Lee Hammond explains why marketing’s future is composable, how to unify siloed data, and the frameworks CMOs need to drive real-time, privacy-first personalization.
As marketing technology enters a new era, one thing is clear: the future is composable. At the intersection of marketing, data strategy, and enterprise architecture, Lee Hammond has emerged as a leading voice, guiding organizations through the shift from monolithic Customer Data Platforms (CDPs) to flexible, warehouse-native ecosystems. With experience spanning leadership roles at Hightouch and Universal Music Group, and now spearheading Customer Data Strategy and Solutions at KLH, Hammond brings a unique vantage point to the evolving data landscape.
In this conversation, Hammond breaks down what’s driving the shift toward composable architectures, why CMOs and data leaders must act as strategic partners—not siloed functions—and how real-time identity resolution and activation are reshaping personalization in a privacy-first world. He also shares practical frameworks for MarTech audits, vendor evaluation, and building cross-functional collaboration that bridges marketing and engineering.
Whether you’re navigating a CDP overhaul, struggling with customer data silos, or exploring how AI will transform engagement, Hammond’s insights offer a roadmap for building smarter, more agile marketing systems grounded in data, trust, and adaptability.
Full interview;
How do you see the market evolving from monolithic to composable CDPs, and what are the key advantages and challenges?
The evolution from monolithic to composable CDP architectures represents a fundamental shift in customer data management. We’re witnessing a market transformation driven by traditional CDPs’ limitations in scaling with enterprise needs.
The warehouse-native approach is winning because it aligns with broader data democratization efforts. Organizations recognize that data warehouse investments can serve as the foundation for customer data platforms rather than creating another silo. AI is accelerating this trend, as the warehouse data provides the foundation AI requires for insights and marketing automation.
The primary advantages are cost efficiency through leveraging existing infrastructure, flexibility to adapt to changing business needs, and improved data governance with a single source of truth. Composable architectures allow teams to select best-of-breed solutions rather than being locked into a vendor’s ecosystem.
The main challenge is bridging skill and operational gaps between marketing and data engineering teams, especially in large enterprises where these divisions have historically worked autonomously with separate technology stacks.
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How can large enterprises most effectively unify siloed customer data using strategies and technologies you’ve seen succeed?
The most effective strategies begin with organizational alignment, not technology. Success requires establishing cross-functional data governance teams with clear ownership and accountability.
Technologically, implementing a universal ID resolution framework that combines deterministic and probabilistic matching creates a flexible identity spine. Adopting real-time data synchronization capabilities through Reverse ETL and webhook architectures ensures customer insights flow to activation channels without delay. A metadata-driven approach to data cataloging and quality management also establishes trust in the unified view.
What does ethical and impactful personalization look like amidst today’s privacy rules, fragmented journeys, and data overload?
While personalization has become more challenging, forcing mechanisms move all stakeholders toward a unified customer data architecture with workflows to support consumers, marketers, and privacy officers.
Privacy Management Platforms (i.e., OneTrust) are integrated with Cloud Data Warehouses as another data source in the Customer 360. This ensures marketers activate warehouse data, follow legal requirements, and respect consumer consent.
How is marketing’s evolving role impacting organizational data strategy, and are CMOs increasingly taking on CDO-like responsibilities?
The relationship between marketing and data has fundamentally transformed. What we’re seeing isn’t CMOs taking on CDO responsibilities, but rather marketing and data functions operating as strategic partners.
Today’s effective CMOs articulate clear data requirements based on customer experience needs rather than just requesting reports. This requires stronger technical literacy and data governance understanding, with increased involvement in data acquisition strategy, identity resolution, and operational data flows.
However, these roles won’t fully merge. While CMOs articulate the “what” and “why” of customer data needs, data leadership brings crucial expertise in architecture, governance, and technical implementation. Successful organizations establish collaborative frameworks rather than consolidating these functions.
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What are the top common MarTech stack gaps you see in audits, and how do you advise on prioritizing fixes?
