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How to build data-sharing habits to Accelerate Digital Business:Saipem case

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说明

For decades, business leaders have shied away from sharing data, not just in regards to competitors, but even with internal stakeholders in other business units. This mindset is starting to shift, however, as studies back up the benefits of sharing data. Data sharing is a business-facing key performance indicator of achieving effective stakeholder engagement and providing enterprise value. Chief Data Officers who have successfully executed data-sharing initiatives in their organizations are proven to be more effective at showing business value and return on investment from their data analytics strategy. This class will highlight how the Saipem data sharing solution, based on Autodesk Forge and BIM360, brings more value to stakeholders from analytics initiatives, facilitates new way of thinking and points out how CDOs that embraces the new mindset around data-sharing will play a more strategic business role.

主要学习内容

  • Enable efficient cross-team collaboration strengthening teamwork
  • Facilitate knowledge and information sharing
  • Increase business execution and reduce rework
  • Enhance workflows to make every day’s tasks easier

讲师

  • Luca Bazzocchi 的头像
    Luca Bazzocchi
    Consulting Services Senior Manager and proud Autodesker since 2008! Team leader, always excited to meet dynamic and creative thinkers, I am enthusiastic about shaping innovative solutions. I received his Computer Science bachelor's degree from University of Genova, Italy. Also started to work with Forge in the early days, and have been working with the platform for the past 7 years.
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Transcript

LUCA BAZZOCCHI: Hi, everyone, and welcome to this Autodesk University 2022 class where, together with Alex, we will present how Saipem builds data sharing habits to accelerate its digital business. This class will take a little bit more than 30 minutes, and it consists of five main topics. We begin with a brief introduction of ourself and the reminder of the learning objective for the session.

We then give you an introduction on who Saipem is, what its business is, and how they decided a long time ago to innovate with Autodesk. We then jump into the challenges and objectives driving to the initiatives taken. And then we dive into the solution itself, starting from an overview and then detailing the main enhancement and capabilities that were developed after the last AU class presentation a couple of years ago.

We consider those enhancement-- those that might be of interest for you and that can be applied to your business as well. No matter which industry you are focusing on, if it is oil and gas or any other one. We close then this presentation looking at the future, and we will complete it with some final thoughts.

So I'm Luca Bazzocchi. I'm a senior manager in EMEA Autodesk consulting practice. I joined Autodesk a long time ago now. It was early 2008. And I joined as a technical leader, became then a solution architect, and since few years, I am managing-- I am a manager leading a team of people that I really like to define as rockstars in their role.

They are solution architects and business consultants. A team who is relying on values such as diversity, inclusion, and collaboration with a strong feedback culture that welcome different point of views. And those are all the strengths that we have, and most-- I mean, the most important strengths we have, and that allows us to work with many named account customers, such as Saipem.

I started to work with Saipem more or less 10 years ago when we started to look at how to improve some workflows, some processes, and how to adopt in the best way possible our products. Then when Saipem started to adopt and implement solution based on Forge, I started to follow all those initiatives. And more in general, I started to work with Forge when it was even not called Forge. It was the Large Model Viewer APIs back in 2016. And since then, I have delivered more than 25 successful projects around the globe.

ALEX GIOSO: Everyone, I'm Alex Gioso. I'm a fabrication and construction system manager in Saipem. Since 2010, I started working worldwide to spread the implementation of our in-house tools. So our in-house tool, our management construction information system tool. And I contributed to the growth of our in-house platform. During the last few years, I was also involved in the Saipem digital transformation, and I started the collaboration with Autodesk in the 2018.

Now, let's come back to the class. In the last two or almost three years, we learned a new way of work-- the smart working. The pandemic forced us to stay at home, forced us to develop a new habit, forced to find a new way of business. Probably we already started this change before, but for sure COVID-19 speed up this process. These are kind of evolution, let's say.

