Is the CDP Still Queen? Exploring the Future of Customer Data

The Gist

• CDPs are no longer the unquestioned center of the stack. Exploding data volumes, AI-driven tooling, and cloud warehouse maturity are pushing businesses to reevaluate whether traditional CDPs still meet modern needs.
• New architectures offer greater flexibility and governance. Warehouse-native, composable, and zero-copy approaches promise real-time activation, lower duplication, and stronger privacy controls compared to classic all-in-one CDPs.
• The future is hybrid, agentic, and AI-powered. Experts predict a shift toward generative, orchestration-first systems where CDPs act as governed coordination layers—integrating identity, policy, and real-time intelligence across the entire data ecosystem.

Customer Data Platforms (CDPs) were once hailed as the answer to marketing’s biggest pain point: fragmented data and siloed customer insights. But as enterprise architectures evolve, data volumes explode and privacy regulations tighten, a new debate is emerging—are traditional CDPs still fit for purpose, or are businesses moving toward more flexible, composable and warehouse-native approaches?

Today, the customer data stack is being reshaped by advances in AI, the rise of zero-copy integration, and an increased demand for real-time personalization and privacy-by-design.

This article explores whether traditional CDPs still sit at the center of customer data strategy, or if the future belongs to a new breed of agile, interoperable and AI-powered solutions.

The Changing Customer Data Landscape

For much of the past decade, CDPs have been hailed as the gold standard for managing and activating customer information. By promising a unified “single source of truth,” CDPs enabled brands to break down data silos, personalize experiences and orchestrate campaigns with unprecedented precision. Businesses rushed to adopt these platforms, viewing them as essential infrastructure for digital marketing and customer experience.

But the landscape is shifting fast. The explosion of new data sources, evolving privacy regulations and advances in AI-driven analytics are challenging the long-held supremacy of CDPs. Some businesses are questioning whether a single platform can truly keep pace with the complexity and speed of modern customer journeys. As data moves closer to the cloud, and real-time orchestration becomes the new baseline, the idea of a centralized “truth” is being re-examined—and new approaches are emerging.

The CDP debate is no longer about whether these platforms have value, but about how data strategies are evolving.

Christian Monberg, CTO and head of product at CDP provider Zeta Global, told CMSWire, "By 2026, static enterprise software will give way to generative interfaces that assemble themselves at the moment of use. We’ll stop building products and start building protocols for creation." Monberg argued that the CDP era is evolving, not ending. He predicted a shift toward generative, agentic and composable systems where marketers interact with data in real time through conversational interfaces, rather than relying on static dashboards.

Related Article: Inside the CDP Illusion: When Data Dreams Meet Mid-Market Reality

Frequently Asked Questions on Customer Data Platforms (CDPs) in 2025

This FAQ highlights the core themes of CDP evolution, warehouse-native architectures, zero-copy activation, AI-driven orchestration and how businesses can choose the right customer data strategy for 2025.

The Case for CDPs — Then and Now

Customer Data Platforms emerged to address a pressing pain point: fragmented customer data that is scattered across marketing, sales, service and legacy systems. Before CDPs, building a unified customer profile was a tedious, manual, error-prone process—often leaving brands with incomplete insights and disjointed customer experiences.

CDPs were designed to pull data from multiple sources, resolve customer identities across devices and channels, and create a single, actionable profile for each customer. As a unified data source, the CDP enabled businesses to activate personalized marketing, orchestrate journeys and measure outcomes with far greater accuracy.

Today, CDPs still deliver significant value in several core areas. Identity resolution—matching disparate data points to a single customer—remains a foundational need for any personalization effort. CDPs excel at unifying profiles, enabling precise audience segmentation and pushing data to downstream systems for real-time usage. For businesses that are still struggling with siloed data and inconsistent customer views, the right CDP can serve as both a catalyst for digital transformation and a practical engine for cross-channel engagement.

Classic CDPs still serve vital needs—particularly where marketing needs out-of-the-box functionality, consent management and easy integrations. Andrew Romanyuk, co-founder and SVP of Growth at Pynest, noted that this is especially true for mid-market and regulated industries with many SaaS systems. 

Similarly, Katie Austin, senior developer advocate at CDP provider Progress Software, told CMSWire that traditional CDPs remain the most straightforward path to identity, segmentation and activation when time-to-value is the priority. She added that as data maturity and governance needs grow, many businesses evolve toward hybrid models that layer warehouse control beneath the CDP.

