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Rosendin's Big Data Foundations Yield Big Construction Solutions

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Description

Embark on an exhilarating journey with Rosendin, as we elucidate our dynamic approach to managing data from 1,000+ projects in Autodesk Construction Cloud. Prepare to be dazzled as we automate data acquisition, extract actionable insights, slash errors, and save time. Dive into our groundbreaking collaboration with Autodesk, harnessing the power of Autodesk Construction Cloud Connect recipes to effortlessly pipe data into a centralized data repository. Experience innovative data engineering techniques, transforming raw data into dazzling visualizations. Join us as we dive deep into Autodesk Platform Services APIs and Autodesk Construction Cloud Connect recipes, uncovering secrets to turbocharge data consolidation efforts with SQL wizardry. Brace yourself for an electrifying showcase of Power BI visualizations, revealing hidden gems within our data sets. Prepare to be astounded by the transformative impact of our extract, load, and transform processes, delivering efficiency gains of up to 60% compared to traditional methods.

Key Learnings

  • Learn about executing data pipeline construction to databases using Autodesk Platform Services APIs and Autodesk Construction Cloud Connect, with comprehensive recipe guidance.
  • Learn how to use SQL queries for data engineering on normalized tables, integrating automated scheduled recipes.
  • Experience a live demonstration highlighting data visualization of Autodesk Construction Cloud modules through Power BI dashboards.

Speakers

  • Adam Roberts
    I work as a Data Analyst for Rosendin Electric. My background is nested in information technology and I am involved directly in solution development for our company.
  • Avatar for Liang Gong
    Liang Gong
    He is a structural engineer by training (PE) with a background in preconstruction/estimating, construction management, BIM/VDC and data science. He helps customers leverage the data they produce through the design and build process to generate actionable insights including forecasting and scalability. He also automates customized workflows with ACC Connect and Autodesk Platform Services. After graduating from Duke University, Liang is currently working on his second master's degree in Applied Data Science at University of Chicago, focusing on AI/ML as a part-time student.
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      Transcript

      LIANG GONG: Hi, everyone. Please read the safe harbor statement. Welcome to our presentation, Rosendin's Big Data Foundations Yield Big Construction Solutions. This is Liang with Autodesk. And this is my partner, Adam Roberts with Rosendin Electric. Speaker introduction, I have a background in civil engineering. So I'm a PE in Civil. And then I'd be working in BIM, in VDC, in BIM estimating in the design build world. And then pivoted to tech. And now I work on analytics and automation consulting at Autodesk.

      Meanwhile, I'm a part-time student in MS in applied data science, focused on AI and machine learning at UChicago. Next, I'll have my teammate, Adam, introduce himself.

      ADAM ROBERTS: Thanks, Liang. Welcome, everybody. Thanks for tuning in. My name's Adam Roberts. I work for Rosendin Electric. I don't have any cool letters after my title like Liang does. I'm kind of normal in that regard, I guess. He's an overachiever. I am definitely not. My background is strictly IT. I've been with Rosendin for nearly 11 years. Before that, I had some various IT jobs before I came to Rosendin.

      I moved over to our quality department in 2020 to help with some initiatives regarding dashboarding and data analysis. So I just kind of wanted to point that out there. I don't have a data science master's, or anything like that. I really think that this stuff's approachable. If you have the will and you want to implement these things in your environment, you can do so without being a turbo genius. Obviously, if you have a turbo genius like Liang to help you, that helps a lot. But I just wanted to make that point that if you want to implement these things in your environment, you can do so. It's really not that daunting.

      We're just going to cover our presentation agenda very briefly. First, I'll take you through our business background and motivations. Talk to you a little bit about our philosophy, why we're doing this. Liang will take you through a bit more of the technical stuff, starting with executing the data pipeline construction to our database using the Autodesk Platform Services APIs, the ACC Data Connector, and Autodesk Construction Cloud Connect, which includes Workato. We use ACC Connect and Workato interchangeably, that's why they're the same thing.