As you can see in the slide, the main goal is to improve the collaboration, enable efficient cross team collaboration, strengthening the team work. So create a collaborative culture where employees work together to achieve a common goal while taking responsibility for direction or encouraging a sharing culture or creating, sharing spaces for knowledge sharing, formalizing a knowledge management process, or better, leading by example. So what do we have?

We have to practice what we preach. We have to provide training, create a quality process, again, provide training, prioritize task, and yes, again, one more time, provide training for increased business because we think that who is more well trained has more opportunity to do things right the first time.

So communication. Communicate clearly, get organized, and define the difficult tasks. So again, the word is collaboration. Create more sustainable processes and results and increase operational efficiency. Before to move up to the solution, we have a quick look or a quick overview of my company. As I said, I started in Saipem 12 years ago.

And as you know, we are an oil and gas company, but we'd like to define us also as an advanced technological and engineering platform for design for the construction and for the operation of complex, safe, and sustainable infrastructural plans.

My division is the onshore division, the onshore ENC division, and our ambition can be defined as three-fold. Becoming the partner of choice for a client committed to do low carbon energy transition, transformative digital innovation at scale towards carbon neutral operation along our entire APC value chain.

I just spoke about onshore ENC division, but here you can see the pillars, instead, of Saipem. So an asset based solution that is our traditional core business. So offshore ENC division and offshore drilling. The energy carriers. So my division, as I said before, onshore ENC division. Robotics and industrialized solution.

So they answer to the needs for the energy transition. And here are involved with both the reserves, so onshore and offshore. And then the last one is the infrastructure division that has a lot of experience is safety, reliability, and technology applies to infrastructure. In the last slide of my introduction of my company, you can see a summary with some number about us, about our business. And we can see with Luca how it works, the collaboration with Autodesk.

LUCA BAZZOCCHI: Thank you, Alex. And I would like to underline one statement done by Alex concerning his work, one of his tasks at Saipem. He is working in the digital transformation, and I'm very happy to say that I was, as all Autodesk, involved in supporting this journey of this digital transformation. And how we did that?

We are supporting the innovation at Saipem as well. So let me just say that you all know who Autodesk is, and you know it, otherwise I would wonder why you are here. However, most of you have always seen Autodesk as a company who is providing you the right tools to do your daily jobs, your daily activities.

But you need to know that there is much more than that. As I told you when I presented myself, I am part of the Autodesk consulting practice, a group of experts, subject matter expert. Quite a big group of people based worldwide. And we can help you in adopting our products, in innovating with such adoption, and we can help you in finding the right solution to your problems.

What I would like to underline is how you can innovate without risk. Innovation is our DNA. There are no doubts about it. And Autodesk is a leader in innovation in whole the industry we serve. We are investing with patents. We are making significant investment in research and development over here. We have made significant investment in acquiring and developing innovative and new technologies such as PlanGrid, such as Assemble, Spacemaker, and most recently, Innovyze and [INAUDIBLE] just to name a few of them.

We have invested in using, learning, and adopting innovative methodology that has held us collaborating with some of our most progressive customers on game changing approaches to business transformation, and Saipem is one of them. We continue to do research in all the industry we are involved. AC, manufacturing, oil and gas as well.

You will see that we started with these initiatives five years ago, and with Saipem we started from a proof of concept that was just involving some Excel spreadsheet and some new emerging technologies. As I said, it was the Large Model Viewer APIs. And since then, we started to enhance and to adopt even newer frameworks and newer possibilities.

And we have now a full solution that you will see is going in-- it is used in production. What I would like also to underline are the technology opportunity that could really support your business. And those technology are-- you can see some of those, but we are not limited to those. The importance of data, we will see them during this presentation. The importance of data, the use of data, the quality of data, and the, really, benefit that you can achieve using those data in the right way.

The adoption of a platform for your business, business and services that you might have never thought of adopting. The importance of to improve and automate processes, looking at the future of work without forgetting the importance of sustainability. Even for those industry where-- I mean, that you might think that sustainability might not fit with. And we just had an example from Alex.

