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Digital Twins with Autodesk Tandem: From Setup to Data-Driven Analysis

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Description

Why should your organization create a digital twin? In this session, you will learn about KEO International Consultants' digital-twin business case and how we built our first digital twin using Autodesk Tandem software and IoT devices. We'll go through our digital-twin creation journey from inception, when we identified operational challenges, business opportunities, chosen solutions, and the implementation strategy. We'll look at the Autodesk Tandem project and IoT sensors setup and integration. And we'll show how outcomes of digital-twin data analysis can be used to make data-driven decisions to improve operations and employee well-being. This class is perfect for anyone who wants to learn how to use digital twins to improve the performance of their assets. Whether you're an asset owner, consultant, or contractor, this class will give you the skills you need to use digital twins to make a difference.

Key Learnings

  • Explore the problem, solution, implementation approach, objectives, and challenges involved in a digital twin.
  • Learn about the Autodesk Tandem setup: 3D model, facility template, custom parameters, and data streams.
  • Learn about the setup of IoT devices, including a sensor types overview, assembly processes, and integration with Autodesk Tandem using webhooks.
  • Learn about analyzing data and making informed data-driven decisions to improve operations and well-being.

Speaker

  • Avatar for Mateusz Lukasiewicz
    Mateusz Lukasiewicz
    Mateusz Lukasiewicz has over 12 years of experience in the AEC industry, and throughout his career, he successfully led digital delivery of large-scale projects and developed a number of modern digital engineering solutions by combining BIM expertise, computer programming skills and project management principles. Mateusz undertakes a vital role in driving company's clear vision towards achieving the leading digital innovator position in the market and its long-term digital capability goals.
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Transcript

MATEUSZ LUKASIEWICZ: Hi, everyone and welcome to my presentation. The topic of the class today is Digital Twins with Autodesk Tandem, from setup to data driven analysis. Few words about myself, my name is Mateusz Lukasiewicz. I'm Digital Projects Manager at KEO International Consultants. I'm based in Dubai. In my role, I'm focused on BIM, computer programming, and computational design, project and construction management, and digital twins.

The format of this class is a case study. We will start with a short introduction about digital twins, our objectives, and strategy. Then we'll move to practical step-by-step digital twin implementation and results overview.

Why are we here? In the last 10 years, we can observe growing interest in digital twins. Looking ahead in the future, digital twin is a rapidly growing business, expected to reach close to $50 billion in investments in the next few years. We are here because we want to be early adopters and understand the benefits of this relatively new and promising concept.

What is digital twin? It can be defined as virtual model of real object or process for analysis and optimization. Digital twin is composed by physical asset, digital model, and real time data connecting both. The real benefits of digital twins come from data that can be analyzed by using data science, parametric models, and optimization algorithms. The video shows digital twin of our office where we can see geometrical replica of physical asset and real time air quality and desk occupancy data. This model is analyzed by using custom parametric model, which is used to calculate the results and visualize various metrics that we will explore later on.

There are multiple uses and benefits of digital twins, starting from internal, such as real-time monitoring and analysis, reduce downtime, optimized resources utilization, predictive maintenance, health and safety improvements, employee well-being and retention, training and simulations, also external, like new revenue streams, services expansion, improved customer experience, reputation gains, and emission reduction. Let's go back to our case study. We have identified five objectives, such as improve assets monitoring, prevent equipment failures, improve maintenance, improve employee's productivity, comfort, and well-being, and also evaluate what if scenarios for different layout changes.

To achieve them, we implemented five components by creating 3D Revit model based on the physical asset, assembling IoT sensors, creating new facility in Tandem, importing 3D model and integrating sensors, then analyze model by creating parametric model using Dynamo for Revit where we define functions analyze historical data, calculated, and optimized results. We had challenges. Currently, digital twin software maturity is rather undescriptive and informative side, rather than predictive and comprehensive. As long as we've been able to achieve first three goals by using out of the box functionality, we had to create custom solution to optimize results and explore what if scenarios.

