Why Data Collaboration Projects Fail — and How Yours Can Succeed with a Data Clean Room

As privacy standards continue to evolve, businesses face a dual challenge: to uphold ethical standards for data use while seizing the opportunities offered by data collaboration. Enter data clean rooms: a privacy-enhancing solution that allows organizations to share valuable insights without compromising compliance.* If you're new to data clean rooms, our recent blog post “Data Clean Rooms Explained: What You Need to Know About Privacy-First Collaboration” breaks down the fundamentals.

While the potential of data clean rooms is vast, implementing them successfully can be as complex as any enterprise technology project. Between navigating regulatory policies and addressing technical demands, many projects stall or fail outright. But the good news is that despite these challenges, businesses that master data collaboration through clean rooms can gain a significant competitive edge.

So why do some of these initiatives fail — and how can you ensure yours succeeds? Let’s explore the six common challenges, with actionable strategies to overcome them. 

Compliance

Navigating compliance with privacy laws and regulatory frameworks is often the primary barrier in data collaboration. Organizations must juggle compliance across different layers of corporate policies, industry regulations (such as HIPAA and FCRA in the U.S.) and government mandates (such as GDPR and CCPA). Add a collaboration partner to the equation, and the complexity increases, particularly as privacy laws continue to evolve.

To tackle these challenges, it’s a good idea to engage your legal teams at the onset of your project to identify the potential risks and address them early on. In addition, make sure teams are aligned on data usage policies and establish agreements through procurement teams. This initial legwork sets up a repeatable framework and process for future collaboration. 

Consent management and data storage strategies are equally as important to maintain compliance in data collaboration. Ensure data collection processes are transparent, and respect the preferences and permissions provided by each consumer. Consumer consent is a foundational element of successful collaboration. You should also implement robust tracking of data origin, maintaining records of how and where data was collected, as well as the approved scope of data usage to simplify compliance management later.

Governance

Compliance is impossible without strong data governance. At its core, governance is about enforcing data access and usage policies. However, in today’s complex data ecosystems — where information is often spread across multiple systems and platforms — operationalizing governance can be a significant challenge. Every new data copy is an added risk.

Fortunately, advancements in data platform technology now offer unified governance solutions that eliminate the need to move or duplicate data across systems. These tools enable secure collaboration directly within your ecosystem, simplifying governance and reducing risk.  For example, policies can be applied to control the data access for marketers and analysts, as well as control the use of that data. Additionally, organizations that align governance with business operations rather than relegating it to a single team can avoid governance becoming a bottleneck. 

Strategy

Technology alone is never enough to ensure the success of a project, and data clean rooms are no exception. A solid, well-thought-out strategy, where both business and technical teams are aligned, is essential. Without one, the concept of collaboration can feel overwhelming.

To overcome these challenges, organizations need a step-by-step approach that focuses on achieving smaller wins to build confidence and create momentum for larger opportunities. 

Start with a clear use case and a collaboration partner. For advertisers, measurement is often the first choice. Understanding the effectiveness of their campaigns is key to optimizing ad spend and paid media strategies. By identifying a media owner that has a robust first-party data set aligned with their target audience, an advertiser can unlock valuable insights.

Technology

Secure and scalable technology infrastructure is essential for any data collaboration initiative. Organizations needing to expand their data capabilities often face a choice: build their own solution or buy existing technology. However, the complexity of embedding privacy-enhancing technologies (PETs) such as encryption and anonymization can make in-house development incredibly time and resource intensive. Even those organizations that have expertise and time to spare may not be willing to build the technology themselves.

The challenges don’t stop there. Collaboration also requires integrating with a broader ecosystem of data solutions. This could involve enriching first-party data sets with third-party data, working with multiple identity vendors to match data sets without exposing PII or activating data within the paid media ecosystem.

For most organizations, the optimal path is to leverage existing capabilities through modern platforms. Data cloud providers offer integrated privacy, security and governance controls, enabling them to meet enterprise demands for secure and compliant collaboration. Vendors whose solutions align with your long-term strategy can ensure a seamless ecosystem for collaboration, data enrichment and actionability.

Interoperability

Interoperability is a hot topic in data collaboration conversations, encompassing cloud technology, region, data storage and format, and identity. Organizations want the flexibility to make their own choices while remaining unconstrained in their ability to collaborate with those who’ve made different decisions. 