      Next, he'll show you the SQL queries they've built using the data engineering for normalizing the tables. So the stuff comes from the data connector. It's a bit complicated. We really wanted to normalize that stuff so it was easier to manipulate, and analyze, and then tie into our other databases. He's going to show you how he's done that using the recipes in Workato. The beautiful thing here is it's all automated. So we don't have to do any sort of manual interference. It's all done scheduled. And that's all we have to do to worry about syncing that data.

      Next, I'll show you some of our high-level KPIs, and some of the stuff that we've done in Power BI for dashboarding some of these things. And then lastly, Liang will take you through some predictive analytics stuff that we may be implementing in our environment. And maybe some parallels, some other things that-- maybe the stuff I'm talking about won't resonate, but the things that he will talk about using predictive analytics with some other types of data could resonate for your environment.

      So I'm going to start off with a quote, which is super cliche. So sorry about that. So the quote is, "the most dangerous kind of waste is the waste we do not recognize." Quoted by Shigeo Shingo. He is an engineer consultant that was sort of made famous in the West by his documentation and study of the Toyota manufacturing process. I know Toyota has sort of become buzzwordy. But a lot of really good stuff here. If you don't know about him, I highly recommend researching the guy. He's very brilliant.

      So let me talk about our philosophy a little bit. So I think it's important to talk about the philosophy. It's one thing to have a solution, but the philosophy sort of propels the solution. So our philosophy was sort of three-tiered. We want to learn from our mistakes. Learning from your mistakes is very important. But what we wanted to do was scale up a solution where we could learn from other people's mistakes, which sort of enhances that process and speeds things up.

      So instead of people making the same mistake all over the country, if one person makes a mistake, we could share that information at scale. So for that to work, there has to be an environment that fosters learning and a willingness to admit mistakes. And that definitely needs to be reinforced by leadership. Luckily, we have some people in our company that feel very strongly about this stuff. Next, changing the culture is often difficult. And I just want to recognize that for a second.

      It's one thing to say, oh, yeah, just do this. Changing the culture in your company can be very difficult and take a long time. So just keep that in mind. Stuff doesn't change overnight. It never does. Don't get frustrated. Next, understand your strengths and weaknesses. I think this is very important. Knowing your strengths is good. You can use them as a foundation to build off of. But understanding your weaknesses is equally as important. And then you need to, if you can, address the underlying contributing factors for your weaknesses, whatever they may be.

      Lastly, adjust accordingly and continuously improve. So we have done this a little. This first bullet is important. It kind of ties directly in with this project. So introduce ways to collect information that can be analyzed, reported on, and distributed. So if you want to collect information about the mistakes that are happening in your organization, this presentation is very quality focused. But this can be-- the same project can be implemented for safety, or any number of different instances.

      But it's important to collect that data so you can get it back into your hands, analyze it. And then get it back into the hands of the people that actually need that information so they can do their jobs more efficiently. And then of course, the cycle continues. So it's continuous improvement. As you find these things and implement them in your organization, you just will basically be doing the same three steps forever, hopefully. Maybe not. Maybe you perfect them pretty quick. But most people find problems and then find more problems. You turn over a rock, and there's another rock underneath. And it's rocks all the way down.

      So our business background and motivations for this. I talked about our philosophy. I can't overstate the philosophy enough. Money is a bottom line for a lot of businesses. But there are other things at play here. So for starters, we wanted to become more efficient as a company. So we want to find problems before they happen, which means doing things right the first time. We're an electrical sub. Rework plagues our industry, and it's a huge time sink. It's a place where a lot of money gets spent.

      And also in our specific trade, electrical rework is the most dangerous type of work. So we want to make sure we are doing the things we can to mitigate, to make sure, our people are safe and they can go home. But also we want to make sure that we're doing right by our customer. So we want to become more efficient, finding problems before they happen. And we want to do more with less.

      Anyone that has had any, I guess-- projects are popping up all over the place. There's more projects than there are people to actually complete those projects. The workforce is aging. There's not as many people coming into the construction workforce as there are people sort of retiring. So we have to become more efficient with what we have. There's just kind of no other choice.