Three gaps consistently emerge across organizations:
- First, disconnected customer identity resolution. Companies often have multiple, conflicting customer definitions across systems, creating fragmented experiences and inaccurate analytics.
- Second, activation latency. Organizations collect abundant data but struggle to make it actionable when it would provide the most impact. Not everything needs millisecond response times, but your architecture should support different speed requirements.
- Third, insufficient measurement infrastructure. Companies invest heavily in acquisition technologies but underinvest in attribution and incremental measurement capabilities.
For remediation, I recommend starting with quick-win activation use cases to build momentum, prioritizing identity resolution as the foundation, and implementing proper measurement infrastructure.
What are the key architectural considerations for building a system that turns customer insights into marketing action?
The key consideration is separating your system of record (comprehensive data) from systems of engagement (real-time decisions). The system of record must include feedback from engagement systems to create a continuous learning loop.
I recommend a three-tier architecture: First, a robust data foundation including warehouse and identity resolution; second, an analytics layer handling audience segmentation, campaign planning, and measurement; and third, an activation layer executing campaigns across channels through APIs and integrations.
What framework do you use to evaluate MarTech vendors, particularly in CDP and customer identity resolution?
My framework centers on capabilities alignment, technical compatibility, organizational fit, and total cost of ownership.
For CDP and identity vendors, I evaluate technical architecture (is it built for your scale and aligned with your data infrastructure?), data governance capabilities (consent management, data lineage, regulatory compliance), interoperability (open standards, comprehensive documentation, partner ecosystem), time to value (incremental implementation, business user accessibility), and performance at scale.
Traditional vendor comparisons often overemphasize feature checklists while undervaluing implementation complexity and organizational alignment. The best technology is worthless if your team can’t effectively implement it.
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How can you foster effective collaboration between marketing, data teams, and engineering when implementing composable MarTech strategies?
Breaking down traditional silos is essential. Establish shared metrics and outcomes rather than function-specific KPIs, so collaboration becomes necessary rather than optional. Create cross-functional “pods” organized around specific customer journeys, including marketers, analysts, and engineers working together throughout the process.
Invest in translation capabilities through dedicated roles or upskilling. Success requires individuals who speak both customer experience and data architecture languages. Communication should continue through work-in-progress showcases, shared documentation, and regular retrospectives.
Leadership must model collaborative behavior. When marketing, data, and technology executives demonstrate joint accountability, their teams follow suit.
Composable CDPs offer increased flexibility, but what are some less obvious benefits or potential drawbacks organizations encounter in this transition?
Beyond flexibility, organizations often discover unexpected benefits in talent development and retention. Technical teams find the work more engaging because it involves solving novel problems rather than administering vendor platforms.
Another benefit is improved vendor leverage. When you can replace individual components rather than entire platforms, vendors become more responsive and reasonable in negotiations. Enhanced cross-domain team cooperation and readiness for AI innovation are additional advantages.
On the downside, organizations underestimate the internal capability building required. Composable approaches shift integration burdens from vendors to your organization, requiring stronger technical product management capabilities. They can also lead to analysis paralysis when teams get stuck evaluating options rather than implementing solutions.
How does composable MarTech affect traditional MarTech suite vendors? Do you anticipate a fundamental restructuring of the market?
The composable MarTech evolution is already restructuring the market. The first phase—cloud data warehouses—has forced packaged CDP vendors to adapt. Salesforce rebranded its traditional CDP as “Data Cloud,” adding data sharing with enterprise cloud data warehouses like Snowflake and Databricks.
The second phase—AI agents—is beginning with greater implications. LLM-powered agents will work autonomously across applications using natural language prompts. This is fueled by standard protocols like MCP and A2A from Google, Microsoft, OpenAI, and Anthropic, enabling interoperability.
Forward-thinking vendors will transform their business models to focus on interoperability rather than end-to-end solutions. We’re moving toward a market where the “MarTech suite” concept becomes obsolete in favor of specialized, seamlessly integrated capabilities.
