Beyond CDPs: Building a Future-Proof Martech Strategy

To achieve true data maturity, organizations often don’t need an endless array of new tools. Before investing in or expanding a Customer Data Platform (CDP), consider smarter strategies to become genuinely data-driven. The focus should shift to strengthening your data foundation, empowering your people, and exploring a dual-core architecture alternative.

CDPs once promised a comprehensive 360° customer view, but the reality of their implementation can often be a disappointment. Budgets and implementation times frequently run over, data reliability can degrade, and compliance issues may arise over time. Consequently, a CDP isn’t always the most sensible next step for mid-market or smaller organizations, particularly if they haven’t adopted one yet.

This disparity between promise and practical application is supported by research. A 2022 Forrester and Zeta’s study revealed that just over half of companies found their CDP met most current needs, yet only 1% believed it would fully address future requirements. While many organizations were initially satisfied, few were convinced their CDP would remain suitable as their needs evolved. This trend is now evident across many businesses:

• Initial investments often failed to stand the test of time.
• The pace of technological change surpassed expectations.
• The future has arrived, rendering many CDPs unable to keep pace.

Where the focus was once on acquiring more tools and chasing the latest trend, it has become clear that true progress isn’t solely about technology. It’s fundamentally about the data itself and the people equipped to work with it effectively.

The Power of Data Hygiene: Two Strategies Compared

Consider a comparison between two retail clients operating under nearly identical conditions, with approximately 80% of sales generated offline and 20% online.

To become truly data-driven, simply accumulating more tools is not the answer. Begin by optimizing your existing technology stack through better data activation in the right places and with the right people. This approach doesn’t necessarily require a CDP, especially with current advancements.

This scenario is not an isolated incident; similar patterns have been observed across various brands and industries. While a CDP-led approach can still be effective for businesses operating almost entirely online, it often falls short when offline sales are significant, or when multiple brands and domains are involved.

The Evolution of the Martech Stack

Scott Brinker’s and Frans Riemersma’s MartechMap tracks over 15,000 tools, marking a 9% year-over-year increase. Their research highlights a striking shift in core martech stack usage:

• CDP centrality has declined from 27.3% to 17.4%.
• Cloud Data Warehouses have surged to 23.9%, establishing themselves as the leading data hub.
• Marketing automation platforms have risen to 26.1%, now leading activation within the martech stack.

Many teams are moving away from positioning the CDP as the central component of their stack, despite continued advocacy from some vendors and agencies. This raises the question of whether users are recognizing a trend well before some market experts.

Originally rooted in marketing departments, CDPs are increasingly shifting into the IT domain. As data becomes more strategic, its relevance expands beyond marketing to the boardroom, involving CIOs, data leads, and CFOs in critical decision-making. What began as a marketing necessity has evolved into a broader business strategy driver.

This shift creates new pressure to deliver measurable results, requiring expertise often found outside traditional marketing teams. Data-driven roles are gaining prominence, while marketers concentrate on activation with tools like personalization engines.

Despite their positioning as a strategic cornerstone, Customer Data Platforms (CDPs) continue to face low adoption among business users. As noted in Gartner’s 2025 Magic Quadrant for CDPs:

“Despite the strategic importance of CDPs, their use by business users remains low, with only 22% of marketers reporting high use. Instead, marketers often rely on other solutions… CDPs are serving as enabling technologies rather than primary tools for marketing operations.”

Further insights come from Gartner’s 2025 Critical Capabilities for Customer Data Platforms report, which highlights a broader decline in martech utilization, falling from 58% in 2020 to just 33% in 2023. CDPs are not exempt, with marketers utilizing only 53% of their available capabilities on average in 2024.

Beyond underused features, up to 68% of incoming data often remains unused in practice. This frequently occurs because CDPs replicate only a limited set of data fields instead of unlocking the full dataset. Consequently, valuable information is left untapped, not due to its absence, but because the system fails to make it accessible or actionable.

CDP adoption continues to face significant challenges, while AI, composable architectures, and zero-copy data sharing emerge as crucial components of the modern martech landscape. Given these shifts, it is logical to decouple CDP capabilities: assigning data management to IT and data teams, and embedding activation features into the marketing tools professionals already use daily.

