Common Questions

When you have questions about data clean rooms, we’re here to answer.

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A truly comprehensive, transparent, and cohesive view of decentralized data is becoming increasingly more difficult to achieve.
Due to the privacy-related changes to cookies and browsers, the approach to advanced analytics and many tried and true use cases around targeting, measurement, and optimization must change. Data clean rooms are the vehicle for privacy-preserving data collaboration and advanced insights in this new world.

Adding a data clean room process to your business will protect you from customer-activity blindness and unlock new opportunities to leverage your own data and partner data not previously available to you.

With a data clean room, you’ll know exactly how your customers are interacting with your brand and products, even within a Walled Garden, like Google, Facebook, or Amazon.

We can broadly identify two primary groups whose data collaboration needs make data clean room solutions a good choice:

  1. Businesses of all kinds with active, data-centric marketing. These organizations often lack critical visibility into how their marketing campaigns are performing. Without access to accurate data analytics, they’re essentially flying blind, and not making the most efficient use of their marketing spend. These businesses would like to be able to interact with the data of walled gardens such as Amazon, Google, and Facebook, as well as other restricted datasets, to understand how their advertising is performing — and how they can make better decisions. Ideally, since marketers are the main users, they’d like to adopt a business-friendly, out-of-the-box intelligence solution.
  2. Businesses with rich datasets and extensive partner ecosystems. Enterprises with large datasets and well-developed data platforms seek a simple, scalable solution to enable dozens or hundreds of partners to interact with the organization’s data in a safe, intelligent way. These businesses often want to build their own data clean rooms, but they prefer to use a solution that simplifies the task with easy building blocks. In these organizations, adopting data clean rooms is often a strategic initiative and involves several stakeholders, such as product managers, engineers, data scientists, and marketers.

Habu delivers a software solution both for data clean room owners looking to custimize their clean rooms and also for data clean room collaborators who are looking to get better insights by accesing rich datasets.

There are two primary types of data clean rooms: media clean rooms and partner clean rooms.

The most common media clean rooms today are those within the Walled Gardens, such as Google Ads Data Hub (ADH), Facebook Advanced Analytics (FAA), and Amazon Marketing Cloud (AMC).

Media data clean rooms are most commonly used by brands that spend significant budget within the Walled Gardens to enable them to access user-level data that is only available within those clean room environments.

Partner data clean rooms are often leveraged by two partnering brands (for example, a CPG and retail company or a publisher and advertiser) to safely share data assets; each party has full control on which data can be shared, for how long, and for what use case.

In a constantly evolving privacy landscape, media and partner clean rooms are one solution for traditional use cases such as targeting, measurement, and advanced insights. At the same time, they are opening up a treasure trove of new opportunities and business benefits for brands to pursue.

All companies need data to fuel critical use cases for analysis, personalization, and measurement.

With a data clean room, media companies can share rich first party data assets with their advertising partners to fill gaps, enhance insights, and grow advertiser investment, all while keeping proprietary data in control.

Similarly a CPG company would benefit from a shared data clean room with a retail partner for closed loop attribution to improve customer growth and lifetime value.

Also, a brand that makes a significant media investment within the walled gardens would benefit from the additional data available within Google, Facebook, and Amazon’s data clean room environments to improve targeting and media efficiency, and for cross-channel measurement.

Organizations that embrace data clean rooms now will find themselves with a competitive advantage and well-positioned for the future.
As the countdown clock to cookie deprecation quickly approaches and traditional advertising use cases become obsolete, it has become clear that consumer data will only be fully unlocked in select, highly controlled, neutral data environments.

Those companies with a robust 1st party data asset have a tremendous opportunity to securely share that data with strategic partners to unlock new revenue channels and strengthen existing as well as establish new partnerships.

Data clean rooms offer brands without a strong 1st party data asset, the ability to tap into another partner’s rich data asset and by doing so now, it ensures minimal disruption to your business as the clock winds down at the end of 2021.

The technical complexity of data clean rooms can scare off most professionals. Habu was built with a user experience that any marketer can appreciate and with the depth of insight that a data scientist will respect.

Additional roles that would benefit even if they aren’t the day to day user of the clean room include: head of revenue at a media company, data strategists, product development directors, agencies, and brand loyalty professionals.

