Data composability: A blueprint for modern data management | Simon AI

Data composability: A blueprint for modern data management

Author
Matt Walker
CTO & Co-Founder

Data composability represents a paradigm shift in data management by emphasizing integrating data-centric applications directly on a cloud data warehouse architecture. This approach contrasts traditional data strategies prioritizing data centralization over application-driven value.

By focusing on the interoperability of data components and their seamless interaction within a composable framework, organizations can achieve greater agility, efficiency, and scalability in their data operations.

You can read my previous article about the importance of a data composability strategy and the various applications essential for leveraging your centralized data.

Data composability creates a flexible, scalable, and interconnected data environment where components — like data sources, models, and applications — can be assembled and reassembled to meet evolving business needs. This approach ensures that your data assets remain dynamic and adaptable, enabling rapid innovation and responsiveness. With data composability, you can be confident that your data strategy is future-proof and ready for whatever changes come your way.

What is data composability, exactly?

Data composability is the data-centric counterpart to application composability, akin to how composable commerce leverages the MACH architecture (microservices, API-first, Cloud-native SaaS, headless). It emphasizes the interactions of data applications built directly on top of a cloud data warehouse architecture.

Apps are data applications on top of the cloud data warehouse architecture
Where possible, data remains within the CD, and most apps “bring compute” to the data
It is composed of a logical pipeline of modular stages with bounded context and robust interfaces
Extends end-to-end beyond modeling, through data applications, and back to generated data capture

It also fundamentally exposes data to an organization throughout its traditionally CDP-managed lifecycle. Rather than internally processing data opaquely and then exporting it into downstream marketing channels, a data-composable approach allows an organization to power additional, potentially non-CDP, use cases such as inventory management, supply chain optimization, or customer service improvement from each stage of data processing and keep the data within the CDW throughout more of the process.

Data composability principles

Let’s break down the core principles of data composability.

Data proximity: data predominantly remains within the CDW, minimizing data movement. Most applications bring computing to the data rather than transferring data to the computing environment, enhancing efficiency and security.

Modular design: The data processing pipeline is structured as a series of logical, modular stages, each defined by a bounded context with a robust interface. This modularization ensures a clear separation of concerns and facilitates easier management and scalability.

End-to-end integration: The composability extends from data modeling through various data applications to the capture of the data they generate. This comprehensive approach ensures a continuous loop of data utilization and enhancement.

Cross-organizational accessibility: Data is made accessible at all stages of processing and modeling, enabling a wide range of cross-organizational use cases. This openness allows for greater collaboration and innovation across different departments.

Extensible utilization: By exposing data at various stages, data composability supports extensible uses, from advanced analytics and machine learning to real-time decision-making and operational reporting.

How is that different than a traditional SaaS CDP?

In traditional SaaS CDP environments, data is often exported to and processed in a separate system, limiting visibility and control over the data. These platforms typically use a closed environment for processing, and scalability depends on the provider’s capabilities, which may not always align with your organization’s growth or needs.

Conversely, a data-composable CDP utilizes the cloud data warehouse architecture to keep data centralized and secure within your cloud environment. This approach reduces costs associated with data movement and enhances security by leveraging cloud-native protections.

How is that different than reverse ETL?

Reverse ETL vendors can provide you with a head start in tooling that lets you put together a stage more quickly; however, you’ll find their view is limited to how components work with maybe some illustrative recipes for integrating components into a use case but lacks the broader strategic view of your whole ecosystem.

In contrast, a composable CDP is inherently designed to ensure data composability across various stages of data handling without requiring users to possess excellent technical skills. Thanks to its largely no-code interfaces and AI-assisted functionalities, a composable CDP democratizes data access, allowing users from various backgrounds to engage effectively with data. This ensures that even the most challenging data workflows are streamlined and efficient, minimizing the user’s effort and maximizing the business value extracted from the data.

Data composability benefits

Data composability offers several benefits:

Cross-org utility

Each module in the pipeline is designed with a bounded context, ensuring that interactions between modules are well-defined and manageable. This design allows different departments within your organization to effectively tap into the data pipeline.

Security and governance

With data composability, your organization maintains complete control over the security and governance of your data. This control defines who can access the data and what actions they can perform with it.

Swappable components

The architecture of a composable CDP is inherently modular. Each stage of the data pipeline acts as a distinct module, allowing you to customize your setup by selecting the best components suited to your needs.

Cost efficiency

One of the primary advantages of data composability is the significant cost savings. Keeping data within your cloud data warehouse and minimizing replication saves on storage costs and reduces operational complexities.

Theory vs. reality

One strong advantage of selecting a composable CDP to begin tackling powering these data applications for your organization is that you can draw upon the CDP’s applications as well as the data-side artifacts that drive them.

Conclusion

In this deep dive into data composability, we've explored how this approach can revolutionize your organization's use of data. By centering on data applications and maintaining data within the cloud data warehouse, data composability offers unparalleled flexibility, scalability, and efficiency.

A composable CDP, like Simon Data, is a cornerstone of this strategy. Providing a framework for data modeling, integration, and application development empowers organizations to unlock the full potential of their data.