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

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Description

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

Key Learnings

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

Speakers

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[CLEARS THROAT]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

LUCA BAZZOCCHI: Thanks a lot.

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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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