ALEX GIOSO: Thanks, Luca. So challenges, Saipem challenges too. During the last 10 years, our industry's productivity declined while project complexity, cost, and time to deliver increased. This is the reason for us to embrace transformative innovation in our strategy, reinventing internal processes to boost productivity, unlock efficiency, and create a new value proposition.

We'd like to define us as strongly committed to the client. So here, what our client value outside then can help so that's why I said our challenges. We imagine a future where the data sharing is at the core of the data driven culture. We imagine the data sharing, as illustrated. Try to picture with us.

What if all stakeholder had the access to the trusted data they need to do their job? What if there was a culture of openness and transparency amongst the teams department and line of businesses? What if you could produce new analytics reports and handle a doc request easily? What if you have the access to a single [INAUDIBLE] with the data you could trust?

What if you could expand the scope of the analysis that data consumers can conduct? What if you could explore new technology like machine learning, like artificial intelligence, and have access to advanced analytics and prescriptive data driven insight recommendation? There is a lot of question. We already reached some answers-- not all, but you know, that's our journey.

But for sure we said we know that the solution is only one, the data driven construction. That is our solution. So we think that to foster a culture that encourages data sharing instead of data ownership. That's the main goal, change the mindset is the big challenge and the big opportunity for growing and for getting new businesses.

As Luca said, we started our journey in 2017, all integrated Excel sheet. Then we moved during the year to connecting also in-house and commercial tools pretending always more. More analysis like workfront or feasibility. More disciplines. For example, we started only with the steel structure, then we added piping, then we added foundation, and so on.

More data. We connected more than 25 millions of element. In 2010, we also include BI dashboard-- enterprise BI dashboard. We start to approach the advanced work package methodology and we start to combine, to merge, to compare different models.

Last year, we finally find a solution. I will say find, and then later on you will understand why finally. Find a solution for the spooling and other analysis like the management of the punch list or the management of the test pack. Till today, well, the flag speaks. So we move the solution to the production, incorporated also the 4D simulation and the what if analysis.

It's also important to highlight, to underline that the solution lives in different divisions. In the 2017 and 2018, so in the first two years, only the offshore ENC division was involved. Starting from the 2019, also the onshore division. Also, my division will start to join the solution. And then from the 2020, we are working together. So now let's start to have an overview of the solution. Luca, off to you.

LUCA BAZZOCCHI: OK, so just to give you some-- to spoiler a little bit. The next set of slide will be a mix of standard slides and videos because we thought that looking at what we did and having the chance to see how our solution works will give you the better feedback, the best information you can get. So we start with this short video, which present the solution of a review.

Just remind that our goal is to provide all the users with a single place where they can ask all the information-- assess all the information they are looking for. So we can load multiple models in our 4d viewer instance. In this case, we do have piping systems and structural systems. We can search for the element that we want to focus on, as we are doing here, and just isolate those elements so that we can have a better analysis of them.

You see a lot of tabs, and depending by the selection of those tab, you can see that the teaming of the solution is changing. We have more information that we will see later on. But the idea here is that, depending by your area of interest, by your role, you will be able to see some of those information or all of them.

And last but not least, you can also integrate those views with the legacy BI system, such as Microsoft Power BI. So we are using dashboard that have been defined in Power BI and that were completely disconnected from the data and the model so that you can have a 360 overview of the information you are looking for.

ALEX GIOSO: So now we start illustrating all the enhancements adopted in our solution. Now this is just the list. Then we will go one by one to check all of them. So data analysis. So multiple analysis, multiple views, as I was saying before, like test pack management, like punch list management. Spooling.

So finally-- again, finally, we included the spooling information inside the 3D model. So after the spooling activity, we are able to get all the spooling information inside the 3D model . 4D sequencing for being able to intercept all the possible issues. And the what if simulation, so the forefather of the artificial intelligence. And the others.

Let's say less fashion enhancements, but very usual [INAUDIBLE] improvement. So let's focus now one by one, as I was saying. We continue to improve our solution integrating all the BI dashboards of our company and getting always more information. We started from-- as I say during the journey, we started from workfront analysis with progress, with quality, with the checking if one item, one object for one particular phase of work has been already tested.

