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Validation of DfMA and Data Platform in Data Strategy Beyond BIM and Its Future

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Description

Daiwa House has achieved early adoption of building information modeling (BIM) in the design phase, adopted Autodesk Docs as its standard common data environment (CDE), obtained ISO 19650 certification, and is currently pursuing a CDP strategy. Toward further growth and innovation beyond BIM, we've launched the "Data-Centric Project" with Autodesk this year, focusing on a data strategy. We've defined a DDP (DfMA data platform), prepared a KoP (kit of parts) using DfMA methodology, and verified its effectiveness in real projects to address social issues such as construction waste and decrease of skilled workers. As data strategy has become essential, along with the verification of the effectiveness of the DfMA methodology in actual projects, we'll present the overall concept of the data-centered project, and discuss how DfMA and data strategy are related to each other and to the realization of the digital construction industry.

Key Learnings

  • Identify key elements when applying DfMA in Industrialized Construction (IC)
  • Validate the possible business benefits coming from DfMA
  • Define the data platform to gain the benefits from applying DfMA
  • Define the Beyond BIM data strategy

Speakers

  • 犬塚 道彰
    My name is Michiaki Inuzuka.? I joined Daiwa House in 2008 and have been working on architectural design of various kinds of buildings, such as commercial facilities, hotels, nursing care facilities, logistics warehouses, factories and so on. ? If you have ever stayed at a hotel in Nagoya during your business trip to Japan, it might be a hotel I designed.? As I went through a transition of design phase from 2D to 3D to BIM, I am very excited to think about how BIM will develop further in the future. ? Hobby: San-Shin (Traditional stringed instrument of Okinawa), Aromatherapy Graduating school: Nagoya City University Graduate School of Design and Architecture
  • 澤 海斗
    After studying digital design and digital fabrication at university, I joined Daiwa House Industry in 2020. I belong to the DX Promotion Department. I am an engineer who promotes DfMA/IC based on data strategy.
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Transcript

MICHIAKI INUZUKA: Good afternoon. Thank you very much for attending our session. We are going to present validation of DfMA and data platform in the data strategy beyond BIM and its future. Let us introduce ourselves.

My name is Michiaki Inuzuka. I joined Daiwa House in 2008, and have been working on architectural design of various kinds of buildings, such as commercial facilities, hotels, nursing care homes, logistic warehouses, factories, and so on. If you have ever stayed at the hotel in Nagoya during your business trip to Japan, it might be a hotel I designed.

As I went through a transition of design phase, from 2D to 3D to BIM, I am very excited to think about how BIM will develop further in the future. Hope I can share my excitement with you today.

KAITO SAWA: My name is Kaito Sawa. I am honored to be here and to have so many professionals in the audience I studied digital design, digital fabrication, and BIM at University and joined Daiwa House in 2020. Although it's only been three years, I have been working towards the transformation of the construction industry and the theme of DfMA and IC, industrialized construction.

Today, I'd like to share some of our efforts and our vision for the future. With your feedback, we'd like to further evolve our activities.

MICHIAKI INUZUKA: This is our company profile. Daiwa House Industry, Company Limited. Head office is located in Osaka, Japan. The company was established in 1947, and started its operation in 1955, about 70 years of history. Number of employees are around 50,000. The net sales in the last fiscal year resulted in 4.9 trillion yen, which is around $33 billion. If converted as a fixed rate, 145 yen to $1.

In the 1950s, when we began operations, the post-war reconstruction in Japan was in full swing. With the rapid economic growth, many buildings, schools, residences, offices, et cetera, were needed. In 1955, our founder developed a prefabricated building, utilizing the standardized components, being shown on your right in the slide.

It enabled to deliver large quantity of buildings in short construction periods. Based on the philosophy of proposing and implementing solutions to the needs of the times, Daiwa House has grown significantly. Here is a short video to show our broader business areas currently working on.

