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How to Optimize Project, Design, and Risk Management with Autodesk Forge

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Description

Would you like to optimize project and design management? In this class, we’ll go through workflows and automated processes that enable us to track deliverables’ development progress and compare with schedule and budget more efficiently and accurately. We’ll work within the ISO 19650 framework and use some of the standards’ metrics in a set of dashboards to provide an overview of the sample project’s development. To analyze the data, it first gets exported from BIM 360 software and Revit files using Autodesk Forge software. All exported data, with schedule and budget, is imported into Microsoft Power BI. For contextualization, we also embed the Autodesk Forge Viewer into Microsoft Power BI, enabling us to dynamically switch between the project’s models and sheets corresponding to the data displayed on the dashboards. This approach to data and project management allows us to improve collaboration, enhance quality of deliverables, and reduce cost and risk. Throughout the course of two years, we predict saving $190,000 in one project alone.

Key Learnings

  • Learn about how Autodesk Forge is a powerful tool to manage your projects and access data.
  • Learn how to apply automated processes that increase productivity and quality, reducing costs.
  • Learn about adopting standardized processes and methods that allow for scalability and a higher ROI.
  • Learn about implementing workflows using standard formats and automated processes to satisfy your needs and improve your outcomes.

Speaker

  • Avatar for Anna Escolano
    Anna Escolano
    Anna Roig Escolano is a Senior Specialist in the Digital Twin Solutions group at Mott MacDonald with 10-years of experience working in the Virtual Design and Construction (VDC) and Digital Delivery field for the Architecture, Engineering, and Construction (AEC) industry. Anna is originally from Valencia in Spain. She studied Architecture and later obtained her Masters in Structural Engineering. She has experience in high profile projects and advanced knowledge in infrastructure design and construction. Anna is inquisitive about new applications and solutions with a passion for the future of AEC technology; a creative problem-solver with an interest in improving processes who encourages collaboration and knowledge sharing among team members. She has a strong technical skill set with a desire to expand workplace competencies and is very committed and self-motivated with the ability to push boundaries and to step out of the comfort zone. Now with Mott MacDonald Anna’s focus is on implementing new digital delivery technologies, developing custom made tools and optimizing digital processes globally.
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Transcript

Hi, everyone. Thank you for taking the time to watch this CAD recording. As you may already know, this class is called How to Optimize Project, Design, and Risk Management with Forge. And we're going to talk about an automated process that we developed using Forge to extract data from BIM 360 and Revit and visualize it in Power BI together with other data sources.

Before diving into it, I wanted to briefly introduce myself. My name is Anna Roig Escolano. I am originally from Valencia in Spain, but I'm based in New York City, and I am an architect and a structural engineer. At this point, I have been working in the VDC and digital delivery for the AEC industry for about 10 years, and I am currently a senior specialist in the digital twin solutions group at Mott MacDonald.

I also wanted to take a minute before we start the class to acknowledge the important role that Cory Dippold, vice president and head of the digital tool solution group at Mott MacDonald, and Rene Chicas, former project data specialist in our group, have played in the execution of this effort. Let's get into it.

Since you are in this class, I am assuming that you know the class description as well as the learning objectives, so I won't stop here. But if you can read them in the hand-- but you can read them in the handout if you're interested. The next section of this class is a description of the project that served as a case study for what we developed. The project is the restoration of a major linear infrastructure. The infrastructure is 92 miles long or 148 kilometers. It is over 100 years old, and it serves one of the biggest cities in the world.

Mott MacDonald provides condition assessment, detailed design, facility planning, and design services during construction. What did we need this project to be? We needed a project with a high level of digital maturity of big size and that was standardized. This project had a high level of maturity with a Common Data Environment and a Master Information Delivery Plan. It was a multi-year, multi-discipline project with lots of different models and deliverables and with 16 subcontractors. And it was perfect for scalability since it was following the ISO 19650 standards. All of this made this project a perfect case study for this effort.

That being said, what is the problem that we're trying to solve? It is common that the engineers don't have a lot of transparency into the detailed financials of a job specifically at the discipline level. They might have an overall view of the schedule and budget of the project and what their next milestone is, but they're missing if the deliverables are at the right level of development compared to other metrics.