      Secondly, we want to provide a better product and service to our customers. So we want to strengthen existing relationships and new relationships. So we have projects with newcomers. We want to make sure that they come back to us. We want to make sure that they were satisfied, the interactions were good. That helps us get repeat business, which is big. Next, have better cohesion and communication with our project teams, the GCs, and the customer.

      I'd say from experience, most rework happens when there's some sort of breakdown in that communication. Whether it's among our own project teams, or it's from maybe our customer, the GC, the information doesn't trickle down to who it needed to. That's usually when stuff goes wrong. Lastly, make more money. We're a business. It's part of this. But I like to think of it as an after effect. If we're doing these other things, the money piece comes naturally. We're not necessarily out to make more money. It's just an added benefit of doing this process.

      So make more money. If we're more efficient, we do more with less, then we make more money. So we're doing more with less. Great. Lastly, revised programs and processes that don't add value. So there are-- especially in electrical, there's all kinds of regulatory stuff, which is great. But we've put in self-imposed things that maybe don't necessarily add any value. And we're doing some analysis internally to identify things so we can modify those processes, modify the programs. Make sure they are adding value across the board so they make sense for everyone included in these projects.

      So I just wanted to talk a little bit about the solution that we're looking at today broadly, just so we can all follow along a little bit easier. I come from an IT background, so a lot of this stuff made sense to me right out of the gate. But for those of you without that background or lack of experience with these solutions, I kind of want to just show you, very broadly, what this process looks like. So what we're doing is we're taking the project data in the platform that's entered by our project teams-- so this is just people filling out issues, forms, filling out assets, and changing the statuses.

      All this stuff gets collected into ACC. And what we are doing is we're pulling that data using the data connector. And we're going to move it into our internal database. So we have this data in our SQL database. And that's where the normalized table construction happens with the queries that Liang and his team have built to take the raw data, normalize it so it's easier to analyze. From there, we can do our analysis and dashboarding. So there's a couple components here at the very end here that may or may not pertain to what you want to do in your environment.

      We have chosen to not only do the analysis in Power BI-- you can use whatever program you want to. We happen to use Power BI-- but we've also-- going from the analysis and dashboarding into the consumable information, that piece of it, we have the Power BI dashboards and reports that we've built. And then we make those available to the project teams and personnel-- the people that can actually learn from this stuff. So they can use the dashboards for oversight, easier management, reporting.

      If they want to create reports to send up like weekly status reports, things of that nature, they can do that to the GC or the customer without anything from my end at all. They can do that all on their own. And then the added benefit of making this information available to your project teams is they can come to these conclusions on their own. So I'm not just going, hey, I found a thing. I think you should do things differently. They can come to those conclusions on their own.

      After all, I have a IT background. I'm not a construction person, per se. I'm not a project manager. I've never worked in the field. Those professionals know way more about those processes than I do. I've just made the data available so they can consume it. So without further ado, I'm going to turn you over to Liang, who is going to explain all of this stuff in much better detail than I can. Thank, Liang.

      LIANG GONG: Thanks, Adam. Adam is just being very humble here. So Adam mentioned the project data. And reality, as shown in the picture, the project data could have different modules. You use ACC, SS, Docs, checklists, forms. They all come from different silos. And the end goal is to diagnosing and analyzing and visualizing the data. How are we going to achieve that goal from those data and different silos? We need to consolidate them. We need to process them. Put them in a central repository, like a SQL database. And perform data engineering before we could use the consolidate table to perform analytics.

      So this gives a overall picture of how we are processing the data, how the data pipeline looks like. The next page that-- intuitively, I want to give you some concepts that you probably use the issues, module, and ACC a lot every day. This data shown on the UI are not ready to be analyzed, because ACC is not a database. On the backhand is, but on the front end UI, it is not. So we want to grasp all this data. Like, what's the issues name, issue credit date? And what's the attribute value? And what's the attribute here?