New Challengers: Warehouse-Native, Composable and Zero-Copy Architectures

As cloud adoption accelerates, new approaches to customer data management are gaining ground—each challenging the traditional role of CDPs in the marketing tech stack. At the forefront is the rapid rise of warehouse-native architectures, powered by platforms like Snowflake, BigQuery and Databricks. These cloud data warehouses offer businesses the scale, flexibility, and speed to centralize massive volumes of data, enabling advanced analytics and machine learning (ML) directly where the data already lives. 

The New Customer Data Stack: Key Approaches Compared

As customer data architectures evolve, organizations are evaluating a wider range of tooling options to support identity resolution, activation, analytics and governance. From turnkey CDPs to warehouse-native and zero-copy models, each approach comes with different trade-offs. The table below outlines how the major data stack strategies compare across strengths, ideal use cases and limitations.

The shift doesn’t stop there. Composable stacks—built on API-first tools that “snap together” as needed—give businesses unprecedented control to assemble custom solutions from best-in-breed components. This modular approach breaks free from the “one platform to rule them all” mentality, letting businesses tailor data pipelines and activation workflows to specific use cases, without being locked into a single vendor ecosystem.

Perhaps most disruptive of all are zero-copy CDPs and direct-to-warehouse strategies. Instead of duplicating data through complex ETL (Extract, Transform, Load) pipelines—which require constantly moving, reformatting and syncing data between systems—zero-copy CDPs activate it directly where it already resides. This allows real-time activation and analytics without compromising governance or compliance. By eliminating unnecessary data movement and ensuring that analytics always operate on the freshest, most complete information, zero-copy architectures are simplifying operations and setting a new standard for efficiency and privacy.

Romanyuk reiterated, "The main reasons for abandoning ‘pure’ CDPs are warehouse-native with zero-copy, increasing demands for transparency and PII control, plus placing ML/AI directly in the data warehouse," and stressed that modern stacks favor governed, in-place analytics with minimal duplication. Classic CDPs still help mid-market and regulated firms with out-of-the-box IDs, consents, and connectors—but the center of gravity is moving to the warehouse.

Related Article: Which Is Broken: Your CDP or Your Customer Data Management?

AI and Privacy: Forces Accelerating Change

Two forces are fundamentally reshaping the customer data environment: the relentless advance of AI and the tightening web of global privacy regulations. Together, they are accelerating the shift away from traditional, all-in-one CDPs toward smarter, more flexible approaches.

The real differentiator for the next generation of Customer Data Platforms is how they embed AI and adapt to mounting privacy expectations. Monberg believes that the next generation of marketing infrastructure is going to be agentic, composable and generative. "AI only succeeds when it operates across every layer: apps, data, users, brand, and business outcomes," Monberg said.

In addition, Monberg pointed out that future-ready platforms will rely on agentic AI working across applications, data and business logic—without friction. Success depends on integrating intelligence at every layer while respecting user privacy and control.

Even as AI introduces greater flexibility and automation, businesses should remain vigilant about emerging risks around privacy and data governance. Romanyuk noted that while AI agents accelerate experimentation, recovery and consent validation, they also introduce new risks: policy creep, the introduction of harmful data or prompts, and the silent accumulation of personal information.

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Related Article: How CDPs Bridge the Customer Data Gap CRM Can't

AI-Powered Data Orchestration and Personalization

Artificial intelligence is now at the heart of modern data strategies. AI-driven tools can unify, clean and enrich customer data at scale, unlocking advanced personalization and real-time decisioning that go far beyond basic segmentation. By connecting siloed information and predicting customer needs, AI enables businesses to provide contextually relevant experiences at every interaction—raising the bar for what’s possible in engagement and loyalty.

At the same time, AI-driven personalization requires governed, live data. That pushes architectures toward warehouse-native access and reframes the CDP’s role.

Artur Balabanskyy, co-founder & CTO at Tapforce, told CMSWire, "CDPs must evolve into orchestration layers, enforcing traceability and policy, rather than acting as another data silo." Balabanskyy suggested teams should reassess CDPs when activation is slow, data is copied repeatedly, or compliance is weak. 

Privacy Regulations and Data Residency

Regulations such as the GDPR, CCPA, and a patchwork of local laws are making compliance more complex than ever. Strict rules around data residency and consumer consent require businesses to know exactly where customer data is stored, how it is used, and who has access. This is driving a move toward architectures that keep data centralized (or even virtualized) within compliant cloud warehouses, minimizing unnecessary movement and exposure.