An Alternative: The Dual-Core Martech Strategy Approach

The industry continues to debate whether composable or traditional stacks are superior. In reality, most organizations require both. Different teams operate at varying paces and have distinct appetites for risk. To support both and to effectively combine data management with activation capabilities, mid-market and smaller organizations can benefit from observing and learning from the challenges enterprise players have already faced and are now addressing.

Instead of relying on an all-in-one CDP to drive data maturity, consider enabling two dedicated core capabilities within your martech architecture, strategically placing each where it fits best:

1. A Data-Readiness Core

Typically located within your cloud data warehouse (though it can be set up elsewhere, or temporarily so), this acts as a dedicated data layer focused on marketing- and customer-specific data. This layer is responsible for quality checks, metadata enrichment, and privacy safeguards. Much of this processing can occur in batches to optimize efficiency and minimize risk.

Crucially, this layer is distinct from your operational data or core customer view. Examples include data from unknown visitors or promotional campaigns, the kind of data you wouldn’t want to inadvertently “pollute” your CRM or operational data warehouse. These datasets are often managed by different teams with distinct expertise, and marketing data is typically less structured and precise than operational data.

For data engineers, data often needs to be 100% accurate. Marketing data, however, rarely meets that stringent standard, and that’s acceptable. It operates under a different dynamic, where speed, experimentation, and directional insight often supersede absolute precision.

2. A Data-Activation Core

In most cases, this core is managed by your marketing automation platform or similar tools. It forms the real-time layer responsible for activating processed data, including personalization engines, campaign orchestration, real-time triggers, and customer journeys. This is where data transforms into action.

Real-time capabilities are implemented where necessary, but deliberately not universally. Real-time operations incur costs in infrastructure, complexity, and risk. Not every use case demands millisecond precision; in many instances, near-time or batch activation is sufficient and significantly more sustainable.

This layer heavily depends on the quality and structure of data flowing from your readiness core. Without that robust foundation, even the most advanced activation tools will struggle to deliver relevant and timely experiences.

A new generation of tools is emerging that no longer relies on proprietary internal data foundations. Instead, they connect directly via zero-copy integrations, eliminating redundant data deduplication. This makes these systems not only more flexible but also significantly more efficient. It is no coincidence that Bloomreach is strategically partnering with Google BigQuery and Salesforce with Snowflake, encouraging customers to transition from proprietary data storage to more open, scalable architectures.

While established marketing platforms are adjusting their vision, they still face the challenge of adapting legacy systems to this new approach, and their ultimate success remains to be seen. Meanwhile, other software, often originating outside the traditional martech space, have embraced this vision from the outset.

Consider SAS Customer Intelligence 360, which adopted a zero-copy architecture from its inception. This was a natural evolution given its deep roots in data and analytics and its design to operate with data stored in external environments. This foundation positions the platform several steps ahead in the ongoing evolution of marketing technology.

Such software is becoming increasingly attractive as marketing converges with data and IT. They offer a strong foundation that empowers marketing teams for more effective activation while meeting the technical standards that data and IT teams demand and trust. For nearly 50 years, SAS has developed intelligent systems that solve business challenges with analytics and AI. With extensive experience in handling sensitive data across industries, the company is well-positioned for this shift. As marketing becomes more data- and AI-driven, organizations like SAS, grounded in analytics rather than traditional marketing or advertising, may prove to be the more logical and future-ready choice, especially compared to platforms that originated in marketing and often collected data in less structured or compliant ways.

These two cores don’t necessitate a specific tool. They represent functional layers requiring certain capabilities, which can be delivered through a single platform, a suite of smaller tools working together (e.g., via zero-copy integration), or even partially embedded in existing systems or temporary processes.

As more organizations transition to using first-party data—collected directly and with consent—the dual-core approach becomes even more pertinent. It allows teams to maintain stringent data handling while retaining flexibility in activation. Many companies now prioritize collecting first-party data through their CRM, loyalty programs, and websites. Instead of relying on a CDP to ingest all data, they route these first-party data streams to a central data warehouse, which offers greater control, easier scalability, and a clear separation between data management and activation.

The tooling itself is less critical than possessing the right competencies—specifically, data competencies. While marketing teams often delegate this to IT, perceiving it as slow, cumbersome, and complex, it’s increasingly evident that bypassing the proper data governance route can lead to even greater challenges down the line.