On the other hand, the PMs rely on the technical capabilities of the designers to deliver a good quality product. Therefore, it is typical that they are focused on client care, ensuring deliverables are produced in a timely manner, that budget goals are hit, and things like that. Additionally, sometimes they don't have the software skills to review the models and drawings on a regular basis. So we could say that there is a disconnect between project management and design management.

That is the problem. So what is our goal and the solution to this problem? Our goal is improving the engagement of design management and project management by creating a unique environment where everybody, regardless of their role, can access complete and relevant information. We want to make the commercial side of the job accessible and meaningful to designers and models and drawings easy to view for managers and combine all of it in a single source for higher accountability and better understanding and communication.

In other words, we want to build an easy and intuitive common environment so that you don't need to, let's say, be a skilled Revit user to see the models or a professional scheduler to understand the timeline of the project. Those are the goals. So now we will get into the how we're achieving all of this.

As we mentioned earlier, the project is set up within the ISO 19650 framework. At Mott MacDonald, we pride ourselves in having used these standards shortly after they were first published and not widely known. For this project in which we have 16 different subcontractors, the benefits of using these standards go beyond controlling information properly. We are able to obligate our subcontractors to manage their information in the same way that we do which is very powerful.

Specifically, for today's topic, I wanted to point out the parameters associated to the contract drawings that we developed in Revit and what these parameters indicate. These are metrics that we have extracted from our models using the Forge API and then used to track the design progress. These parameters are suitability code which gives us an understanding of the maturity of the data and what this data can be used for. Some of the codes and their definitions would be S0 for initial version, S1 for suitable for coordination, S2 for suitable for information, and so on and so forth.

We also have state which provides transparency in the status of a file. For example, if it is a work in progress file, if it is a shared file or a published file. We also have implemented the recommended folder structure and the naming convention which provides a uniform approach towards information and data management. And finally, we added percent complete and hours to completion. These are not metrics that are in the ISO standards, but they give us metadata to quantify design progress.

In a digital delivery job, we start by setting up the digital delivery architecture map. This lays out the tools and systems where all the data resides and how the information flows from system to system. In the blue box, we have the project management tools and data. We use Deltek for the financials, Master Information Delivery Plan for deliverables, an Excel risk register and Microsoft Project for schedules.

As for the design management tools, we use Civil 3D and Revit as design tools to develop models and drawings and ProjectWise and BIM 360 Docs as document management tools. Traditionally, the project manager would also go through the effort of setting up the management visual tool that allows them to supervise project operations. For that we used Moata.

What is Moata? Well, Moata is the Mott McDonald's proprietary digital solutions platform. We have a number of solutions that can be used to solve any of the different problems that come up across the asset lifecycle. For this particular project, we use Moata Insights for project management and controls. I would like to show you how this looks like for us.

Once we log into our Moata hub, we can see all the projects we have access to under Moata solutions. Due to confidentiality reasons, I cannot show you the dashboards of the project subject of this class, but I will show you the Insights dashboards of the demo project which are very similar. As you can see, these dashboards are powered by Power BI. And in these dashboards, we track things like budget with a project cost summary, baseline versus actual cost over time, purchases of the project. We can also track schedule with a project schedule summary, and we track the deliverables, which in our project we do by using the MIDP, as we previously mentioned.

We also track risk with a risk register, and there's also, in this case, a graph that shows the risk mitigation effect. As you can observe on the left hand side, there are many other categories of information that can be analyzed and plotted on these dashboards depending on the needs of your project. All of that I just showed you comes from the blue box. We have not yet got into how the design is coming along. This illustrates the problem of lack of connectivity between project and design management.

How do we get all of the blue and red data into a single environment? We already got the budget and schedule data into an Excel format, as we can see in the blue box. We will extract the design metadata from ProjectWise to an Excel file using a PowerShell script. Revit is a bit trickier, as the metadata resides in the sheets within the files with BIM 360 as instance parameters. Here's where Forge comes in. We would automate the process of extracting the data from Revit files and BIM 360 by building scripts and using Forge to get all of the required information in an Excel file.

Now that we have all of the data in the desired format, we can compile the budget, schedule, and design data into a unique environment using Power BI. Ultimately, the compiled set of Power BI dashboards can be pushed into Moata Insights. For the automated workflow that we developed to extract the data from Revit and BIM 360, the main tools and technologies used are Forge APIs that enabled us to leverage the design and engineering data to develop a custom application and a connected workflow.