      So essentially, we want to grab all these important information about the issue. How we are grabbing those information by performing data engineering to consolidate the normalized tables for the issues table. For example here, we want to grab the Builders FirstSource. We want to grab Punch List for the issue subtype. I want to grab the Punch List for issues type. And you can see all the values are distributed under different normalize tables. And we have to perform data engineering.

      So just want to give you some intuitive concepts right now for better understanding of the latter slides. I want to know how many people are actually using the insights module on our ACC? A lot of the times, people just click the Run Extraction, and the Zip file can be downloaded, which includes all the-- more than 200 normalized tables here. And Rosendin doesn't want to go through this manual process to download the data every day, or every week. It's a waste of time.

      So we're helping Rosendin and Adam to automate the process-- the data pipeline. And what we're automating here, we're grabbing all those automated normalized tables from the ACC cloud into their SQL database. And the reason here I'm circling the customer attributes three tables is because I'm going to give an example of how to consolidate these three tables in the latter slide. So this is just a heads up.

      Speaking back like, how we're actually automatically piping the data from ACC cloud into Rosendin SQL database, this is how we are doing it. We are doing this through ACC Connect, a.k.a. Workato. For the recipes, there are two different levels. This is on the parent level, we're hosting all the parent recipes. And they say the Utilities folder, which are hosting all the child recipes, which include all the 200-- more than 200 tables that how we are piping those tables from CSV into SQL database. Let's take a deeper into how we're actually do this.

      On the left side, these two screenshots are from the parent table-- the parent recipe. And on the right side this is the child recipe. So the general logic is that we hit the APIs, telling the APIs, grab a data extraction on the UI. So the UI began to hit that button, Run Extraction. So after the extraction, the run is finished with [AUDIO OUT] the file. Unzip that file, and begin to loop through all those more than 200 tables in the zip file. Will process them one by one. For each CSV, we send them to the child recipe, which is on the right side.

      For the child recipe, what it's doing that create a SQL table if there's not one yet in the SQL database on our Rosendin SQL. And analyzing the CSV, and gradually upsert each line of the CSV table-- upsert into the SQL table-- corresponding SQL table under Rosendin. So this is what we're doing here. Essentially, grabbing the data from ACC cloud and download it, analyze it table by table. And for each table, we're upserting those rows in the table into Rosendin's SQL database. So this is the overall idea.

      And all this are on a time schedule, which means it's totally hands free. You do not have to do this manually in any of the single steps. After we put all those SQL tables-- we filled all those SQL tables with the CSV files, the next step, we need to perform data engineering, which I was talking about earlier. Because all these tables are just normalized tables. You have to consolidate them. If you remember the issues example, by sticking the tables together to extract the actual valuable information that is ready to be analyzed and visualized.

      I was talking about earlier for the issue of customized attributes tables for this three tables. So from the first table you choose Custom Attributes table. Left outer join the Attribute Values table. And then left outer join again with the Issues Type table. In this way, we could expose the attribute value, the attribute name, and the issue type that the attribute belongs to. That was an example from the attribute perspective. How about on the issues level? Because we're caring about the issues creation date, the issues type, which category the issues falls into. These are all the normalized table.

      The most difficult part of the work is how we find the common columns between different tables, and how it correctly mapping them. There's already an a schema in the zip file. However, it's not providing a ERD. Essentially, we're working on something like this. This is for the modules-- the ERD. Basically, we're mapping the different columns between different tables to map them correctly. So after stitching them together to make sure it is accurate, there is no duplicated rows. It's ready for analyze for visualization. And this is a hard core of this data engineering part.

      And that is equivalent to the Power BI part. What I did first is that we're putting the Power BI, looking into one single project first as a proof of concept to make sure the logic-- the way the tables are merged together are correct before writing the massive SQL queries in SQL. Just using Power BI as a proof of concept. As you can see here, after we prove the concept in Power BI, we're using the SQL to write the SQL queries. Because all this queries are essentially the same as the apply steps in Power BI.

      It just move the logic from Power BI into SQL. That's all it is doing here. Next, after we're generating this Issues Consolidated tables, we hand it over to Rosendin. And Rosendin could begin to perform analytics to drive insights from this data. And I'll head over back to Adam to talk about the visualization and analytics.

