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There’s been a major shift in how enterprise organizations manage customer data — and it’s being led by data and IT teams. As cloud data warehouse technologies evolve, the composable customer data platform (CDP) is becoming the new standard.
The End of the Bundled CDP Era
Traditional CDPs used to be the norm, forcing data and IT professionals to implement bundled technologies regardless of their wants or needs. As brands raced to keep up with rising expectations and deliver more personalized customer experiences, alternative choices were limited.
But all that has changed. Thanks to the hard work of data and IT teams — and the evolution of cloud data warehouse technology like Databricks, Snowflake, Teradata VantageCloud, Amazon Redshift and Google BigQuery — the future is looking a lot more flexible.
Read on to learn why composable customer data platforms will become the new standard for enterprise companies — and how data and IT professionals can make sure they get it right.
Moving Beyond Fixed Menus
There are nearly 200 CDP vendors on the market today, according to the Customer Data Platform Institute. Data and IT teams are spoiled for choice — but only if they want to order off a fixed menu.
While there are certainly differences between vendors, most have one thing in common: traditional customer data platform architecture. So, whether you’re ordering from a hyperscaler or a startup, the choices are limited, with little to no room for customization.
That puts IT teams in a tough spot. They need flexibility to architect a customer data infrastructure that fits their unique needs – one that gives them control over cost, performance, and governance. Traditional CDPs make this difficult. But composable CDPs open the door to a truly à la carte experience.
Why Enterprises Are Abandoning Traditional CDPs
After years of settling, IT leaders are walking away from bundled solutions that require:
- Buying everything instead of only what you need
- Copying data from one system to another instead of deciding where it lives
- Strict system architectures that can’t adapt to evolving business needs
- Closed systems that restrict swapping new capabilities in and out as needed
Embracing Composable (Customer Data Platform) Architecture
There’s a reason data cloud companies such as Snowflake, Databricks, Google BigQuery, AWS Redshift and Microsoft Azure are making waves: They’re helping organizations embrace the power of composable technology stacks.
Composable architecture allows data and IT professionals to centralize customer data and create their own sources of truth — and eliminate the need for bundled or prepackaged CDPs in the process. With the cloud data lakes they’ve built, data and IT teams can develop a 360-degree view of their customers and tailor their customer data stacks to their unique requirements.
According to Gartner, 63% of chief information officers at organizations with high composability report better business performance when compared to industry peers. That explains why 60% of organizations plan to invest in composable enterprise technology within the next three years.
But building a 360-degree customer view is only one piece of the puzzle. Now that data and IT professionals can build the ideal CDP for their needs, the challenge is making sure it serves the needs of the whole organization.
Building a Better Customer Data Stack with a Composable CDP
Creating a comprehensive view of the customer is key to delivering better customer experiences, but data and IT teams need a way to extend the hard work of centralizing data in their cloud data lakes so business teams can leverage the benefits of data centralization for audience segmentation, journey orchestration and real-time experiences.
Reverse ETL is key—but only under the right conditions. Data and IT professionals want to focus on building and maintaining data systems, not fielding ad hoc requests. But too many applications built on top of data lakes require IT professionals to write endless SQL queries on behalf of business users.
To build a customer data stack that serves both the needs of technical and non-technical users, IT professionals must seek out solutions that give business teams a user-friendly interface that makes it easy to generate queries, have those queries automatically translated into SQL and push them down to the data lake to be executed.
By processing the results and serving them back up to the application, it allows data and IT teams to decide where data is stored and queried while making sure business users can self-serve their needs — with role-based access to data.
Connecting Your Composable CDP to Your Data Warehouse
Not all composable CDPs connect to the data warehouse in the same way – and “zero copy” doesn’t mean zero effort.
Here are the main integration methods to understand:
Reverse ETL Data Pipeline
Reverse ETL is a quick and simple way to move data from your warehouse to downstream systems. However, it is extremely limited in its ability to allow business users to orchestrate campaigns and experiences in a way that CDPs typically enable. What’s more, it only supports a one-to-one database-to-destination relationship.
Data Sharing
Data Sharing solutions provide a storage layer with access to data. The compute layer (e.g. execution of the query), however, is decoupled and not provided in this scenario.
A Data Sharing integration enables a CDP to access data in the data warehouse and use it for orchestration and other CDP functions, but it is limited to one specific warehouse and requires a lot of data copy in the process.
Query Pushdown
CDPs use query pushdown to delegate compute to where the data is stored, in this case a single cloud data warehouse. This offers both the benefits of a CDP and a high degree of benefits that composability provides, by essentially no compute or storage.
Federated Query Pushdown
Federated query pushdown goes one step further to combine data on-the-fly from cloud warehouses and local CDP data stores to power more sophisticated, real-time use cases across systems.
Pro Tip
Test all integrations during implementation to ensure your stack performs and scales seamlessly.
Selecting the Composable CDP that’s Right for You
Composable CDPs are the future. But to guarantee success, data and IT teams must choose tools they can plug and play into their existing martech stack to maximize power, control and performance.
And one more thing: AI readiness.
As AI adoption accelerates, your CDP should be built to support it. That means structured, high-quality data readily accessible to AI engines – no transformation or migration required. Example: Uniphore’s Composable CDP.
Uniphore’s Composable CDP operates on a unified, agentic AI platform that enables businesses to harness their customer data for AI applications out-of-the-box. No costly data migrations. No complex setups. Just immediate AI-readiness.
Is Your CDP Built for AI?
Learn how Uniphore’s composable CDP accelerates AI development and deployment.
FAQ on Composable CDPs
A Composable CDP is a customer data platform that allows organizations to apply marketing applications and capabilities like audiencing, orchestration and activation from their CDP solution directly into their existing data warehouse. With a composable approach, companies can select the tools and capabilities they need and integrate them together with a centralized customer data infrastructure.
A Composable CDP works by tapping into the brands’ source of truth—the centralized data store like a cloud data warehouse to support a unified customer database. Tools for segmentation, orchestration, etc. can then query the centralized data to power use cases like audience building, campaign management, real-time personalization and analysis.
To build a Composable CDP, organizations typically start by centralizing their customer data in a cloud data warehouse or data lake. They can then layer on additional tools and applications for activities like data transformation, identity resolution, audience segmentation, journey orchestration, etc. The key is using technologies that can plug into the central data repository and integrate together for a seamless CDP solution.
Traditional, pre-packaged CDPs provide an all-in-one customer data platform as a single product bundle. In contrast, a Composable CDP allows companies to build their own customized CDP using separate best-of-breed tools for different requirements like data ingestion, identity resolution, segmentation, etc. This provides more flexibility than traditional CDPs.