First-Party Data and New Consent Models

With third-party cookies fading and data brokers under scrutiny, businesses are doubling down on first-party data—information that is gathered directly from customer interactions, with explicit consent. New models for permission and preference management are becoming central to both compliance and customer trust, with transparency and user control embedded into every touchpoint.

Hybrid Models and “CDP-Plus” Approaches

Because of the continually evolving state of customer data, the reality for most enterprises isn’t a binary choice between classic CDPs and new cloud architectures—it’s a blend of both. Many businesses are adopting hybrid models, layering modern warehouse-native or composable solutions on top of their existing CDPs to unlock the best of both worlds.

CDPs and New Architectures Coexisting

Rather than ripping out legacy platforms, businesses are finding value in using CDPs for identity resolution and audience management, while taking advantage of cloud data warehouses for advanced analytics and AI-driven orchestration. This hybrid approach allows teams to centralize and activate data efficiently, without sacrificing the unified profiles and marketing integrations that CDPs still deliver so well.

CDPs are becoming an intelligence/coordination layer that can operate packaged, hybrid or fully composable—with zero-copy activation where possible.

Michele Nieberding, director of product marketing, AI at CDP provider Treasure Data, told CMSWire, "The CDP era is evolving. The platforms that win will have the deepest context and most trusted, governed data powering their agents. The Intelligent CDP becomes the system of record and coordination." Nieberding suggested that the differentiator isn’t the chat UI but rather, is the data "brain." She positioned modern CDPs as enabling activation anywhere—ideally without duplicating data—while preserving identity, governance, and real-time context.

Vendor Convergence: CDP + Activation + Analytics

Vendors are also moving quickly to meet these changing needs, expanding traditional CDP offerings with native analytics, data activation, journey orchestration, and even real-time decisioning. The result is a new generation of “CDP-plus” platforms that promise unified data, advanced insights, and omnichannel execution—all under one roof. For businesses, this convergence means fewer integration headaches and a more cohesive customer view, even as underlying data architectures become more flexible and cloud-native.

John Steinert, head of thought leadership and content creation at Informa TechTarget, told CMSWire that as a marketing leader, he wants to be very outcomes-focused and pragmatic. The ongoing evolution of CDP and data infrastructure often leads to technical debates about architecture, but he said that the real priority should be enabling business users and aligning on a single version of the truth. "And I want all the functional players to be enabled to act more effectively and more quickly in their areas of expertise. The infrastructure we need is one that drives better actions, faster."

Steinert suggested that the ultimate test for any architecture is whether it empowers frontline go-to-market teams to move quickly and collaboratively. He suggested the most successful businesses are those that enable users across sales, marketing, and product to act on data, rather than debate the underlying architecture.

Making the Right Choice: Decision Points for 2025

With so many options on the table, choosing the right customer data approach can feel daunting. The reality is, there’s no one-size-fits-all answer. Each strategy comes with tradeoffs, and the best solution depends on your business’s goals, data maturity, and future roadmap.

When Is a CDP Still the Right Answer?

If your business needs fast, out-of-the-box identity resolution, unified customer profiles, and turnkey integrations with marketing and CX tools, a classic CDP remains a strong choice. CDPs excel for teams with limited data engineering resources or those seeking to break down silos quickly, especially in environments where marketing and business users need direct access to audience-building and activation.

Choosing between a classic CDP, warehouse-native approach, or a hybrid depends on your current resources, regulatory demands, and digital maturity. Romanyuk suggested that brands should "Stick with a CDP if you need classic marketing management and don't have many data engineers on staff. Warehouse-native is suitable for situations where you already have models and reverse ETL, and you want a single source of truth without copying data. A hybrid is ideal if profiles and consents are in a DWH, while a ‘thin CDP’ is for the interface and the latest connectors.”

He suggested that CDPs are best for teams lacking data engineering expertise, while warehouse-native and hybrid models make sense for businesses with robust analytics environments and a need for greater flexibility and transparency. 

Decision Points for 2025: CDP vs. Warehouse-Native/Composable

As organizations reassess their data foundations for 2025, many CX and marketing leaders face a pivotal architectural decision: whether to double down on a traditional customer data platform or shift toward a warehouse-native, composable approach. Each path carries different implications for identity resolution, activation, analytics maturity, and long-term scalability. The comparison below outlines the key decision factors teams are weighing as they plan next-year investments.

Steinert suggested that, from a commercial standpoint, "If our data isn’t getting to frontline players in a way that enhances their performance, we have a problem. If our clients have difficulty realizing the differentiated value of our data, we have a problem.”