So if the certificate is available or not in the quality purpose. Then we move and we add new analysis, like the welding. So any kind of weld detail. So the detail of the welds with all the attributes of the weld, the detail of-- the history of the weld. So all the information about the activities performed on the weld. We are able to track the spool.

So to track where is the spool getting information from our database, we know where is the spool and we are able to run some analysis on the spool. Punch list management, test pack management, and then the accounting for, again, let us know if one object, one item has been already paid or not to our subcontractor. Luca, up to you for the detail of the analysis.

LUCA BAZZOCCHI: Yeah. And let's dig into the data analysis capability. As we have briefly seen in the previous video, we do have several point of view, several types representing the different information a user might want to access. Here, again, we will focus on the same model we saw before, so structural model. And between all the elements loaded, we focus on a piping system.

So what we presented a couple of years ago, those were the forefront analysis, the progresses. So I'm able to select a specific element and see all the details of it. The quality we already added. So again, for the specific element, we can focus on the details and see how many tests have been run and if we have the certificates. And here is the new part, the welding.

Let's start from here. You see that we are focusing on-- we can represent where the weldings have been done. We see that in the properties, we can identify the welding number. In this case, we do have 5. And we can see all the information for each group of weldings. Then we can focus on the welding activities. So is this been completed?

Has this been still pending? Is this still to be started? And as you can see, we team each welding with a different color. So this one is a completed. This one is to be. And the next one, as you can see in this data set at the end of the element-- of the row if it is still pending. But from here, we can even go to the welding activities details.

Here we were focusing on the welding number 5, and for this one we can retrieve all the different activities done to complete this job. We can also easily export those information in an Excel spreadsheet that can be then shared with the colleagues, with the anyone who needs this kind of information. And in the Excel spreadsheet, we can have both the information as we see them here in the table or even the screenshot of what we see in the viewer.

We then have a punch list and test package which we don't have many, many data for, so we are skipping that. Spool. We will go back to the spooling later on in the next enhancement. And then accounting. We can see that this piping system has been already accounted, has been already paid. But we can also go back to the whole set of elements we add and focus filtering from Power BI on the elements that have not been yet paid and those that have been paid from a higher point of view. Not only on the specific element we filtered out, but on all of them.

ALEX GIOSO: Finally, the spooling. As I said from the beginning, finally because probably when I met Luca the first time, I spoke about this. So spooling, for us-- so I mean, including the spooling activity, it's a sort of dream for our construction manager. Why?

Who is working for oil and gas knows because a construction manager want to know what can be installed so he has to every time to check if something is missing. So if the joint is not yet welded or if some entities has to be performed, or for the engineer part, if they have to run some revision. They have to check if the fabrication has already started or not. So here we finally got the solution for getting the spooling formation and for reverting them inside our solution.

LUCA BAZZOCCHI: So here I would like to underline that we worked a lot also on the processes. Not that we never work on the workflow on the processes for the other functionalities, but here it was a little bit harder than in the other cases. And the way I would like to approach this enhancement is exactly from the process. So we have the 3D model, which is [INAUDIBLE].

We have all its metadata. And then with SPOOLGEN we can generate all the spooling metadata. The process in Saipem is well defined and well established. They create DWGs and PCF file. PCF file are-- you can see them as text files containing all the information related to the spools. But those are text files.

We had to find a way to integrate those information in 3D model, and here it came a third party solution based on Navisworks that allows us to load those PCF files and generate a model with all the information in an NWC file. An NWC file that is then loaded in Navis, as you can imagine.

You can see here this same model that now-- in the piping system you should be now-- you should know quite well. And here we will see we will have a lot of new metadata. In particular, this component attribute 29, which represents the spooling ID. If we search for the one containing S02, we will find this set of elements. And if we select one of them, we see that the component attribute 29 for this element, it ends by S002.