      ADAM ROBERTS: Thanks, Liang. So what we have here on screen is an example of some scoring KPIs that we've put together internally. Again, I just want to reiterate. Our focus, specifically at Rosendin right now, has been on rework type data and quality metrics. But you can use this for any-- safety incidents. Really, kind of the sky's the limit. It's just a matter of what you can collect in the platform, and then what you can creatively apply in terms of analysis to figure out what kind of KPIs you would want to use on your end.

      So I'm going to run through these very quickly. So at the top, we have a summarized score. And then underneath that, we have the contributing KPIs. So the left and right side you're looking at here is the left is overall-- it's all issues. The right side is a relative date running score with the same KPIs, but it's just for the last 30 days. So when you're looking at these two numbers, the right side is sort of a visual indicator of how are you doing in relation to your overall score. Or have you improved over the last 30 days?

      Maybe you've not improved. In this case, what you're looking at here, there's an improvement that can be observed. But the reason we have that there is sort of a trending indicator that you can look at very quickly and say, hey, how are we doing lately? You can get it real quick. So what we've done is these KPIs are actually weighted differently depending on the criteria. There's little goal indicators under each one of those figures. And that's the self-imposed goal that we've set at our company to try to achieve.

      And then the scoring is based off of whether or not they've made those goals. So there's some other stuff under the hood. I won't bore everyone, but some measures that we've put in place to build these KPIs and the weighting. So really quick, I'm just going to run through these. We have average days past due, average days to close the issues, percent of issues closed total, percent of L4/L5 issues. Were L4/L5, in terms of electrical speak, is sort of issues that have been discovered maybe at time of energization.

      External punch list items are just like they sound. Things that weren't up to whatever the customer specifications were maybe, or something-- some kind of rework that was indicated after turnover. Percent of rework items overall. How many actual items do you have logged in your issues that are considered rework? Or at least we are considering rework internally? And then lastly, average percent of blanks. So this is something I just want to talk on really briefly. I think Liang mentioned this once already. Maybe not.

      But your analysis is only going to be as good as the data you collect, correct? So what we've done here is we've built a KPI for our people to be able to manage how data is being put into the platform in their project. So if they look here, there's actually a breakdown that goes along with these. These blanks can be detected in various fields. So if someone fills out an issue in the platform, we can say, hey, they didn't fill out a root cause. Or maybe they didn't put in a due date, or something to that nature.

      And we want to make sure they're filling those issues out completely. So that way, we can collect good information. So I just wanted to talk on that very briefly. Analysis is great. But if you don't have great data to start with, it makes the analysis much harder. Or you have to leap to conclusions, which is never something you want to do. But this is just an example of some scoring metrics that we've put together on our end using the data that we're collecting from the platform.

      Some filtering options that we have. We call it slicing in Power BI, if you're not familiar with that term. Slicing is filtering. It's the same thing. So we're able to slice by company. Internally, we have regions and divisions. So we can go down to basically, if we want to look at Rosendin's rework numbers, or maybe a supplier-- if we're logging issues on a supplier, we can go in here, filter by the supplier. And then see how many rework issues we have with that supplier.

      So maybe we have issues regarding shipping. Maybe items are damaged in shipping, and we can log all that stuff, and very easily pull up what's going on with that supplier, for example. We can also filter by project. So that's sort of to circle back to the entire point of getting this information in people's hands. So if you're looking at company-wide data, that paints a picture. But it's not very specific. So that's why we have these other filters to get really dig down to region, division, and project so people can-- at any level of the organization, people can get the information that they want to see.

      Maybe a division manager wants to know how their division is doing overall. And they can get that information. The visual that you're seeing here is just, again, an example visual of something that we've put together, where we can see what is contributing to this rework value by either region or division. So you can really dig down quickly to figure out what you're contributing factors are. Some other things that we don't have shown here are some breakdowns by root cause, or by issue type. So that way, we can get some more information.