In addition, we had minor issues with mapping IoT sensor data due to data type temporary restrictions in Tandem. However, it was easily handled by writing custom translation Cloud Function in Microsoft Azure. Finally, in the future, we are expecting automated way of assigning hosts to data streams rather than doing it manually. We found the practice of assigning hosts manually for 100 plus data streams quite inefficient.

Now, let's talk about Tandem setup. The first step was to create digital representation of physical entity, which is selected floor of our office in Dubai. We modeled the relevant scope of structure and architecture, and applied special considerations to Revit rooms, which were split as per expected sensor coverage zone. Instead of having one room, we have multiple rooms in the same open space. Also, we utilized family instance mark parameter to identify desk number.

Let's move to Tandem. We have created additional categories by modifying default classification system. By adding rooms and sensor categories, it is easily done by editing Excel file exported from Tandem. We create a new classification system based on the updated classification system.

In the next step, we added custom parameters, such as carbon dioxide concentration, temperature, humidity, pressure, and occupancy to capture data coming from IoT sensors. This process was repeated for all expected data types. Later, we created new facility template using newly created classification system that contains sensors categories, where we applied custom parameters created earlier.

Finally, we created a new facility, which is basically a Tandem project, and imported model directly from Autodesk Construction Cloud. The imported model is the latest published workshop model. In either case, we are using a Revit model. The input is very straight forward process. So first step done. We have now virtual model in Autodesk Tandem. It is not yet digital twin.

The next step was to create data streams that are used to create connection with IoT sensor. Data streams can be hosted to specific Revit elements. In our case, desk occupancy data was hosted to desks. And other data streams were assigned to Revit rooms, such as air quality metrics, or meeting room occupancy. Data streams can be also added to classification system for grouping purpose.

In our case, we created more than 100 data streams, which are represented in Tandem as green spheres that indicates approximate sensor location in physical office. So far, we have geometrical model and data streams. However, at this point, we still don't have connection between virtual model and physical asset.

To do so, we need another component of digital twins, which are the sensors. There are multiple IoT sensors providers in the market. The manufacturer we selected offers following sensor types like temperature, humidity, touch motion, desk occupancy, water, object proximity, and air quality sensor. This is how it looks once installed. The assembly process is very straightforward.

Basically, Cloud Connector is plugged in the socket and connected to internet cable. Other sensors are assembled by using double-sided tape. Each sensor comes with installation manual and recommendation for the ideal placement. So for example, air quality sensor cannot be placed too close to building facade or air exhaust.

In terms of data flow, data is collected by sensors. Then it is sent to Cloud Connector device, and finally, to IoT service. From IoT service, we can link the data further to other software, such as Autodesk Tandem, which contains additional functionalities, such as data analysis tools, and also model visualization capabilities.

At this point, we have data in IoT platform, office model in Tandem, and data streams placeholders. Data Bridge between sensors and Tandem can be made by mapping data stream ID extracted from the link that we can see now on the screen. The external ID is the last part of the string. So this is done on the sensor level. And on the project level, we will be using the webhooks.

So we are now in IoT platform. We create new data connector by using webhook and specifying relevant parameters that should be reported back to Tandem. We can rename the sensors to match the IoT platform sensors naming with Autodesk Tandem data stream naming, and add label key for external ID and copy our external ID value to establish the connection. This exercise was repeated for 100 plus sensors.

Back in Tandem, we can notice data in JSON format received from sensor. We can now simply select key value pair for each parameter to start reporting data and display in the charts. So now, we can see that there are some entries for the data. Select the single entries. Now we can see data for last few days. We can play with different data ranges to display different data.

So basically, at this point, we have fully operational digital twin model. We have virtual representation of the office, and we have real data coming through sensors. The question now is how to use such model to achieve our objectives.

