Data Collaboration Platforms: A Game-Changer in Digital Marketing

Published on February 15, 2024

Last modified on March 21, 2025

TL;DR: In a world with or without third-party cookies, data collaboration platforms are emerging as essential for digital marketers to effectively use first-party data while respecting privacy. Data collaboration platforms facilitate the combination and analysis of internal and external data, offering insights for informed decision-making and enabling targeted advertising strategies. Benefits include centralizing data, extracting insights, enriching data, and activating it across platforms.

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Data Collaboration Platforms: What You Need to Know

Despite Google Chrome’s latest decision to leave third-party cookies up to the user and browser settings, the industry is still scrambling to find new ways to understand customers and execute targeted advertising in a world where the third-party cookie is scrutinized.

In the midst of this uncertainty, a powerful solution has emerged – data collaboration platforms. These next-gen technologies are completely changing the game when it comes to making the most of first-party data in a privacy-centric landscape.

In this guide, we take a deep dive into the world of data collaboration. Together, we explore what these game-changing platforms are, why they matter more than ever before, what key features to look for, and how to chart your roadmap to adoption. Let’s get collaborating!

What Is Data Collaboration?

Data collaboration, as defined by the Interactive Advertising Bureau (IAB), is a technological process that involves combining and analyzing data within a company or alongside partners for various purposes. These use cases can range from integrating internal data silos to crafting targeted campaigns. The essence of data collaboration lies in its ability to turn raw data into actionable insights, fostering informed decision-making and strategic planning and execution.

Customer Data Platforms vs. Data Collaboration Platforms

Marketers today have a wealth of technologies at their disposal, but not all platforms serve the same purpose. While Customer Data Platforms (CDPs) and Data Collaboration Platforms (DCPs) both deal with first-party data, they cater to different needs. Understanding their key differences can help businesses choose the right tool for their data strategy.

Customer Data Platforms (CDPs)

A CDP is designed for individual businesses to centralize and manage their first-party customer data. These platforms collect, unify, and activate data across marketing and advertising channels, providing a single customer view for personalized experiences. However, CDPs have limitations:

Internal-Only Data – CDPs primarily work within a company’s own data ecosystem and lack built-in capabilities to securely collaborate with external partners.
Limited Scale – Without access to external second- and third-party data, businesses may struggle to enrich their insights and expand reach.
Personalization Focused – CDPs excel at customer segmentation and personalization, but they aren’t designed for cross-company collaboration or advanced data sharing.

Data Collaboration Platforms (DCPs)

Data Collaboration Platforms, like Lotame’s Spherical, go beyond CDPs by enabling secure data partnerships between companies. They offer:

Cross-Partner Data Enrichment – DCPs allow brands, publishers, and agencies to combine first-party data with external sources to build richer customer profiles.
Beyond CRM Data – While CDPs often focus on known customers, DCPs incorporate both known and unknown audiences, making them more effective for prospecting and media activation.

Maximizing Data Potential: Why CDPs and DCPs Work Better Together

Customer Data Platforms (CDPs) and Data Collaboration Platforms (DCPs) aren’t competitors—they’re perfect complements in a modern data strategy.

Many brands have invested heavily in CDPs to unify and activate their first-party customer data. But as the industry shifts toward privacy-first marketing and expanded addressability, brands are realizing the limitations of first-party data alone.

CDPs excel at understanding, retaining, and upselling to existing customers, but they weren’t built for scaling beyond known audiences. That’s where a DCP comes in.

Lotame’s DCP, Spherical, introduces complementary capabilities such as enrichment, modeling, insights and activation that go far beyond what a brand’s first-party data can tell them.

This is what’s now. This is what’s next.

Innovative brands have been ahead of the curve and have already stood up a CDP and DCP stack.

This visual depicts how a modern MarTech and AdTech stack should be used to go beyond retention and extend the application of first-party data for customer acquisition, personalized campaigns, and better advertising outcomes in the upper funnel.

CDP vs DCP

The Significance of Data Collaboration Platforms

These platforms are the epicenters where data from diverse origins converge, offering a harmonized and comprehensive view of information. They enable the seamless integration of internal and external data resources, thereby enhancing the depth, quality, and utility of data available to digital marketers.

