How to implement a composable CDP: A crawl, walk, run approach

Composable architecture is an evolving concept in the martech landscape, characterized by its emphasis on modularity and flexibility. While the idea of composability varies depending on the context, at its core, it focuses on breaking down systems into interchangeable, modular components that work seamlessly together. For modern martech solutions and customer data platforms (CDPs), this means creating a flexible system that can adapt to evolving business needs.

MessageGears’ Caroline Nash recently spoke with Darren Rankine from Transparent Partners about how brands can transition to a composable architecture using a phased approach. They discussed the benefits of composability, the potential challenges of implementation, and the need to align technology with business goals.

This article highlights key takeaways and breaks down a “crawl, walk, run” methodology for moving from a traditional, monolithic setup to a more flexible, component-based system. Whether you’re starting your journey toward composability or refining your strategy, this guide offers valuable insights and actionable steps for a smooth transformation.

What is composable architecture?

Modularity and interoperability

Unlike traditional monolithic tech stacks, where all components are integrated across a network of data pipelines, composable systems break down functionalities into distinct, interchangeable modules that operate independently but can work together seamlessly. This modular approach promotes interoperability — the ability of different systems and software to exchange and make use of information.

Warehouse-native approach

Across CDPs and broader martech solutions, composability often refers to a warehouse-native approach. This involves shifting marketing processes and data management functions traditionally handled by a packaged CDP to a brand’s own data warehouse environment or data lake. By centralizing functions like ID resolution, data management, and enrichment within the data warehouse, brands maintain control over their data, ensure consistency, and reduce unnecessary data movement.

Reducing vendor lock-in

One of the significant benefits of composable architecture is reducing vendor lock-in. By building a modular system with components that can easily plug into and out of the data environment, brands avoid over-dependence on a single vendor’s ecosystem. This flexibility makes adaptation and scaling much easier as technology and business needs evolve. It also empowers organizations to experiment with different tools and technologies to find the best solutions for their unique challenges.

The growing popularity of composability

The shift towards composable architectures is driven by several factors.

Advancements in cloud data platforms: Developments across platforms like Azure, AWS, and Google Cloud provide a robust foundation for implementing flexible systems. These platforms offer extensive capabilities for data storage, processing, and analytics, making them well-suited for a composable approach.

Data privacy regulations: With stricter data privacy regulations, brands are prioritizing control over their data. A composable architecture aligns well with these priorities by centralizing data management within a secure environment that can be closely monitored and controlled.

Greater flexibility: Brands are increasingly questioning the need for both a CDP and a separate data platform. With improved data capabilities in cloud data platforms, many organizations are finding that a composable approach lets them achieve the same goals with greater flexibility and cost efficiency.

Traditional vs. composable CDP architecture

Traditional CDP architecture

Packaged CDPs are designed as centralized hubs where brands converge various data sources and execute marketing activities. They aggregate data from multiple sources – such as first-party, second-party, and third-party data – into a unified customer profile. The CDP then handles various data management tasks, such as cleansing, identity resolution, and profile creation, before activating this data across different marketing channels.

While effective in managing customer data and personalizing marketing efforts, traditional CDPs come with a number of limitations:

Data silos: If not fully integrated with other systems, traditional CDPs can create and contribute to data silos.

Vendor lock-in: Brands using traditional CDPs often become dependent on a single vendor, limiting their flexibility and ability to adapt to new technologies.

Cost and complexity: Managing a CDP alongside a separate data warehouse leads to duplicate data storage and processing costs, as well as increased complexity in data management.

Limited measurement capabilities: Many CDPs struggle to provide robust measurement and analytics, making it challenging to evaluate the effectiveness of marketing campaigns. In this traditional setup, the CDP serves as the central node connecting data sources and marketing channels. It integrates and processes data but can become a bottleneck or a single point of failure.

Composable architecture

Composable architecture takes a different approach by modularizing the martech stack and focusing on separating core functionalities into distinct layers.

Data platform: At the center of composable architecture is the data platform, often referred to as a data warehouse or data lake. This platform serves as the centralized repository for all data, reducing redundancy and streamlining access.

Event streaming: Technology for capturing and streaming behavioral events is essential in a composable architecture. These tools collect data from various touchpoints – such as websites and apps – and feed it into the data platform in real time.

Consent management: Consent management platforms handle data privacy and consent requirements. In a composable setup, these platforms write consent data directly into the data platform, so all data is centralized and consistent.

Identity management: This component is split into two aspects: PII identity, which involves matching and unifying customer identities across different data sources, and web identity, which stitches anonymous interactions with known identities.

Activation: Tools in this layer read from the data platform. When these tools generate new data, they write it back to the data platform, ensuring data remains up-to-date.

Measurement: The measurement layer is crucial for tracking campaign performance and conducting incrementality testing. In a composable architecture, this layer is integrated directly on top of the centralized data platform, providing a holistic view of marketing effectiveness.

The benefits of composable architecture

Data centralization and accessibility: By centralizing data in a single platform, brands minimize issues related to data fragmentation and ensure all marketing processes are drawing from a single, consistent source of truth. This helps unlock a true customer-360 view.