If you recall our objectives, The first one was to improve assets monitoring. So what we did, we evaluated temperature, humidity, pressure and carbon dioxide concentration against codes, such as thermal environmental conditions for human occupancy. And based on the results, we were able to optimize air quality by applying corrective actions by inspecting HVAC system and adjusting thermostats. In similar manner, we've analyzed staff attendance, desks, and meeting room occupation. We've been able to optimize desks allocation and revise meeting room booking schedules.

Moving to the second goal of improving maintenance, we've identified the problems in pantry area housekeeping, to reduce housekeeping team response time with utilize the touch sensor to send instant notification during specific hours to notify the maintenance team about various incidents in pantry area. Additionally, we've collected data, analyze it, and analyzed against current housekeeping schedule, and modify the frequency based on the peak and low periods. Our third goal was to prevent failures. We identified IT server room equipment as sensitive to high temperature and humidity. To prevent failures, we set the temperature trigger for sending automated notifications if temperature in IT server room is above 20 degrees. And eventually, as the result, we've been able to prevent failures by improving response time, as it was based on instant notifications whenever the event occurred, rather than relying on manual scheduled in-person inspections.

Previous goals were easily accomplished by using Tandem and notification system. Moving forward, we will go beyond out-of-the-box functionality and start exploring custom solutions to improve productivity, comfort, well-being, and also to explore different office layouts scenarios. So before we move to improvements, we have to be able to actually measure what we are trying to improve. And to measure, we need to define. What we did, we came up with this simple formula taking into account five factors, such as proximity to other desks, point noise sources, and communication paths. These three components have negative impact in our formula, as well as we look at the air quality and daylight access, which have positive impact.

First, we've noticed that two sides of our office are more or less independent. Therefore, each side was evaluated in separate exercise. So moving forward, we will focus only on this part of the office. To calculate the impact of each factor, we created a parametric model in Dynamo, which calculates the value of each impact, and also visualize for each desks. You can see there is some color coding applied for each of the metrics. Model is dynamic. Whenever we make any change in geometrical model, the metrics are being updated.

So let's have a look on this impacts and how they are calculated. So first, let's start with desk proximity. The general principle is that the closer and more frequently occupy disks are, the higher the negative impact. On the video, we can see which desks are impacting the desk that we are considering at specific moment.

In our exercise, we neglected desks that are further than five meters. So it's basically a sum of impacts. And the sum varies depending on the task that we are considering in our formula. The results can be exported and shown in the desk's interaction matrix. Based on the matrix, we can calculate the total result per desk. And we can also do some sort of a desk ranking based on the total value of this impact.

For daylight accessibility, formula is very straightforward. It is multiplicative inverse of the distance to building facade. Basically, the closer to building facade, the higher daylight is, which is represented by parts of various height. In similar manner, we use sensor's data to identify different air zones across the office. In our case, we have three different air zones, as we install three different air quality sensors in this part of the office.

We also had a look at point noise sources, such as printers. Basically, the closer to point noise source, the higher the impact is. And this impact is negative. Also, to demonstrate that the model is dynamic, we are going to simulate what if scenarios. So we are going to move the printer towards the left side.

So what's happening now in the background are moving the printer in Revit. And now, updating model in Dynamo, we can see the geometrical change in the model and also updates of each matrix. Finally, we took into consideration the proximity and magnitude of communication paths. Basically, the closer two communication paths having higher number of employees, the higher negative impact. Simply means that there are more people moving, and there's more potential distraction.

Going back to our case study, the formula for 41 desks, two noise sources, and two communication paths will look like on the screen. And we are going to explore three scenarios. First, we will look on dummy data, or rather, no IoT data. So we are assuming 100% occupancy and 100% air quality, which will give us some theoretical results. Then we'll plug actual IoT data to consider the actual air quality and desk occupancy time, which affects the proximity impact, as the result will have the actual results and desk allocation ranking. And finally, we will try to optimize results to achieve highest values by optimizing desk's allocation.

