The volume and value of data produced by enterprises every day is huge. Organizations looking to tap into that potential in effective and ethical ways are increasingly creating collaborative data ecosystems to safely share data with carefully selected partners.
Insights from data collaboration can deliver a range of benefits including improved customer experience, enhanced audience reach, campaign performance measurement and new commercial opportunities.
Data clean rooms and other data collaboration platforms provide a secure environment for sharing sensitive data while respecting user privacy.
What is a data clean room?
Data clean rooms (DCRs) provide a safe, neutral, and protected space in the cloud in which multiple organizations can share datasets in a controlled, legally compliant manner. Clean room technology for data enables partners to exchange aggregated and anonymized data without exposing any sensitive, personally identifiable information (PII).
The anonymized data can be matched back to each partner’s owned data for a range of strategic analytical purposes – see DCR use cases below. By providing access to other organizations’ first-party data, data clean rooms are a popular conduit for accessing high-quality second-party data.
DCRs promote data governance
Working with clean room providers, data collaboration partners define the parameters of the sharing agreement – the specific data sets, levels of access, usage, and timeframes. This shared data governance is then overseen and enforced by the DCR provider, which also ensures that data sharing remains compliant with all relevant privacy regulations such as the CCPA or GDPR.
| Data jargon buster: |
| Zero-party data: Data intentionally and proactively shared with an organization by a customer such as a poll or a survey (often in return for discounts or other benefits). |
| First-party (1P) data: Data collected directly by a company about its customers’ interactions and transactions on its platforms and services. |
| Second-party (2P) data: Data that is ethically and legally shared between two companies, often under a partnership agreement, for their mutual benefit. |
| Third-party (3P) data: Data about people ethically sourced from data providers, advertisers, or other external third parties that have no direct relationship with the people. |
Data governance collaboration
This multi-layered approach – overseen by data governance experts at reputable DCR providers such as Acxiom, AWS Data Clean Rooms, Databricks Clean Rooms, or Snowflake Data Clean Rooms – ensures data collaboration in no way compromises data security.
What’s more, DCRs ensure data sovereignty as customer information never leaves its environment, eliminating data transfer. Within the secure DCR space, data partners grant access to specific audience elements and then revoke sharing post-completion. Brands maintain full data control throughout.
Data clean room use cases
DCRs enable enterprises to exercise powerful data management, data modeling, and data analysis that would be inaccessible without collaboration. Valuable insights from collaborative data sharing within the DCR can be used to understand customers better, serve them more effectively, find new customers, find new markets, find new commercial opportunities, and extend customer lifetime value. Amongst other things.
Popular use cases to which a data clean room solution can be put include:
Advertising campaign attribution and measurement
Even though omnichannel advertising includes a lot more channels than it used to, DCRs enable more accurate attribution of advertising and marketing ROI for full campaigns. By cross-referencing their conversion data with media publisher data, organizations can use data analytics to objectively determine their cross-channel return on spend.
Build audiences, extend reach
One of the most common uses of secure data collaboration within a DCR is audience building. Organizations have a direct line into partner brands’ customer data – albeit aggregated and anonymized. By carefully selecting the correct collaboration partners with complementary or overlapping audience characteristics, brands can grow their audiences very quickly.
Develop specific lookalike audiences
A huge advantage to sharing information securely with data collaboration partners is the ability to develop lookalike audiences. Data analysis of a brand’s high lifetime value customers reveals traits, preferences and characteristics – all of which can be identified within partner data aggregated within the data clean room. In this way the DCR provides the means to curate new, high-potential audiences resembling the brand’s strongest LTV customers.
Customer and market prospecting
DCRs can play a crucial role in developing a brand’s customer and data strategy. The clean room environment provides the ideal space in which to run test-and-learn programs, and trial new markets or customer groups without having to commit to the time or expense of a full campaign. Using aggregated data from a diverse range of collaboration partners, organizations can reach out to prospects or dip their marketing toes in the water of new sectors or demographics.
Enhanced audience understanding and personalization
Secure data collaboration within a DCR enables brands to use partner datasets to gain greater understanding of their own customer base. Gaining intelligence on everything from their interests and channel preferences to buyer behaviors and credit bureau histories allows more granular segmentation and ultimately more tailored engagement personalization. Not only does this enhance customer experience and promote loyalty, it also helps pinpoint new opportunities to increase the share of wallet.
Internal data management
DCR use cases aren’t limited to external collaborations. A clean room is also an excellent environment in which to address internal data management challenges. All businesses can be prone to developing data silos or data disconnects. Combining, cleansing, and aligning their disparate datasets within the DCR enables organizations to build comprehensive, enterprise-wide data resources to feed back into their tech stack. Combined with enterprise identity solutions, this data resource provides the ideal foundation for customer engagement.
Joint marketing and loyalty programs
DCRs offer the ideal springboard for impactful joint marketing or combined loyalty programs. Complementary brands can securely combine and analyze their customer data to identify overlapping customer segments and shared preferences. This provides the foundation to design integrated loyalty programs or marketing campaigns that offer joint rewards and benefits. The DCR data governance ensures that, despite this integrated approach, strict data privacy and confidentiality are maintained with regard to customers’ personal information.
Collaboration opportunity analysis
Entering into a data collaboration represents a significant investment of time, money, and brand equity. It’s vital to identify suitable partners and quantify the likely outcomes of collaborations before committing. DCRs provide a safe space to analyze samples of prospective partner data and assess the partnership potential for increasing specific KPIs (such as revenue or engagement). Sample information can be tested for efficacy and effectiveness in discrete test campaigns, without ever exposing the underlying data.