Throughout this session, we would like to share the content, as listed here. Our English is not fluent, at all, but we hope we can convey some of the elements that are resonate with you and meaningful for our industry to move forward. Now, I would like to hand over the next section to Sawa-san.

KAITO SAWA: Thank you, Inuzaka-san. Now, among four running objectives, I'm going to cover these three points, exploring some of the important elements to apply DfMA in their project.

First of all, what is DfMA? As many of you, as many of you attending this class may already know, DfMA is an approach in design with manufacturing and assembly in factories and construction sites in mind. Taking a deeper consideration on how to build in the early stages of design, with [INAUDIBLE] method, more optimized, industrialized construction can be achieved.

As Inuzaka-san introduced our company earlier, we have developed prefabrication technology, since the beginning, and have promoted industrialized construction in housing business. On the other hand, in building a business, we still rely on a conventional approach in design, , manufacturing and construction process, except for a few products.

The shortage of labor in the construction industry has become a social issue. We also feel the current process is limited in its ability to meet the rapidly increasing needs for housing and buildings globally. [INAUDIBLE] construction is a solution for such needs and challenges, and we believe DfML will provide significant growth for innovation.

To proceed with our case study on DfMA, we put our focus on these two points. The first is concurrent decision making and the second is productization.

The first point is about transforming processes to achieve concurrent decision making. The left is a traditional process. Data is passed from design to manufacturing to construction.

In contrast, the process on the right the future process. Each stakeholder shares data in the entire project with data at the center of the process. [INAUDIBLE] process from [INAUDIBLE] for decision making to concurrent decision making, is a key.

The second point is to establish a workflow to achieve the building configurations, based on the productized data. In a typical construction process, designed to accommodate manufacturing data on a case by case basis. Our future process, on the right, uses predefined manufacturing data for design. Productized the components may be considered to enable the building design, with standardized parts as much as possible.

The use of productized data supports the concurrent decision making described on the previous slide. Having the support from [INAUDIBLE] this slide summarizes the capabilities required to apply the FMA even more effectively. In the case study, we focus on these two themes as a foundation for applying DfMA.

We will now follow the flow of the hypothesis testing conducted as a case study. It consists of three steps. Let me explain step one.

We have defined the future process by focusing on the early stages of design, product development, and project development. said those processes are very important to apply productization concept and to study the building configuration based on productized data.

The first process is to create the product, or kit of parts, to be used in the design of each project. We designed prefabricated panels and units to achieve the industrialized construction.

In this process, we started to define the product concept. The product must be defined, not only in terms of appearance and function, but also based on the FMA principles.

Next, we use the wireframe approach for modeling. Wireframe is to represent the defined concept and pass a bill, based on the frame, as a master assembly or template assembly. The parts can be designed as a parametric product. Dimensions can be changed following the defined rules.

We also added attribute information to those products, for instance, weight. It will be updated referring to the master.

At this point, products cannot be used for building configuration, as they are. The model for production is converted into a model for building configuration and managed as a library to be used by designers.

The second process is a product evaluation process based on data. Designing products from DfMA principles requires skill to determine and build production ready componentization, considering manufacturing, as well as on site assembly. To support decision making by various stakeholders, dashboards will be deployed, showing data efficiently and effectively.

The dashboard will be maintained in the environment, accessible to all stakeholders. The sale process is a building configuration process by products. Create a concept design based on customer requirements and selects the product that satisfies specifications of the design.

The families associated with the product data assessed and referenced. Product selection is performed, checking the product performance, shown on the dashboard. With the approach, that designer can build the design, using producer products, without having to enter each component configuration from scratch.

In case or components of the building cannot be configured by already defined products, we need a process to request a new product. The process is the evaluation process for the configured building. We talked about product evaluation in process too.

Similarly, we can prepare a dashboard for the building to support decision making. The dashboard calculates KPIs for the building, by reporting it to the KPI for the product. By referring it to KPIs, we can make changes to more advantageous design. Also, the verification history can be saved, shared, and compared by all stakeholders. That's all for future processes we defined in the case study.