Programming, in this case, we have used Python, SQL Query, and JavaScript. And finally, Power BI. Before we move on I wanted to go briefly over the main reasons why we chose Power BI and not another visualization or business intelligence software. This is because Power BI, it is Mott McDonald's standard reporting environment. It is well known across all our project teams and allows us to compile data from any source as well as build into Moata Insights.

I am going now to show you how we have built integration of the project and design management metrics. The design management metrics are extracted from the BIM 360 and Revit files. You can see them on this table. These are parameters that come from the ISO standards. In order to do that, we have built an over 500 line Python script that in itself runs on SQL Query. And after we run it, we get a CSV file that looks like what we're looking at in the screen with the relevant metrics that we had just seen within the Revit file.

I'd like to show you the dashboards that we have created combining the data set that we just saw, that we exported and we saw in CSV format with the projects budget and schedule in Power BI. The first set of dashboards is the Revit deliverables versus schedule and budget. I would first like to bring your attention to the tables on the right hand side of the screen. One of them shows the list of disciplines, and the other one shows the list of Revit files for the project.

As you can see on the left side of the screen, we have three different metrics represented in these graphics. In green, we have the budget and actuals in dollar amounts. The gauge shows an available amount of 1.4 million and a spend amount of 860k. In pink in the central, we can see the time elapsed in days. The design phase of the project is two years which gives us a total of 720 days and an amount passed at this moment in time of 432 days. And finally, in blue, we can see the design progress in percentage. This is what we get from the parameters extracted from our Revit files and BIM 360.

I am sure you have also noticed the cards under each gauge these represent the percentage of budget used, time elapsed, and progress achieved so that we can compare apples to apples and determine if things are on track or we need to take actions. At the bottom, the same three metrics are represented in percentages over time. By default, everything is showing an oversight of the whole project by summing up expenses in case of the budget or averaging in case of the design progress.

But we can also, by clicking on the disciplines in the list that we have at the top right of the screen, we can isolate the information related to each discipline separately. This will also show us the models corresponding to that one discipline alone in the file name list to the right of that discipline list. How does this serve us? Let's take a look at the structural discipline.

As you can see in the percentage over time graph, the pink line, which represents time elapsed, is a straight line with a constant slope. Since time is not depending on any external factor, it is our reference to understand if we are doing good with budget and design progress. For budget, which is the green line, if the line is above the pink line, we're spending at a higher rate than what we want, see here, where budget use was 15% while the time elapsed was only 5%. And with the design progress, which is the blue line, if we are below the timeline, we are progressing at a slower pace than what we would like to, like here where we can see that the time elapsed is 45% while the design progress is only 40%.

As you can see, at the beginning of the project, the budget line is much more steeper than the design progress. This is normal and does not constitute a concern. Since there is a learning curve about the details of the project, the contractual documents need to be set up and started from scratch, and there's a lot of other tasks involved in starting a project that won't happen later on. But at some point, what we want is the spending rate to lower and the design progress rate to increase which is what happened here.

And now if we take a close look, we can see that at some point after the 30% design submittal, the design progress line, which is the blue one, falls under the desired percentage. That can happen, but after we kept getting further away from our goal, that raised a red flag. And we were able to bring the team up to speed short before the 60% submission to deliver a good quality and complete set of drawings.

Now let's take a look at the architectural discipline. The same thing happened at the beginning of the job. The budget line was increasing faster than the design progress one, which is normal, but the difference here is that at the time that it should have gotten softer, it did not. At this point, we were clearly overspending. Thanks to these dashboards, we were capable of noticing these soon enough to do some damage control and flatten the curve. We were able to manage the deliverables without having to discuss a change order with the client or losing money on the job.

As you can see in the cards up top, we finished the 60% deliverable with a budget spent of 70% which gives us a delta of 10% over spend. This is not ideal, but it is very good considering the delta was 28% at some point and is good enough to be able to continue closely controlling the expenses and make it up throughout the upcoming design phases. The other set of dashboards that we built are the models and contract drawings, visuals and status.

For these, we have one tab per discipline. We will take a look at the structural one. For contextualization purposes and to provide an easy visualization of models and drawings, we have also embedded Forge Viewer into Power BI, allowing us to dynamically switch between the models and sheets corresponding to the data displayed on the dashboards. In this case, this was achieved by using JavaScript.