From a cloud infrastructure standpoint, the usage is fragmented across the market with AWS, Google Cloud and Microsoft Azure clouds dominating. That fragmentation creates collaboration challenges. Collaboration often starts by securely matching data sets through a common key, with various solutions offering different benefits and drawbacks. Additionally, interoperability between data clean room technologies poses another challenge: What happens if two organizations choose different DCR platforms? Can they still collaborate securely and effectively?

Solution providers increasingly recognize that no single, dominant solution exists and fragmentation will continue to be a challenge. Interoperability is key to enabling multiple solutions to coexist. Organizations should select a vendor that prioritizes flexibility and advancing interoperability across technologies, identities and clouds. This approach optimizes collaboration opportunities while adapting to evolving ecosystems.

Skills

Traditionally, data clean rooms were built with data analysts and data scientists in mind, requiring advanced technical expertise. While this approach was reasonable initially, it significantly limited accessibility, particularly for organizations or collaborators lacking technical resources. The result? A widening gap in collaboration opportunities.

The real opportunity lies in making data clean rooms accessible to everyone by bridging the skills gap. Modern data clean room platforms are evolving, offering users the flexibility to choose the interface that best suits their experience — whether it’s a code-based environment for technical users or a no-code interface for business users.

Snowflake Data Clean Rooms: Powering secure and scalable data collaboration

Snowflake Data Clean Rooms, built on the trusted infrastructure of the Snowflake AI Data Cloud, offer a comprehensive solution that addresses the common challenges associated with data collaboration. Here’s how.

Compliance: With the AI Data Cloud as a backbone, Snowflake Data Clean Rooms provide organizations with a comprehensive solution for compliance, security and privacy. Snowflake Horizon provides organizations with access to a unified set of capabilities designed to align seamlessly with internal policies, industry regulations and government mandates. 

Governance: Snowflake’s sophisticated governance model enforces data usage policies across data sets, reducing the operational complexity of managing data distributed across systems while maintaining strict control over security and privacy. Snowflake also eliminates the need to move or duplicate data across systems.

Technology: Leveraging the scalability of the AI Data Cloud combined with advanced privacy-enhancing capabilities, Snowflake delivers an infrastructure that meets the needs of even the most complex collaboration use cases. Unlike some alternatives, Snowflake provides a neutral solution by neither selling data nor owning media, helping to ensure trust, transparency and unbiased data handling. 

Interoperability: Snowflake’s features allow organizations to collaborate seamlessly across regions and clouds, including AWS, Google Cloud and Microsoft Azure. The platform’s cross-cloud capabilities eliminate vendor lock-in, enabling organizations to expand partnerships. Even partners who are not Snowflake customers can connect seamlessly through Snowflake Data Clean Rooms, for a truly collaborative ecosystem that fosters innovation and growth.

Skills: Recognizing the diverse skill levels among organizations, Snowflake Data Clean Rooms are designed to support both technical experts and business users. With intuitive interfaces, these data clean rooms lower the barriers to entry for data collaboration. By addressing the skills gap, Snowflake fosters accessibility in an area often dominated by technical constraints.

Strategy: Snowflake champions a strategy-first approach to data collaboration. A primary strategy is to start with small, manageable projects and scale quickly as teams gain experience and confidence. With no up-front licensing fees, Snowflake allows businesses to explore and expand partnerships with minimal risk. 

Trusted by top publishers and industry leaders, Snowflake Data Clean Rooms provide a neutral, privacy-preserving and easy-to-implement environment for secure data collaboration.

Customer spotlight

Popular online job search platform Indeed chose Snowflake Data Clean Rooms to improve campaign performance measurement, reporting significant reductions in optimization efforts and seamless replication of processes across collaboration partners. Watch Indeed’s recommendations on how to get started with your data collaboration initiatives.

Ready to learn more? Download “The Essential Guide to Trusted Data Collaboration for Advertisers” and take the first step toward secure, privacy-first collaboration. 

Need personalized guidance? Reach out to our sales team — we’re here to help you kick-start your data collaboration initiatives with confidence.

* While DCRs are a key technical enabler, they do not inherently guarantee compliance or privacy on their own. Effective use of a data clean room requires a broader ecosystem of practices, policies and responsible decision-making to meet the privacy standards. Without these, there’s still a potential for missteps that could lead to privacy issues, regardless of the clean room's capabilities.