list of about 30 questions that I asked my owners when I would go interview them, and it's amazing the comments that you get back. It's amazing the conversations that get started by talking and asking simple questions, and what that does is it opens up the conversation for deeper data structure. And that's when we really start understanding what a facility team needs for their specific building.

Now, as we start looking here, I get asked about, what parameters do I need to track, what do I need to look at? And as I've developed this over the years and start talking to facility teams and to asset management teams, some, they only want six or seven parameters. Others want 50 parameters. But start by asking the questions, what's the asset description, do you want the estimated life of the asset, do you want a photo of the asset, do you want a photo of the nameplate, room names, room numbers?

One of the biggest things we see is the room names and room numbers don't match the building plans and the models. We've got to get that fixed, so that we know what's going on with the parameters and getting them assigned correctly. In a space management, we're opening up an entire new world for space management. We're looking at properties, IDs, the same room numbers, space descriptions, the status of the parameters, departments, capacities, paint colors, flooring, square footage, the functional space. What zone is it located in?

Start asking these questions, so that we can get this data populated in a Digital Twin. Does that make sense? What do you think? Is this kind of the right way to start approaching some owner specific data as we move into a Digital Twin world?

Well, what we've got here is a smart building workflow. Yeah, this may not work for everybody, but it's a start. It's a way to look at specifying the data, how we get everything set up, how we break down the data, and then we start tagging the data. We start classifying it, and we capture the data input.

We capture the parameters. We capture the documentation. Here's something that our industry needs to really work on is validating the data, reviewing the completeness, reviewing the accuracy, making sure that what we hand over to the owner is actually what's there.

You may have heard me talk about this before. One of my projects, we got handed O and M manuals for belt drive air handling units when they were direct drive air handling units. All of the maintenance procedures were set up for belt drive air handling units and not for the direct dry ones, so we had data. We had some really good data, but the data didn't match. It wasn't complete. It wasn't accurate.

So little things like that can make a huge difference when it comes to warranties and how our owners operate, and sometimes, I think we may miss the little miss in the review and the completeness. And we really start to look at that, and with Tandem, we're going to be able to look at some of these parameters and start running reviews on some of this for completeness, accuracy, understanding what data needs to be there. So this will give us a hand over of a continuous exchange of data and the beauty of this.

This can start during design. It doesn't have to wait till three months before we open. We can even bring this in during schematic design, bring the models in and start looking at the process, start looking at the data, start specifying the data, find issues that are going to crop up, because we don't work in a perfect world. But if we can catch those issues earlier in the process, that's going to make for a better handover for our owner.

So Tandem has a facility template, a classification system, and this is the core to our transforming our data into actual insight. Because once-- so what we've talking about, we've talked about specifying data. We've talked about you normalizing data, and what this allows us to do is to build a classification system for your facility or for an owners facility. And thus, we start building a data dictionary of parameters and documentation requirements, and then, in turn, this allows us to have a facility template with the associated parameters, specific asset, specific space types, and actually, data stream types as we're going to be looking at in the future.

So on the right here, you can see we've got a classification of hydronic pumps. We can see that it's a Bell and Gossett. We can actually have links to our document libraries here. We've got the warranty expiration date, and then we've even got the flow rate, the volts, the currents.

We can even have the panel or the VFD for this pump in here. The data is all up to what the owner specifies, and we're truly trying to use this as a building block of information, trying to get this data normalized for whatever building type and push this data forward to make it useful. I mean, what we're going to do is we're going to capture this during design and construction.

We're not going to wait till we're at substantial completion. We're going to look at the design models. We're going to start looking at the construction models with Revit and IFC files currently. We're going to look at common data environments. Where is our data stored, and where is the owner going to store it?

Then we're going to look at the installation and commissioning data solutions, you know, Autodesk Build, even Excel, because we realize Excel is going to be used. It's a powerful tool that owners use, so we've got a way to quickly include data through Excel. And what we want to do is we want to automate the creation of this descriptive Digital Twin by using the model data, using the building commissioning data, and actually, moving that data into the Digital Twin. Basically, it's taking what we have wanted in the industry for years and moving it into operations in a seamless format.

So it's so much closer to where we've wanted it to where, as an industry-- now, I was in industry for 24 years. You know, it's what we've wanted. We're getting closer to where we need to be for our owner structure, and Tandem is really-- I've been excited. I got moved to this team earlier this year. It's going to help. It's going to be there for us.