Benefits of Data Collaboration for Digital Marketers

Centralizing Data: Data collaboration platforms offer an end-to-end solution for aggregating diverse data sources, thereby eliminating organizational data silos.
Insight Extraction: Advanced segmentation, predictive modeling, and comprehensive analytics tools within these platforms facilitate the extraction of valuable customer insights.
Data Enrichment: By enriching internal data with external sources, these platforms expand the customer view and provide much-needed scale to first-party data.
Data Activation: Data Collaboration tools can empower digital marketers to activate their data across various platforms and devices, aiding in more effective prospect engagement and customer retention.

Is Data Collaboration Right for My Business?

Data collaboration emerges as a critical strategy for organizations to extract the greatest value from their first-party data. This approach is especially beneficial for industries that are disintermediated from the end consumer and encounter specific challenges related to data acquisition, analysis, and utilization.

Consumer Packaged Goods (CPG)

The CPG industry, often characterized by limited direct-to-consumer interactions, faces unique challenges in collecting first-party data. Data collaboration platforms can bridge this gap by integrating data from various sources, enhancing consumer insights with rich and diverse data sources, and enabling more targeted marketing strategies.

Automotive Industry

In the automotive sector, there is a notable consumption of online content, yet direct transactions with an OEM versus a dealer may be limited. Data collaboration allows for the integration of online engagement data with other customer touchpoints, providing a more comprehensive understanding of consumer behaviors and preferences.

Real Estate

The real estate industry, similar to automotive, experiences high levels of online content engagement. However, the frequency of transactions can be comparatively low. Data collaboration enables digital marketers to merge online engagement data with transactional data and other relevant consumer information, aiding in more informed decision-making and personalized customer experiences.

Travel and Hospitality

This sector is marked by extensive online research and planning by consumers, but with limited direct interactions until actual booking or visitation. Data collaboration can play a pivotal role in knitting together various data strands – from browsing patterns to booking habits – to create a more complete picture of consumer behavior and preferences.

Media and Publishing

Media owners, particularly those with authentication rates below 30%, face challenges in understanding both authenticated (logged-in) and web visitors. Data collaboration platforms can assist in unifying disparate data sources, offering deeper insights into audience preferences and behaviors. This, in turn, aids in content personalization for existing subscribers and monetization strategies for advertisers.

Key Features of Data Collaboration Platforms

When scouting for a data collaboration platform, certain essential features should be considered:

Comprehensive Data Management and Integration: Efficient handling of various data-related processes is key. This includes data onboarding, unification, modeling, enrichment, and activation as well as seamless aggregation and integration of data from various sources, both internal and external. The ability to manage and integrate data is crucial for transforming raw data into actionable insights and breaking down data silos within organizations to provide a holistic view of the data landscape.
Advanced Analytics and Reporting Tools: These platforms should offer robust analytics capabilities, including AI/machine learning and segmentation tools. These features enable users to extract meaningful insights from large datasets and make informed decisions.
Scalability and Flexibility: It’s important for these platforms to be scalable to handle growing data volumes and flexible enough to adapt to various business needs and changes in the data environment.
Identity Resolution Capabilities: Effective identity resolution tools are essential for combining data from multiple sources while maintaining accuracy and privacy. This often involves using both deterministic and probabilistic approaches.
User-Friendly Interface: A platform should be accessible and easy to use, with an intuitive user interface that allows users to easily navigate and manipulate data without needing extensive technical expertise.
Cost-Effectiveness and ROI Measurement: It should provide good value for the investment, including clear metrics for measuring return on investment (ROI) and flexible pricing models suitable for various business sizes.
Ease of Integration: The platform should integrate smoothly with existing marketing tools, CRM systems, data warehouses, and other business intelligence tools, avoiding disruptions in current workflows.

3 Things to Consider When Choosing a Data Collaboration Platform

When choosing a data collaboration platform, it’s crucial to select one that aligns with your organization’s specific requirements and future growth plans. Here are three things to consider in your selection process, to ensure you make a decision that truly benefits your business.

1. Maintaining Scalability

Scalability: As organizations grow, the volume, variety, and velocity of data they handle also increase. The data collaboration platform must be able to scale accordingly, handling larger datasets and more complex data structures without performance degradation.