      Again, digging further, deeper into what's going on. So we can do some sort of corrective action to bring projects back up to where they're successful again. Or come up with a better plan. Like I said, the sky's the limit here. You can really do whatever you want. It's just a matter of being creative enough to come up with what you want to do. Which sometimes is easier said than done. I'm going to turn this back over to Liang here so we can talk a little bit about predictive analytics. Without further ado, go ahead, Liang.

      LIANG GONG: Thanks, Adam. That's really comprehensive. Coming back here, I'd like to show you guys the evolution of the analytics descriptive, prescriptive, and cognitive. So far, what we all have talked so far about, the descriptive analytics. However, this really lay a very solid foundation for predictive analytics. Predictive analytics answer the questions like, which asset is going to fail? When it's going to fail. And the prescriptive analytics going to answer the question like, what can we do to prevent it?

      So with a solid foundation of the data that already lives on our Rosendin SQL database, what can we do? For example, here is a quick structured data using the structured data, performing a supervised learning by using secular package to predict the issues priority level. Like, which issue can be solved on the construction side objectively, rather than people on the side just pick up the issue to solve subjectively? By leveraging these eight columns, we're predicting the issues priority level. Like high, low, medium.

      This is a quick example of supervised machine learning by-- falls under the predictive analytics. But there are tons of other use cases under predictive analytics. The first one, just talking about the previous slide. And the second one is actually Revit. This is a great example for unsupervised machine learning. As you can see here, this dots representing the models of with different disciplines. If there is a new model causing the blue without actually opening the model-- which could take a long time to open it if it's a big model, I don't want to spend the time opening it.

      Just by using its metadata from Revit, I could tell it falls under the cluster of structure. So I know this is structure Revit model even before opening it. So this is an example of unsupervised machine learning. And another big area is about the text. We put a lot of the text under the issues description, under submittals, transmittals. Have we ever really used those texts? Those texts are literally unstructured data. We could perform sentimental analysis.

      We want to know which issues, descriptions really inactive, which could be flagged as red so that we could solve that issue first or prioritize that issue. Things like that for those unstructured data, those are very important. Valuable resources, if we don't use them, they just-- it's just a waste. It's a valuable asset for us. And the next one is about ACC photos. For example, this guy is wearing a safety hat, safety vest, pants, shoes, gloves. If you want to identify anyone on the site who is not wearing a safety glasses, you probably have to go over all the pictures one by one, or the videos.

      However, with the deep learning artificial neural networks, it's going to automatically flag the person who's not wearing the safety hat automatically. So you do not have to go through one by one manually. So that we could abide by the OSHA guidelines. Another impact using the computer vision is about the construction progress. If we are taking a screenshot of the same angle on the construction site at the same time every week, we could compare the differences between the photos.

      So say week two and week three had the biggest difference between the photos. That means that's actually have the biggest progress. Again, without manually looking through all the photos like 52 weeks, we could automatically performing the algorithm under cosine similarities to tell how quick of a progress construction project is. Last but not least is about the trend analytics. For example, here, the client walk to us, they want to know their token's forecast based on the seven years purchase of tokens with us. They want to know how many more tokens they should purchase for the next year.

      So by performing trend analysis-- and here, I'm using a SARIMA model-- you could see here the orange line is predicted. This really provides a baseline for conversations between the account team and the clients so that they know how many more tokens to buy, how many more to deduct. This is just a baseline. All this cannot be achieved with a solid database, the solid data repository, which has already acquired all the ACC's data into a Rosendin SQL database automatically. And that's the foundation, again.

      That really wraps up our presentation for this year's AU. Any comments or questions are welcome here. And thanks Adam, for co-hosting the session with me. Thanks to everyone.

      ADAM ROBERTS: Thank you.

      LIANG GONG: Thanks.

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

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

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

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

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

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      We can access your data only if you select "yes" for the categories on the previous screen. This lets us tailor our marketing so that it's more relevant for you. You can change your settings at any time by visiting our privacy statement

      Your experience. Your choice.

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

      May we collect and use your data to tailor your experience?

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