Next, I'd like to explain how we are going to achieve the process we shown in the video. We call it DDP DfMA data platform. At the bottom, the data layer, project data, and product data shown in the red box across three link. The linkage allows us to configure buildings with product data and to evaluate performance with dashboards.

Data is managed in the cloud and can be accessed using protocols defined in the middle layer, the service layer, through the presentation layer at the top.

The data is available to those who need it, when they need it. So far, we have discussed the process definitions and to proceed with our validation steps. As a next step, we have examined the feasibility of the process and the barriers for implementation. Then analyzed and evaluated the results.

The project we've selected for this verification is a timber apartment about 300 square meters. Since [INAUDIBLE] has not been matched upright to timber buildings and processes are fragmented in Japan, without the benefits of applying DfMA could be significant. In our [INAUDIBLE] we focus on three points.

Try to quantify the effect as much as possible. First, how much time can be saved in creating building manufacturing data in design phase with the productization concept? Second, how much of duplicated works can be eliminated by using data? Third, how much risk of inconsistency in design phase can be reduced by applying our future process?

The scope of the validation is illustrated in this slide. The process rolls from left to right. First, project related to data and factory data were collected, shown in green. Then future processes one to four were executed in parallel with the actual project execution, shown in orange. And finally, a prototype of the part of the products was made, shown in blue.

From this slide, I will explain three verifications conducted one by one. The first case is with the future process we define how much we can reduce the time to build manufacturing data in each project design phase. In this verification, we executed the future process, in parallel with the traditional process, and we compare the time to build a manufacturing data for the entire building.

In our variation, executing the defined feature process has resulted in about 80% reduction in the time required to build the manufacturing data. Most of them can be built simply by selecting and aligning the productized data from a library, according to the required performance. We could eliminate the time to create fabrication drawings from scratch. [AUDIO OUT]

The manufacturing data for products must be defined and ready to be manufactured. When prototyping some [INAUDIBLE] we created design data and product data. However, the data could not be linked and we had to rebuild the manufacturing data. This result reminded us the importance of data integration across departments in productization.

Next, let's look into the second verification. Here, we investigated on how much duplicate tasks we can reduce by implementing the future process we have defined. We identified the tasks done in the previous phase but the similar tasks repeated in later process. We examined whether such duplicated work could be eliminated by applying the future process.

Through this verification, the total time saved in the design phase was about 20 hours. The cost estimation only needs to address project specific elements and quantities are already known from product data. The time spent discussing on how to bear can all be deduced by reusing the product data, eliminating the same studies for those productized area.

Although the saving time was about 5% of the total design time for the entire building, if the scope of reusable data is expanded and applied to larger buildings, the impact could be larger. In order to ensure this time saving effect, it is important to accumulate reliable and high quality, reusable data.

Finally, the third validation was to verify how much the risk of inconsistency could be reduced in each project design phase. By constructing the building with productized components, with it, it is possible to resolve inconsistencies in the design stage, considering each component level, not just between architecture design and structure design.

If the crash is found during the panel production stage, it causes on site corrections and panel remaking, resulted in schedule delays and additional costs. In this validation, we could automatically detect a crash at the inside corner of the external wall panel. We could also identify an error in design, where it couldn't be configured by available panels.

If we apply further products based configuration at the design stage, we could reduce subsequent process delays and additional costs, effectively. Furthermore, we tried to verify the process through the actual panel prototyping based on design data. Unfortunately, the attempt was failed, due to the high dependency of the manufacturing equipment.

Here is a summary of our validation. First, we saw the possibility of up to 80% reduction in building manufacturing data in each project design phase. Second, we could save 20 hours, eliminating duplicated tasks. Third, with a future process, building can be configured by producable products during the design phase, and we can eliminate the risk of inconsistency.