Integrate only the CDP features you genuinely need and allow each system to play to its strengths. A CDP functions as an activation tool, not standalone data software. The overlap between CDPs and existing tools in your martech stack is growing. Therefore, invest in optimizing your current stack. For many organizations, this begins with addressing customer data management first, not by acquiring more tools, but by building robust foundations. Remember, there’s a distinct difference between data for marketing and data for operational processes; treating them identically often leads to confusion, inefficiency, and missed opportunities.

AI as the Next Piece of the Stack (and a Confirmation of the Dual-Core Approach)

When reviewing your martech stack today, the rise of AI is undeniable. It’s essential to strategically integrate AI into your architecture. Many tools within the data-activation core have heavily invested in AI, but is that always the optimal placement?

Many activation tools archive older data to maintain smooth performance and manage SaaS costs, especially as prices continue to climb. In 2024, an average SaaS spend increase of 9.3% year-over-year was observed, marking the first rise in three years, a trend experts expect to persist. Gartner forecasts global SaaS spending to reach $299 billion in 2025, up from $250.8 billion in 2024, a 19.2% increase. Consequently, storing extensive historical data within activation tools is not ideal given rising costs and associated risks.

AI performs best when supported by two solid foundations: first, a well-structured data layer with historical context, typically processed in batches to control costs; and second, a flexible activation setup that can react swiftly when needed. One provides depth, the other brings speed—both are indispensable.

A few CDPs promise the best of both worlds: deep AI analysis and rapid real-time triggers. However, achieving this effectively on a single platform is challenging in practice. Modern data-CDPs are moving in this direction by adopting a dual-core structure. For instance, Tealium offers two storage formats—structured and semi-structured—allowing data to be managed in ways that best suit specific needs.

Feed your AI models with clean, well-governed data from the readiness layer, and let the activation layer manage real-time triggers, recommendations, and personalization. As highlighted in this Forbes Expert Panel article, ethical and effective AI integration depends on strong data foundations. Neglect either layer or implement them poorly, and you risk an underdeveloped, low-impact outcome—a messy, inconsistent, and difficult-to-leverage data environment.

There’s no need to get bogged down in the nuanced differences between CDPs, DMPs, or marketing automation platforms. Instead, focus on bridging the capability gaps in your existing tools and construct a dual-core, modular stack that prevents incomplete results.

A useful framework for this approach is the model below, which outlines the essential building blocks of a martech stack. While comprehensive, it clearly illustrates the components you might need, regardless of the specific tools they reside within.

Source: Yearly Marketing Tech Lab research

Enterprises have already embraced this split between data-readiness and data-activation layers, powered by zero-copy integration and true zero- and first-party data for real-time orchestration and AI-driven insights. Mid-market organizations, however, often still contend with hidden data challenges. Relying on a standalone CDP risks merely masking the problem rather than solving it.

Future-proof businesses, irrespective of size, build their own data foundations independent of cookie-based tracking, leveraging zero and first-party data. This empowers them to grow with confidence and, as agile challengers, remain competitive even against larger market players.

You don’t need to rebuild everything from scratch. By addressing data at the right points, rather than within each individual marketing tool, you can significantly optimize your existing stack, especially with the right people involved. Modern stacks are no longer built around a single dominant platform. They have evolved into modular and adaptable architectures, tailored to contemporary business operations, not to how software was traditionally packaged and sold.

Key Takeaways

• CDPs rarely provide a complete 360° customer view and often create more confusion than clarity.
• Separate data readiness (in a cloud data warehouse with governance, consent management, and quality controls) from data activation (in your marketing automation or personalization tools).
• Use a modular, warehouse-first architecture to avoid vendor lock-in, hidden costs, and unnecessary complexity.
• Marketing data differs from operational data. It can be stored as a separate layer within your data warehouse, but in many cases, it’s also valuable to maintain a dedicated marketing database built on warehouse technology.
• AI only works well when it uses clean, well-managed historical data and is supported by a simple, real-time activation layer.
• Prioritize people and processes over new tools. Focus on having the right data in the right places, with the right expertise to generate real insights and results.

Is your CDP truly providing you with a full 360° view? Or, to be candid, is it more akin to a bubbling data stew that leaves you uncertain of its actual ingredients?