As you can see on the screen, the model can be rotated. You can zoom in and zoom out. You can explore it, and we can also switch views. As you can see, we're going to now switch to a different view, and we can click on the elements and open up the property pane so that we can take a look at the metadata associated to these elements for review all from the Power BI interface.

In our case, we have customized the list of models that we can view to show the existing plus demo, the existing plus proposed we were just looking at, and in this case, the complete proposed model with all the disciplines on for completion. In every model page, we have on the left side up top the navigation buttons. Each one brings us to a different project model.

We also have three cards with the state, design progress, and stability code for each model. And at the bottom of these cards, the list of sheets developed in the model that we're looking at. In the middle, we have the Forge Viewer, and on the left side, a plan view that works like Google Maps with the exact location of each asset.

Right now we're going to look at the last model in this example. And here we can observe the accuracy in the representation of the asset. If we compare the proposed model with the satellite view on the right, we can see that they're very close. We can also see how the sheets that we can access through the document browser match the sheet list on the left side of the screen and how we can take a look at them from the same Forge Viewer without the need to know how Revit works or having to open each model individually or printing out PDFs or anything like that.

After having looked into what we have achieved so far, I would like to go over some of the benefits that we have estimated. The effort of the team to get to where we're at was equivalent to one person working full time for 13 weeks. That is 520 hours or just over $57,000 at an average hourly rate of $110 an hour.

For this project, we were having two-hour weekly meetings with all eight disciplines and the project management. This is roughly 20 people. We estimated that the time saved with these dashboards in these meetings would be a half hour per week conservatively. That makes up to 10 man hour every week. At the average hourly rate of mid senior engineers, that is close to $2,400 saved every week. Therefore, if we do the math, in 24 weeks, we would make up the investment.

The life of the design phase is two years. So the estimated long term benefit considering the investment is of almost $190,000 on this job alone. Because we have implemented the workflow in a very scalable way and developed it within the ISO framework, it can be applied to any project, especially if they're following the same standards, making the financial benefits exponential. For example, applying it to 10 projects of a similar size over two years, we would have close to $2.5 million in savings.

This graphic shows a summary of what we just discussed, and it makes it pretty obvious. So the green bar is the initial investment. The purple one is the long term savings over two years for this one project, deducting the initial investment. And finally, we have the orange bar, which shows us the savings that we would achieve over two years for 10 projects of a similar size.

Other benefits less easy to quantify in a monetary way or savings way are reduced risk at missing deadlines, which we just saw with the structural example; reduced risk of overspending, which we also saw with the architectural example; higher client satisfaction; higher deliverables quality; higher accountability; and better data management and oversight. This solution gives us a much higher degree of control of the project. The amount of problems avoided down the road is incalculable. Overall, what you get is proactive project management rather than reactive troubleshooting.

One thing before we finish is that I would like to give you an overview of our next steps. We would like to add quality management and risk and safety to the created integration of project and design management. We are working on linking the risk register with the models. The idea is to click on a specific risk on the table in Power BI, and the Forge Viewer would bring you to the location of that risk in the model.

We also want to reflect the checked and approved workflows specified in the ISO 19650 at different levels of granularity in our dashboards. This would allow us to see the 3D objects, models, and drawings have been checked and/or approved. The way we envision this is by color coding the objects depending on their status. We can also check percentage of objects that have been checked and/or approved in a model or per discipline.

I would also like to mention other initiatives that are happening at Mott MacDonald and that are in the line of what we have seen today. My colleagues at Mott MacDonald, Paul and Fouad, have created Power BI dashboards using Autodesk Assemble for validating and checking model content against project standards. Their class is called Assemble plus Power BI, Developing a Powerful and Practical Model Checking Tool. If you're interested in learning these other automated processes that can help you automate quality control, especially in high volume projects with quick turnaround, you can check the recording using the class number in this slide.

And finally, I would like to summarize what we have learned today which is that Autodesk Forge allows us to access and manage project data. That we can implement custom automated workflows to satisfy our needs and improve our project outcomes. That automated workflows increase productivity and quality and reduce costs. And lastly, that standardization allows for scalability and the higher profitability of an investment for automation. Thank you very much for your time and attention. I hope you found it interesting, and please feel free to reach out with anything.

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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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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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