So Tandem facility templates, so Tandem comes with facility templates out of the box, master format, unit format, different types of industry standards that we use in our everyday world of BIM. Now, what Tandem also allows us to do is we allow you to add your own custom classification system. So if you don't want to use the industry standard, we allow you to build that.

Data parameters, we come with a library of data parameters. We have worked with people in the industry. We've come with some of our experience. We've talked to owners. We've talked to contractors. We've talked to owner operators to come up with this library of intelligent data parameters, so that we give you a head start.

We don't make you create this from scratch. We actually give you some of the industry standard parameters that owners use, but what we also allow you to do is build your own custom parameters that are your parameters for your facility and managed how you want to do them. This adds a lot of power, because you may want parameters for, say, a dorm that are different from a classroom setting on a college or university. This will allow you to do that and specify that.

And then, as we do this, we're able to build facility templates, and we've got some sample templates inside of Tandem that are ready to go. You can see here, we've got a simple category one. We've got a master format, and we've got a unit class already pre-built into Tandem. But then, again, we also allow you to add a facility template to this process.

And then, if we set everything up correctly, then this is what we're going to get. We're going to have model data over here. We're going to have classification systems. We're going to be able to look at families. We're going to be able to look at spaces.

We're going to be able to touch this energy recovery unit over here and see that that's an energy recovery unit ER1 with heating and cooling. We're going to see that it's got a heating and ventilation assembly code, and it's classified as operation assets. It's a custom asset, or it's a custom classification that we built. And we see this categorized as mechanical equipment, and then all of the asset data right here, and then we can look at the link data as well. And then you can't see this right here, but we can actually have the space data for the roof right here as we go along.

So if we build this right, all we've got to do is start touching this data and moving forward with it. And a lot of people have ask, well, I don't understand Revit. I don't have Revit. The beauty of the Digital Twin is we've simplified the process of working in the model, of working with the data. That's why it's a touch and click, and we're able to access the data.

In the demo I'm going to show you here in just a couple of minutes, we will actually show you a little bit about how we have this model set up and how we can use the facility templates to create actionable data that's going to give us better insight. So we've talked a lot here. We've talked for the last 35 minutes on smart data, but this descriptive Digital Twin ontology is we want to know the asset system in space.

So if a VEV box goes out, we need to know what space is affected. We need to know what system that is, and we need it to go all the way back to the air handling unit. Or we need to know-- on a fire alarm panel, we need to know where the smoke is. We need to know what doors are going to shut in the event of a fire. All of this type of information, everybody talks about the water leaks. What if we actually had the valves in there, in their correct locations, and it's tied to the system, and we can actually go see the space that the valve is in?

We can actually do that representation, and how the systems relate to each other, that is going to be the power of the smart information data model or our descriptive Digital Twin. And hopefully, the outcome of Smart Digital handover is going to help us even become more transparent. You know, BIM has helped us work together. But what we truly need to do is accelerate the handover, so that we can accelerate the operational readiness of a building, so that we can provide the owner with detailed facility information faster than we ever have before.

With Tandem, I truly believe we can do that, if we set everything upright, if we look at the data that an owner needs. We're going to be able to accomplish this, and we're going to be able to change the way we hand over data to an owner. So real quick, we're going to go to a quick demo here, and you will notice-- whoops, let's see here. There we go.

As most of you all know me, I'm not the best with technology, but we're good. We made it through here. So here is our demo building, and as you can see here, we've got it set up. We've got items classified, and then we actually have our facility template set up. And you see here, asset data that I've got mapped, asset links, and space data down here as well.

So I can actually look at the actual room names, room numbers, floors, room departments. We can actually start looking at this data. And then, if we come here and click the Manage tab, we can actually get into our data. This is the screen you saw before, and then you can actually see some of the simple categories, unit class. And then this is my very simple operational data one that I have, your custom attributes, what you want to do.

Then, if we come over here to parameters, then here is that library of parameters that we have, and you can see here, we have a pretty robust list. We can sort by category, by data types, by context, even by name. So we give you the ways to filter this.

If we want to look for manufacturer, we can search right here and bring data up really quickly. And then, in turn, we're able to work with these facility templates and then actually specify what parameters that we want to use inside of each description inside of each code inside of each template. So you can see right here, I have different ones for the space management versus the asset management. You see here, there's completely the same or completely different. I'm sorry.

You all know me. I make mistakes. I can't help it. So this is what's really cool, so how do we use this? How do we make this work?