Then it comes the easiest part. We now have an NWC file, a model with all the metadata we are looking for. We store it in BIM 260 and then it is available in our solution, in the data driven construction solution. So here we see the piping system team according to the different status. If we search for the S02 spool ID, we identify the same element that we saw before in Navis. The status is that it's stored in the shop. And the first element that we see in the property, it is exactly the customer attribute 29. Instead of naming it the customer--

[CLEARS THROAT]

Excuse me. Customer attribute 29, we name it spool for a better user experience.

ALEX GIOSO: The sequences, for us, is the easy way-- the easiest way to get the possibility to intercept the potential issues, potential problem in materials and documents, planning, and in preparation through dedicated dashboard. And it's also a way for including 3D and 4D visualization from these different perspectives.

LUCA BAZZOCCHI: So since we said at the beginning that the onshore department is not the only one adopting this solution, so Alex's department is not the only one adopting solution, let's change model and let's look at an offshore model, a plan. Here the concept is very easy. What we wanted to do is to recreate the timeline that we have in Navis is our solution. So we are connected to a Primavera database and we are simulating and showing the sequencing and teaming the model accordingly to the values that we have in Primavera.

So everything that is yellow means that we do have ongoing activities for those elements. When they become green, that means that all the activities, all the construction activities, have been completed. The GAD can be changed from a granularity point of view. We can go from a single day simulation to a quarter one. Now we are focusing on months. We can speed up the step increase or slow it down. We can focus on a specific data range. But anything that you expect from a 4D sequencing tool.

ALEX GIOSO: As anticipated before the what if analysis, the what if simulation allows us to define several scenarios, starting from the lesson learning of previous project.

LUCA BAZZOCCHI: And here, again, we stay on the same offshore model with just so. The idea is that now we do have two instances of the 4G viewer so that we can have a side by side comparison between two different set of dates. As you can see, in the [INAUDIBLE] we do have a fat bar, which is the forecast data set, and the thin one, which is the planned one.

As you can see, we might have a problem in the forecast because there are pipelines that have been completely erected before the completion of the erection of the structure maintaining them. While on the right, in the planned, we do have the right sequence of events. So just to make this clear, we manipulated the data set to show these two different scenarios.

But at the end of this process, of this side by side comparison, the user can share and can suggest which is the best construction sequence to adopt. And then a last consideration on the less fashion, but still useful improvement. No videos here because some of them you have already seen it.

Since we presented the last class on the data driven construction two years ago, we invested a lot of time in increasing and changing the user experience. We want a much better user experience. We want the user to adopt and to use our solution and to find it comfortable to adopt it. Then we also did some changes in the background.

We adopted the latest technologies and frameworks, such as .NET core instead of [INAUDIBLE] .NET. We are using the latest Forge APIs available. But at the same time, we are improving performance in review-- performances, reviewing those algorithms that were taking some time to be completely performed.

We improved the coordinated integration with Power BI. We improved security aspects, such as we introduced the adoption of the OAuth 2.0 de facto standard, security standard even when we assess the databases, the different databases. And we made a lot of deployment consideration to support the expected adoption peak while going on production does this.

ALEX GIOSO: Almost to the end of our class. In this slide, we just mentioned some value gain. For example, reducing the risk, improves monitoring, we got the quality, the sped up decision making, et cetera. But it's not just a matter of improving activities or of getting the result. We found out-- we discovered that the solution is also satisfying Saipem people because they are more ready for collaboration.

And they stopped to say, I have to do something, but they started to do it to say, I want to. So we think that this is our best achievement. As for the value, also we put some examples of user's pol-- so some comments. We can have a look of some of them.

All employees, no matter the level, are gaining advantages thanks to the solution. But once again, the best ask or the best gift that the solution donated to us is the reaction that we get, the new mindset. So more collaboration, more participation, more involvement of the people in the solution.

We want to continue our journey, so we want to continue the collaboration with Autodesk. The next year, the infrastructural division will also join the solution. So for us, it is not over. Someone said that it's a long way to the top, but we know it. But we want to do it and we're down that way. And thanks, that's everything from our side.

LUCA BAZZOCCHI: Thanks a lot.

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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。