Through this case study, we run some key elements to apply DfMA. First, the importance of data integration across organizations. It is essential to enable productization and product based design configuration. Second, the importance of reliable, quality data management. To promote reusing data, it is important to accumulate reliable, high quality data, and maintain property

Finally, consistency between design and production. Productization can not be effective if design data does not flow directly to production. In summary, how we create, accumulate, assemble, and maintain data, we will enable the DfMA for effectively in our company.

So far, I have covered three learning objectives. Next, I'm going to hand over to Inuzuka-san to talk about the last topic, our data strategy.

MICHIAKI INUZUKA: Thank you, Sawa-san. As we mentioned at the beginning of this session, we have been developing and constructing products in housing business for almost 70 years, focusing on prefabricated housing. About four years ago, we launched a project to revisit and reinforce our DNA, industrialized construction, having support from Autodesk consulting.

Through the project, we realized once again, the importance of the way we create and accumulate data. As we dig deeper into the methods and concepts of DfMA, in this chapter, we share our data strategy, including new restarted project with Autodesk Consulting this year.

As Sawa-san's team worked on the productization process for building components in its basis of DfMA. By implementing this process, the design and manufacturing of assemblies for factory production can be more effectively implemented. And then once [INAUDIBLE] buildings is established, the conceptualization, design, and simulator of complete built environment asset can meet downstream project requirements.

Furthermore, connected construction will simplify field operations and supply chain logistics through a digital site and assembly process. Common data environment is a cloud based platform to support the design and delivery of products and projects. It's centralized data storage and access, according to defined protocols.

As we discussed in the previous slide, there is a close relationship between BIM and [INAUDIBLE] Similarly, between DfMA approach and data strategy. We have described our model as [INAUDIBLE] of BIM and construction data, where data has grown in a circular manner throughout the entire construction life cycle.

Data storage and DfMA are similarly related to each other, we believe. In order to achieve true data driven transformation, we are working on a project to achieve data centric world. This slide shows how improved the use of data and information can significantly increase the company's capabilities and ability to execute its strategy.

In our project, we have defined three stages, naming the significant improvements in digital readiness at stages of transformation. As the stages progress, more advanced technologies and capabilities are acquired and utilized. Each stage has solutions that provide more advanced capabilities, as the stage progresses.

Stage one, focusing, focuses on structuring data in functional areas and centrally managing the data on the platform. Analytics in functional areas are possible at this stage.

Stage two will focus on the integration of design and production assembly through the platform, leading to enterprise diagnosis and analysis. Stage three will involve further integration of design, manufacturing, construction, and operations. The introduction of simple AI or machine learning enables predictive analysis across the business.

Beyond that point, we envision more advanced generative AIs, relating independently and collaboratively, for even greater generative design and make capabilities. Based on such data strategy, we have started to work on establishing the foundation of data among business units, as shown in the preview. Our goal is to build an environment where necessary data can be accessed by necessary people, in necessary timing, with security.

I saw some mentioned earlier, there are issues scattered in the current workflow in linking design data and production data. It is essential to transform through conventional workflows and processes, by building a mechanism centered on application independent data. [INAUDIBLE]

Now, we'd like to wrap up this session. Today, we have talked about the items in this slide. Based on DfMA concept, we define four future processes and tested their effectiveness, learning the process, along with the conventional processes. With the result, we have learned some important elements in applying DfMA. We also touched upon the data platform required to achieve greater value and effectiveness, as well as our data strategy.

At the beginning, I mentioned, we have about 50,000 employees in Daiwa House. Many people from many countries work together. With various business operations, the data accumulate. We may differ, depending on the region and the nature of the business. Data required for each, may also differ.

At the same time, changes in the needs of society and changes in technology are accelerating with each passing year. We believe to respond quickly and flexibly to such changes, we need to accelerate our transformation with data at the core, further developing the data center, the data centric project with Autodesk. We will accelerate our growth.

Last year, in AU 2022, we set forth the concept of digital Daiwa House, by promoting data centric initiatives. We try to create new value by confronting and solving various issues, not only internally, but also on a global scale.

Thank you very much for your kind attention.

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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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