There are 3 key benefits that data clean rooms offer marketers. The first benefit is improved media efficiency and sales lift.

  1. Data blind spots create challenges with understanding performance of your media. Clean Rooms are key to unlocking on-target reach, frequency, and return on ad spend capabilities in a privacy-preserving manner.
  2. The second is strengthening strategic relationships. Privacy-preserving, secure collaboration with strategic partners enhance and improve acquisition, product strategy, R&D, lifetime value analysis & sponsorship opportunities.
  3. The third is ensuring consumer trust. The secure environment that data clean rooms provide eliminate the tremendous risk to the data owner and user in the form of fines and loss of consumer trust for privacy violations.

In a privacy-first world, the concept of bringing all of your data into one system is just not feasible. Modern data clean rooms flip the script by requiring no data movement.

Legacy data clean rooms are query-based tools that often require IT resources to manage and interpret.

Habu’s data clean room software does not require IT resources. In addition to the traditional SQL-based queries, Habu adds an intelligence layer based on natural-language questions that interoperates within data clean room environments. This business-first approach allows brands to surface actionable insights in a more automated fashion.

Habu’s software structures reporting on business questions that correspond to queries, which informs the data requirements and the visualization of the results.

Differential privacy is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset.

Differential privacy can be achieved by a number of methods, two of the most common are adding randomized “noise” and/or applying redaction thresholds to an aggregate query result to protect individual entries … all without significantly changing the result.

As privacy regulations continue to evolve, differential privacy makes it possible for tech companies to collect and share aggregate information about user habits, while maintaining the privacy of individuals. This is a huge win for both brands and consumers.

Google was first to put the method to commercial use, in 2014, with RAPPOR: its tool for studying Chrome user data without endangering privacy; Apple’s first use followed in 2016.

Want to learn more about Habu’s privacy-preserving techniques?

Google Ads Data Hub (ADH) is Google’s data clean room environment, which was developed as a privacy-preserving replacement for DoubleClick.

This enabled brands to access impression-level data across all of their media campaigns in Campaign Manager, Display & Video 360 (DV360), Google Ads, and YouTube Reserve.

As cookies go away and Google sunsets its Data Transfer Files, which many advertisers have relied on to get access to raw event-level data that includes UserIDs, Google ADH will be the only environment with access to this granular level of information.

Google Ads Data Hub provides a depth of data and analysis that is much deeper than anything outside of the ADH environment or previously made available through Google’s data transfer files.

With Google ADH, you also have the ability to upload your CRM and any other first party data into ADH, where it will be combined with impression data from your advertising campaigns.

Combining your data with event-level data from your Google ad campaigns across Campaign Manager, Display & Video 360 (DV360), Google Ads, and YouTube Reserve, allows you to unlock new insights, improve media efficiency and targeting, and optimize campaign.

Although ADH unlocks valuable insights, it also requires a team of technical resources to manage and extract insights. Habu’s Data Clean Room is software that sits on top of Google ADH to help people unlock and automate advanced query analysis with efficiency and scale.

Amazon Marketing Cloud is a secure, privacy-preserving clean room solution, which allows businesses to perform analytics across pseudonymized signals, including Amazon Ads signals as well as their own inputs.

Using Habu for Amazon Marketing Cloud, businesses can quickly maximize the value of ads run on Amazon properties and their own data sets without requiring advanced technical or data science skills.

Habu’s intelligence layer automates the querying process and provides marketer friendly reporting to work more effectively within Amazon Marketing Cloud. A library of pre-built queries in plain language and visualizations also make it easy to start uncovering insights.

Implement data collaboration with media partners to discover ways to enrich customer profiles and gain a more complete picture of consumers to deliver more relevant messaging and experiences.

Supercharge acquisition and retention by building more robust audiences with high quality data from strategic partners in a clean room environment. Seamlessly go from insights to activation to improve targeting and conversion rates and reduce wasted media spend.

Use Habu’s library of pre-built queries to automate reach and frequency reporting or build your own custom queries to answer the questions that matter most to your business, like incremental performance.

With Habu, you can merge household-level behavioral data with retailer purchase data in clean rooms. This enables CPG data science teams to execute machine learning without the model owner accessing the data or the data owner accessing the machine learning model.


The State of Data Collaboration

Understand what’s happening in data collaboration in today’s privacy-first world

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