Martech Secret Sauce
Marketing Data Layer — A Guide for Marketing Teams
As marketing teams' maturity in their data sophistication increasingly gaps are emerging between analytical data, digital engagement, and execution platforms – often leaving an operational data headache as Marketing teams grapple with how they activate on increasingly mixed environments of engagement systems, customer data and insights.
A well-designed Marketing Data Layer (MDL) is the key to solving marketers' ongoing data fragmentation, integration, and activation challenges. By strategically building the MDL within a scalable core data platform, marketing teams can unlock the full potential of their Martech stack and deliver more personalised, data-driven experiences.
Three Core Benefits of a Marketing Data Layer (MDL)
Solves Fragmentation and Integration Issues
Empowers Data-Driven Personalization and Campaigns
Enhances Flexibility and Data Quality
By focusing on these core areas, marketers can overcome the limitations of traditional Martech tools and fully leverage the available data assets in a way that better aligns to how marketing teams operate.
Solving Fragmentation and Integration Issues
The core challenge marketers face today isn't generating or storing data but organising and integrating it in a way that meets their needs. Data is often fragmented across multiple systems, making it difficult to deliver personalised, data-driven campaigns.
A Marketing Data Layer (MDL) provides a centralised, structured view of customer data, integrating information from various sources—customer profiles, engagement, product interactions, consent, and analytics—into a cohesive and actionable asset. By centralising data, the MDL eliminates the silos that have historically prevented marketing teams from fully utilising their data.
Part of the benefit of intentionally defining an MDL fit for marketing purposes is that it enables technology, digital and data teams to align delivering the right data points marketing needs.
2. Empowering Data-Driven Personalisation and Campaigns
The MDL allows marketers to unlock the full potential of their customer data by defining data in a way that makes sense to their use cases.
Marketing automation platforms, Customer Data Platforms (CDPs), and personalisation tools are often incomplete solutions, as they don't offer the comprehensive data capabilities that a well-structured MDL can. By building an MDL, marketing teams can:
Personalise customer experiences with data from multiple sources by consolidating exactly what marketing or customer teams need
Segment audiences based on marketing defined profiles aligned to marketing strategies
Deliver targeted campaigns that are more effective and aligned with customer needs.
Provide cross channel consistency through a standardised marketing view available across channels
A well-defined MDL works in tandem with activation platforms (such as a CDP) to standardise the ‘Marketing’ view of a customer and providing processing capability other systems can’t.
3. Enhancing Flexibility and Data Quality
Building the MDL within a core data platform—such as Snowflake or Google Cloud Platform (GCP)—offers flexibility and improves data quality. Unlike marketing automation platforms or CDPs, these platforms provide the scalability and data-processing power required for real-time data integration, transformation or enrichment that engagement platforms like CDP can’t deliver on.
Advantages of this approach include:
Closer access to the data marketing teams needs for campaign execution and analysis.
The ability to collaborate with analytics and other departments for broader use cases.
Maintaining flexibility in the Martech stack, allowing platforms to come and go while keeping the MDL stable.
Centralised data management to improve data quality and consistency across all marketing initiatives.
By treating the MDL as a core business asset, like finance or retail data, marketing teams can ensure they are prepared to adapt as the Martech landscape evolves.
Strategically Building Your MDL
To achieve these benefits, marketing teams need to take a strategic approach to defining and building their MDL. It's often best to create the MDL within a core data platform, which allows for scalability, real-time processing, and seamless collaboration across departments.
Key steps to building your MDL include:
Identifying the core data assets that marketing teams need for personalisation and segmentation.
Centralising data management within a scalable platform like Snowflake or GCP or ensuring existing platforms that can play the role and meet requirements.
Ensuring collaboration with analytics, IT, and compliance teams to maintain data quality and security.
Creating a flexible structure that allows Martech tools to be easily swapped or updated without disrupting the MDL.
By focusing on these steps, Marketing teams can reduce the complexity of managing data across multiple tools and platforms, making it easier to activate customer insights and improve marketing performance.
The Marketing Data Layer as a Competitive Advantage
The Marketing Data Layer is a secret weapon for marketing teams struggling with data centralisation, cataloguing in way that aligns to marketing activation, availability, quality, and use. By designing and managing a fit-for-purpose MDL, marketers can unlock the full potential of their Martech stack, driving more personalised, data-driven experiences and gaining a competitive edge in an increasingly data-driven world.
Rather than relying on piecemeal solutions, the MDL provides a long-term foundation for success by simplifying data integration and empowering marketing teams to focus on what matters most: delivering exceptional customer experiences.
Footnote
Astute observers may ask how this differs from a CDP or Marketing Automation platform? In smaller less complex businesses, it may not – those platforms may play the role perfectly fine – though intentional design and development of the data used by marketing is still beneficial.
Increasingly, as marketing teams become more sophisticated – enabling complex business rules that underpin customer segments and data points can stretch the core capabilities of customer engagement tools (often these needs are fulfilled by analytics teams). Businesses without the right architecture can fail to enable various data points needed to drive high degrees of personalisation, consistently across channels.
Coupled with a need to align data needs to marketing vernacular – a dedicated layer is becoming increasingly important to bridge the limitations in complex environments.