Data clean room vs CDP
DCRs and customer data platforms (CDPs) provide discrete but connected services within an organization’s tech stack. As detailed above, data clean rooms are designed to securely combine and analyze internal and external datasets for individual, time-limited projects.
A customer data platform meanwhile aggregates and unifies a brand’s customer data – all first-party data and third-party data stored across its own data architecture, in each data warehouse, data cloud, or data lake – into detailed personas. Organizations use these CDP personas to orchestrate and execute personalized customer engagement campaigns.
CDPs and DCRs are frequently used together to enhance data impact and customer engagement success. The data clean room allows secure analysis of combined customer data from multiple sources, while the CDP integrates these insights to update and enhance customer profiles and segments. This closed-loop system ensures continuous improvement in customer understanding and marketing personalization efforts.
Acxiom data clean rooms services
Acxiom provides a range of data clean room solutions, designed to help brands uncover new growth strategies, reach new audiences, and identify their most loyal customers. Our DCRs services ensure the perfect foundation for privacy-compliant co-marketing campaigns.
Learn more about how our data clean rooms work with this short ‘Digital Thoughts’ video.
Beyond DCRs: data collaboration platforms
While both popular and extremely effective, when it comes to building data collaboration partnerships there are other options than data clean rooms. These options are often categorized under the broad title of data collaboration platforms.
Strictly speaking, a DCR is just a particular form of a data collaboration platform, but this term tends to be used to describe data-sharing solutions whose purpose, scale, and characteristics diverge from data clean rooms.
What is a data collaboration platform?
A data collaboration platform (DCP) is a software solution that enables multiple organizations to seamlessly share and collaborate on data assets and joint decision-making in a centralized data cloud.
But where DCRs tend to focus on discrete, timed campaigns, data collaboration platforms are designed for larger, longer-term initiatives. A DCP tends to scale easily to accommodate both large volumes of data and fluctuating numbers of collaboration partners.
Other common characteristics of a data collaboration platform include:
- Data standardization and simplification
Data collaboration platforms ingest, align, and standardize data from all participants in order to create consistency and simplicity of analysis. - Self-service data collaboration tools
Many data collaboration platforms include a suite of self-service tools enabling data exploration and creation of combined data models. These tools offer services from raw data visualization and joint analytics to data querying and project management. - Automation and machine learning
Part of the reason these platforms are adept at processing large datasets is their use of machine learning to process and analyze a constant flow of big data from multiple sources. Automated reporting and data sharing create a friction-free collaboration environment. - Data sovereignty
Similar to DCRs, collaboration partners retain full control over their own data assets at all times. While data is shared and collaboratively worked upon, strict access controls governed by the platform operators are put in place to protect sensitive information.
The emerging adtech / martech DCP evolution
Many current DCP discussions focus on a specific set of applications for data collaboration between brands, agencies, and media. According to a recent report from Winterberry Group and Adobe, for example, brand adoption of data collaboration platforms has more than doubled since 2020 – rising from 23% to 47% in 2024.
A key driver of this growth is the shifting regulatory landscape and impending cookie signal loss.
With the stop-start deprecation of third-party cookies amplifying the impact of tightening privacy regulations, brands have had to rethink their reliance on ad servers to reach publisher audiences. DCPs have provided the answer, creating a secure, scalable environment in which to form data collaborations with publishers.
In pursuit of consistent audience segmentation and campaign measurement, many brands have doubled down on DCP use combining internal and external audience information. By incorporating owned media channel data with publisher data, DCP solutions are being used to orchestrate omnichannel audience engagement.
In this rapidly evolving space, the Winterberry report points to five main DCP formats being used to fulfill this function:
- Standalone DCPs
- DCPs with embedded audience identity solutions
- Data warehouses / data lakes with integrated DCPs
- CDPs with integrated DCPs
- Walled gardens with embedded data collaboration
There are pros and cons to each approach spanning costs, ease of tech stack integration, flexibility, vendor dependence, solution interoperability and resource requirements. However the benefits of delivering privacy-compliant, tailored engagements at scale mean this application of DCPs seems likely to accelerate.
- See also, Acxiom’s:
Data collaboration: privacy and consent
Whether using a data collaboration platform or a data clean room, a privacy-first attitude should be baked into any approach. Clear respect for, and adherence to, peoples’ data privacy rights is essential for collaboration success and brand reputation. This is a twofold responsibility.
Firstly, organizations need to ensure that before entering into a data collaboration partnership their customers should be:
- Made aware of plans to share their data
- Reassured that any PII will be anonymized
- Provided with a penalty-free means of opting out
- Notified that collaboration partner brands may contact them
Secondly, it’s important to vet how potential partners have consolidated their customer data, ensuring full compliance with any and all data privacy regulations (see sidebar). This due diligence process should be completed well before entering any data partnership.
Suitable data partners should have robust data privacy protocols and be transparent regarding data provenance.
- See also:
Data privacy is as complex as it is important
The regulatory landscape for data privacy is not straightforward, and it’s evolving all the time. There are multiple overlapping privacy laws governing different countries, which data collaboration partners need to understand and apply.
In the U.S. for instance there is no single data privacy framework. While a draft American Privacy Rights Act (APRA) is under discussion, privacy rights are currently protected by a patchwork of state and industry-specific laws such as the California Consumer Privacy Act (CCPA), the Health Insurance Portability and Accountability Act (HIPAA), the Gramm-Leach-Bliley Act (GLBA), and the Family Educational Rights and Privacy Act (FERPA).
Across the Atlantic it’s a little simpler, with data privacy for much of Europe governed by the General Data Protection Regulation (GDPR). Most countries have some type of data privacy protections. Getting expert advice on compliance with these regulations is a sensible approach.