Enhanced compliance and privacy: Centralized data management makes it easier for brands to comply with data protection regulations and handle data subject requests, tasks that can otherwise be cumbersome when data is dispersed across multiple systems.

Cost efficiency: By leveraging a composable approach, brands can avoid the redundant data storage and processing costs that come with having a traditional CDP.

Flexibility and scalability: Composable systems allow brands to easily integrate new tools and technologies as needed so they can scale their marketing efforts and respond to changing business needs.

Improved measurement and analytics: With centralized data, measurement and analytics can be more comprehensive and integrated, providing a clearer picture of campaign performance.

Transitioning to composable architecture

Many enterprise brands are exploring composable architecture as a way to enhance flexibility and agility. But the transition can seem daunting. If you’re considering this shift, understanding how to approach it incrementally can make the process smoother and more manageable.

Here’s a practical outline of a crawl, walk, run approach for moving to a composable model:

Crawl phase: Assessment and planning

The first step in transitioning to composable architecture is to assess your current setup and plan your strategy.

1. Define objectives and requirements: To guide your architecture decisions, start by clarifying your brand’s business goals. How do your marketing and data strategies contribute to these goals? Break down these objectives into specific requirements or use cases. Then, create detailed user stories or feature lists that outline what your new system needs to accomplish.
2. Establish design principles: Identify guiding principles that will influence how you’ll design your tech stack. Examples might include becoming vendor-agnostic or minimizing data movement. These principles will help ensure your new architecture aligns with your broader goals.
3. Conduct a technology assessment: Assess your current technology stack, including your CDP and data infrastructure. Understand how your tools connect, what data they handle, and how frequently this data is updated. Determine what gaps exist between your current setup and your requirements to pinpoint areas that need improvement or replacement.

Walk phase: Incremental implementation

With a clear plan in place, you can start implementing changes gradually.

1. Simplify data integration: Ensure customer data from your existing CDP is accessible in your data lake or data warehouse. This might involve creating a comprehensive customer data table with key attributes and flags, and establishing a direct connection using APIs or other data integration methods to ensure smooth data flow. The goal is to have your warehouse as your true central source of truth.
2. Find a composable CEP: Identify and implement a composable customer engagement platform (CEP) that meets your design principles and can leverage data effectively. Ensure it integrates well with your existing data environment.
3. Manage redundancies and phase out functions: Begin migrating marketing functions from your CDP to the CEP. This phase may involve some duplication of functions as you transition, but it allows you to move gradually toward a more streamlined setup. Start with low-risk functions to test the integration and ensure data flows correctly between systems. As you gain confidence, you can move more critical functions over time.

Run phase: Full transition

Once you’ve successfully tested and implemented the core components of your composable architecture, you can proceed to the full transition phase.

1. Complete data migration: Transfer all necessary data sources across your tech stack – such as customer profiles, transaction histories, and engagement data – into your centralized data platform. During this process, ensure all data is accurately formatted and tagged to facilitate easy access and analysis.
2. Deprecate the CDP: Once all data and functionalities have been successfully migrated to your new composable architecture, you can phase out the packaged CDP.

Key considerations for a successful transition

As you move through each phase, keep these considerations in mind:

Cost management: Evaluate the financial implications of migrating to composable architecture versus maintaining your current CDP. Consider the initial investment, long-term savings, and ongoing costs.

Measurement capabilities: Composable architecture makes enhanced measurement and analytics possible. Incorporate advanced analytics tools into your setup so you can justify the investment and demonstrate its impact.

Data strategy: Develop a comprehensive data strategy that covers data quality, format, accessibility, and governance. This strategy should address how data will be collected, stored, and accessed within the new architecture. It should also include clear guidelines for data tagging and structuring to facilitate seamless integration across modules.

Operating model: Assess how the transition will impact your operating model, processes, workflows, and team structure. A composable CDP often aligns marketing and technical teams and increases efficiency, but it also can change roles and responsibilities as both your team and tech evolve.  Develop a change management strategy to address changes in processes and responsibilities to help your team adapt to the new setup.

Guiding principles: Regularly review and align your guiding principles with the evolving architecture. As your business grows and your marketing strategy evolves, your architecture needs might change. Maintain flexibility in your approach and be open to revisiting your design principles and making necessary adjustments to your architecture.

Embracing the composable approach

As marketing technology and regulations continue to evolve, the shift toward composable architecture represents a strategic response to the need for greater flexibility, efficiency, and control over data and marketing operations. Unlike packaged CDPs, composable architecture offers a modular approach that separates core functionalities into distinct layers, leveraging a centralized data platform for better data management and compliance.

By following a crawl, walk, run approach with careful planning, brands can transition to a composable architecture in manageable stages, minimizing risks and ensuring a smooth transformation. With the right strategy and support, enterprise brands can unlock the full benefits of a composable architecture, from improved data centralization and cost savings to enhanced flexibility and scalability.

If you have questions or need support with your composable architecture journey, reach out to experts at MessageGears who can guide you through the process. With extensive experience in helping brands transition to composable architectures, our data specialists can assist you in making informed decisions and ensuring a successful transformation.

Whether you’re just beginning to explore the possibilities or are ready to take the leap, having the right support can make all the difference in achieving your data management goals.