2. Economic Considerations:

Cost of Data Storage and Processing: Storing and processing large volumes of data can be expensive. Organizations need to carefully consider their data storage solutions, opting for cost-effective cloud storage options or data compression techniques where appropriate.
Investment in Tools and Technology: The initial investment in data collaboration tools and ongoing maintenance costs can be significant. Companies must evaluate the total cost of ownership, including software licenses, hardware, and the need for skilled personnel to manage these systems.
ROI Justification: It’s vital to measure and justify the return on investment of data collaboration initiatives. Organizations should establish clear metrics to evaluate the effectiveness of data collaboration in achieving business objectives.

3. Overcoming Common Identifier Limitations

Reliance on Common Identifiers: Many data collaboration processes depend on deterministic identifiers (like customer IDs, email addresses, or MAIDs) for merging data from different sources. This can limit scale, causing missed opportunities in unknown data territories. Choosing a solution that employs both known and unknown data ensures more comprehensive and scalable data linking.

Before selecting a data collaboration platform, it’s important to keep these considerations in mind. This will help you determine which features are non-negotiable when choosing the right platform for your business.

Steps to Implement Data Collaboration:

For businesses looking to venture into data collaboration, a strategic approach is vital:

Identify Organizational Needs: Begin by understanding the specific data challenges and objectives within your organization.
Select the Right Platform: Opt for a platform that aligns with your internal and external data needs.
Pilot Projects: Initiate with small-scale projects to test the platform’s effectiveness and compatibility with your organizational needs.
Gradual Scaling: Incrementally increase the complexity and breadth of your data collaboration initiatives as you build confidence and expertise.
Continuous Learning and Adaptation: Stay updated with the latest developments in data collaboration and continually refine your strategies based on best practices.

Introducing Lotame’s Spherical, A Data Collaboration Platform

Spherical is Lotame’s end-to-end data collaboration platform designed for digital marketers, agencies, and media owners. Spherical facilitates access, analysis, and activation of crucial first-party data, streamlining the journey from enabling data collection to consumer engagement.

Spherical stands out in the data collaboration market by offering a comprehensive suite of services that includes customer intelligence, data-informed audience creation, and identity-powered activation. With connections to their own valuable first-party data and partners of their choosing, brands can preserve and extend data connectivity, collaboration, and addressability in all environments. Our platform supports data onboarding, unification, modeling, enrichment, collaboration, and activation, catering to both the data-rich and those in need of data acquisition.

Data Collaboration Platforms vs Traditional Data Clean Rooms

While data clean rooms are integral to data collaboration, they are not a complete solution. Lotame’s Data Collaboration Platform, Spherical transcends clean room point solutions by not only addressing internal data collection and connection but also enabling the enrichment, analysis, and activation of data with external partners.

Data Collaboration Case Study: RE/MAX & Advance Local Unlock New Insights

Data empowered results are possible with Lotame’s data collaboration solution. See it for yourself in this case study, where RE/MAX and Advance Local leveraged data collaboration to uncover valuable audience insights and improve targeting. By analyzing 30,000 unique profiles, RE/MAX discovered new opportunities for engagement beyond traditional real estate targeting. As Josh Culver, Executive Director of Integrated Marketing at RE/MAX, put it, “These kind of data collaboration findings are really helpful for us, because everyone’s trying to be more efficient. Everyone’s trying to be smarter with their dollars.” This collaboration showcases how cross-industry data collaboration can lead to significant success for brands.

Future of Data Collaboration

The future of digital advertising lies in data collaboration. Continuous advancements in this technology pave the way for digital marketers to unite, analyze, and activate data in faster, smarter and easier ways. Artificial intelligence and machine learning will further revolutionize these platforms, making data analysis more intuitive and predictive.

Data collaboration platforms are not just tools; they are catalysts for transformation in digital marketing and advertising. They allow marketers and media owners to dig below the surface and unlock the hidden potential of their first-party data. With Lotame’s data collaboration platform, Spherical, marketers can mine those depths to innovate and grow their business the way they want.

Ready to unlock your data’s potential? Book a free consultation with a data collaboration expert.

The Power of Data Collaboration Platforms: A Game-Changer in Digital Marketing