And what we can do here is we can come and touch this energy recovery unit, and this is the same one that we showed you before. Here, it's on the roof, and it's on the East roof. And then we actually can show the relationships, and then we also show the design properties of this asset. But what this allows us to do is organize the data in a way that the owner wants it, so that the owner can see it. And then you have the ability to set different views.

So if we want to see all of our assets right here, we can actually look at all of our assets, and we can start looking at data, working with data, and understanding data, and actually, turn off my ghosting here. And then we can run reports on the data just this fast right here by hitting export over here, and then we can grab this data out, start using it, start taking it to different ways. Now, one of the other things that we can do is we can actually set up facility tech views and start coming into the different rooms, start looking at different assets as part of this.

We can start looking at different systems, and we can look at the duct and piping system all tied together, color coded for the colors that we want. So using this data and starting looking at the information, by having smart data, it's going to allow us to start looking at the building as a hole, connecting the systems, connecting the spaces, connecting the data and allowing our owners to have more actionable insight, so that they can better manage their facility. Coming to Tandem, and working with the Tandem team, it's really opened my eyes to what we can do with a model, to what we can actually do with the process.

So as we move forward, feel free to email me questions. Feel free to ask anyone on the Tandem team about the facility templates, about the data, anything that you want to know. Feel free to come, and we'll be open and honest as we can with you about what we're trying to do, and how we're trying to move forward, and how we're trying to position in the industry.

So thank you for listening. Like I said, if you've got any questions at all, feel free to contact me, Mark Mergenschroer, Autodesk Tandem team, and thank you for your time. I hope you got something out of this. Please, leave feedback, and I hope you have a great day. Thank you.

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我们通过 Qualtrics 借助调查或联机表单获得您的反馈。您可能会被随机选定参与某项调查,或者您可以主动向我们提供反馈。填写调查之前,我们将收集数据以更好地了解您所执行的操作。这有助于我们解决您可能遇到的问题。. Qualtrics 隐私政策
Akamai mPulse
我们通过 Akamai mPulse 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Akamai mPulse 隐私政策
Digital River
我们通过 Digital River 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Digital River 隐私政策
Dynatrace
我们通过 Dynatrace 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Dynatrace 隐私政策
Khoros
我们通过 Khoros 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Khoros 隐私政策
Launch Darkly
我们通过 Launch Darkly 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Launch Darkly 隐私政策
New Relic
我们通过 New Relic 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. New Relic 隐私政策
Salesforce Live Agent
我们通过 Salesforce Live Agent 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Salesforce Live Agent 隐私政策
Wistia
我们通过 Wistia 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Wistia 隐私政策
Tealium
我们通过 Tealium 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Tealium 隐私政策
Upsellit
我们通过 Upsellit 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Upsellit 隐私政策
CJ Affiliates
我们通过 CJ Affiliates 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. CJ Affiliates 隐私政策
Commission Factory
我们通过 Commission Factory 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Commission Factory 隐私政策
Google Analytics (Strictly Necessary)
我们通过 Google Analytics (Strictly Necessary) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Strictly Necessary) 隐私政策
Typepad Stats
我们通过 Typepad Stats 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Typepad Stats 隐私政策
Geo Targetly
我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
SpeedCurve
我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
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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改善您的体验 – 使我们能够为您展示与您相关的内容

Google Optimize
我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
ClickTale
我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
OneSignal
我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
Optimizely
我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
Amplitude
我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
Snowplow
我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
UserVoice
我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
Clearbit
Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
YouTube
YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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定制您的广告 – 允许我们为您提供针对性的广告

Adobe Analytics
我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
Google Analytics (Web Analytics)
我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
AdWords
我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
Marketo
我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
Doubleclick
我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
HubSpot
我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
Twitter
我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
Facebook
我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
LinkedIn
我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
Yahoo! Japan
我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
Naver
我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
Quantcast
我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
Call Tracking
我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
Wunderkind
我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
ADC Media
我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
AgrantSEM
我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
Bidtellect
我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
Bing
我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
G2Crowd
我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
NMPI Display
我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
VK
我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
Adobe Target
我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
Google Analytics (Advertising)
我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
Trendkite
我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
Hotjar
我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
6 Sense
我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
Terminus
我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
StackAdapt
我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
The Trade Desk
我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
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

是否确定要简化联机体验?

我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

个性化您的体验,选择由您来做。

我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。