Let's move to case number one. So we have no IoT data, and expected output of this exercise is to evaluate which option among four shown on the screen is the best. So again, we are using our parametrical model where we can assess impacts and create desks ranking. Also, we can use option one as benchmark, so we can compare total improvements, improvements for each metric per desk and desk ranking changes. So you can see that the option two is approximately 1.6% better than option one. Similar, we can repeat for option three and four.

So as conclusion of this slide, you can see that the custom parametric model helps us to compare different layout options, and help us to identify the best among given proposed layouts. So in our case, the layout number four is approximately 6% better than option one. Previous example gave us some results assuming not actual desk occupancy and air quality matrix. In this scenario, we will plug the actual disk occupancy and air quality data to parametrical model to calculate the real values.

So we can see the air quality data in Tandem. And similar for occupancy, you can now see data export, the average sum per week and normalized data. Similar for air quality, this data is plugged to our parametric model where we can calculate the actual results and the actual desk's ranking. So the takeaway from this slide is to understand that the actual data does make a difference, which was observed in updated desk ranking and formula results. So the results from this case will be used as a benchmark for further optimization.

Before we attempt to optimize results by changing this allocation, let's spend a few seconds on basics of probability and statistics, and how many ways can three people be assigned to three desks? The answer is six. And this can be also visualized as shown on the screen. This number is not a guess. It's actually calculated using permutations formula.

In our case, n is equal to r, which basically gives us factorial of three, which is six. So that's the background. In our case, we have 41 desks. This gives us factorial of 41, which is equal to this long number. Obviously, we can't evaluate all combinations. And in our case study, we'll take 100,000 random arrangements in optimization calculations.

Before we calculate 100,000 different options, let's first try to do it manually. The first logical step was to optimize results manually by assigning better desks based on desk ranking to staff who are more frequently at their desks. So you can see the desk ranking. We can see the occupancy. So basically, the person who spends most of the time in the office is assigned to the best desk, and so on.

Distorted data is plug the parametric model where we can calculate the improvement. You can also see desk ranking changes and improvements per desk. So by using this intuitive principle, we achieved close to 4.3% improvement against the benchmark.

The results achieved through manual desk assignment in previous example are not optimal. The reason for is that the cranking and impacts are dynamic as they are influenced by time variable in desk proximity factor. To investigate further, we randomly selected 100,000 desk allocation permutations, calculated results, and identified the optimum disk allocation. So obviously, this exercise cannot be done manually. We are talking about 100,000 different combinations. We also use Power BI dashboard to somehow illustrate the results, and how the results are changing depending on the number of permutations.

So in conclusion, by assessing 100,000 options of using optimum desk allocation, we've been able to further improve comfort, while being scored by almost 11% against the benchmark. In separate exercise, we modify the daylight formula to reflect time spent at each desk to calculate daylight hours. Knowing the daylight impact factor per desk and multiplying by average time spent at the desk, we calculated the baseline. Moving further, we have simulated disk allocation changes by using the very simple principle that the higher attendance, the better in terms of daylight factor desk assignment. By using this method, we've been able to achieve overall 78% improvement of daylight accessibility.

In conclusion, setting up a digital twin with Autodesk Tandem is a straightforward process. IoT sensors connected to digital model enabled facility management to enhance asset monitoring, improve maintenance, and also prevent equipment failures. Historical data statistics and parametric model enabled the selection of optimal results and the exploration of what if scenarios. We've been able to improve overall performance comfort and well-being by 11%, reduce negative distraction impact by 27%, and improve daylight accessibility by 78% in a separate case.

To sum up, in our relatively simple case study, we've been able to leverage digital twin and achieve multiple objectives, also, to identify huge potential in analyzing real data. This slide concludes my presentation. I hope you found my presentation interesting. Please drop me a message if you have any questions. And thank you for watching.

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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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