Composable CDPs represent an innovative evolution in the realm of customer data platforms, moving away from traditional CDP models that rely heavily on direct integrations and browser-based tracking. This model focuses on centralizing the control and management of data within the data warehouse, enabling businesses to maintain robust data integrity and compliance with evolving privacy regulations. By leveraging the data warehouse as the core platform for all data activities, Composable CDPs ensure a resilient foundation for capturing and utilizing first-party data.
In a Composable CDP, the data warehouse—such as Snowflake—serves as the epicenter of data activities, from data ingestion to processing and output. This centralization facilitates optionality for governance and management of data, allowing companies to maintain a high level of control over their data processes and ensuring consistency across various data points. The data warehouse does not just store data but actively participates in data transformation and management, making it a dynamic tool in the data ecosystem.
To optimize the data flow from the warehouse to application layers, several specialized tools are employed:
Load data with Fivetran:
This tool automates the continuous import of data into the data warehouse, enabling real-time data availability with minimal latency. It is crucial for ensuring that the data warehouse has up-to-date information, which is essential for accurate analytics and decision-making.
Transform with dbt:
Dbt is used for performing transformations within the data warehouse, allowing data teams to model and restructure the data according to the needs of downstream applications. This tool is vital for preparing data for specific analyses or operational uses, ensuring that data is not only stored but made ready for action.
Utilize the Data Warehouse with Snowflake:
Snowflake acts as the central hub in the Composable CDP architecture, where all data aggregation, storage, and querying occur. Its powerful data handling and query optimization capabilities make it ideal for managing large volumes of data efficiently:
Hightouch as 'reverse-ETL':
Operating as a 'reverse-ETL' tool, Hightouch extracts transformed data from Snowflake and maps it to the schemas required by downstream APIs such as those of marketing automation or product analytics tools. This ensures that the data not only remains centralized but is also actionable and directly influences business operations and customer interactions.
A key advantage of the Composable CDP is the shift in responsibility for data management from application engineering teams to the data team. This shift allows companies to better leverage their resources, as data teams are typically more equipped to handle complex data integration and management tasks. By centralizing data responsibilities, businesses can improve quality control over their data processes, ensuring that data capture, storage, and utilization are consistent, accurate, and aligned with business objectives.
One of the compelling financial advantages of adopting a Composable CDP is the reduced upfront investment compared to traditional CDPs like Segment. Reverse-ETL tools used in Composable CDPs, such as Hightouch, typically offer a more cost-effective entry point. This affordability stems not just from the tool costs themselves but also from leveraging the existing data infrastructure, thereby avoiding the need for extensive additional investments.
Composable CDPs significantly mitigate data loss and enhance data fidelity, overcoming common limitations seen in traditional CDPs. By centralizing data operations within the data warehouse and employing advanced reverse-ETL tools, these platforms ensure high levels of data accuracy and integrity. Unlike traditional CDPs that often rely on browser-based tracking susceptible to cookie restrictions and content blockers, Composable CDPs leverage first-party data that remains unaffected by these external limitations. This approach not only preserves data integrity but also enhances privacy compliance.
A distinct advantage of Composable CDPs is their ability to enrich first-party data with information from third-party systems. This data enrichment is vital for businesses that depend on a diverse array of data sources, including external data streams from partner relationships, public data feeds, or commercially available data sets. By effectively merging these varied sources within a single platform, Composable CDPs offer a more unified and enriched view of customer interactions and behaviors. This capability ensures that the insights derived are comprehensive and robust, enabling businesses to make well-informed decisions based on a complete data picture.
With rapid technological changes and evolving market dynamics, businesses need to ensure that their data strategy is future-proof. Composable CDPs, by deriving event tracking and customer data directly from the warehouse, provide a stable and scalable foundation that can adapt to new tools and technologies as they emerge. This setup prevents the potential obsolescence of data platforms that rely heavily on browser-based or third-party data sources, which may become outdated or non-compliant over time.
Overlooking Detailed Event Taxonomy and Data Governance
A common and significant error in Composable CDP implementation is the failure to adequately plan the taxonomy of events and establish robust data governance from the outset. Without a meticulously defined event taxonomy that is aligned with the requirements of downstream tools, organizations risk capturing and processing data that does not add value. This can lead to the collection of irrelevant or redundant data, resulting in bloated databases and operational inefficiencies.
More critically, if the data collected does not accurately address the needs of downstream applications such as product analytics and lifecycle marketing tools, the entire purpose of the CDP is undermined. Misaligned data can lead to ineffective analytics, misguided marketing strategies, and ultimately, a failure to engage meaningfully with customers. Ensuring that each event is precisely defined and relevant to the needs of these tools is essential not only for maintaining data integrity but also for driving successful outcomes in analytics and customer engagement initiatives.
Neglecting Change Management and Development Best Practices
Another critical oversight is the inadequate incorporation of change management and staging environments in the data integration process. Pushing data modeling or business rules into the reverse-ETL tools without proper developmental oversight can result in poor change management, leading to inconsistent business rules and opaque coding practices. This absence of structured development practices can cause significant disruptions and complicate troubleshooting and updates.
Underestimating the Complexity of Data Streams
Many organizations do not fully account for the complexities involved in converting traditional relational data into event streams suitable for a Composable CDP. This oversight can lead to a lack of transparency and auditability in the data, as well as issues like duplicate events or ever-changing event streams, which can confuse and overwhelm data systems.
Inefficient Query Practices and Resource Utilization
Implementing Composable CDPs without attention to efficient query practices can lead to excessive computational demands and high operational costs. Poorly optimized queries not only slow down data processing but also consume excessive resources, leading to increased costs, particularly in pay-per-use data environments like Snowflake.
Permanent Impact of Data Errors
A critical aspect often underestimated is the irreversible nature of data errors in a Composable CDP setup. Once erroneous data is sent to downstream systems, correcting it can be challenging and resource-intensive. It is crucial to get the data right the first time to avoid costly corrections and potential disruptions in downstream analytics and business processes.
Mammoth Growth combines industry-leading expertise in both customer data platforms and data warehousing, uniquely positioning us to develop robust Composable CDPs. Our Roadmap workstream plays a crucial role by defining clear business requirements and mapping out the most efficient paths to success. This strategic planning ensures that every aspect of the CDP implementation is aligned with the client's business objectives and prepared for seamless integration.
At the core of our approach is a strong emphasis on comprehensive data governance, initiated early in every project. Our Roadmap workstream meticulously plans the event taxonomy to ensure that data capture aligns precisely with the needs of downstream tools, thus avoiding over-capture and data misalignment. This preemptive planning is critical to providing reliable foundations for product analytics and lifecycle marketing, ensuring that our clients’ efforts are effective and data-driven.
Our DTaaS workstream brings operational excellence to executing the strategies laid out by the Roadmap workstream. We employ rigorous change management practices and advanced development protocols, including automated staging and review processes. These practices not only uphold high standards of quality and consistency but also ensure that the CDP implementation is adaptable and scalable, meeting the evolving needs of our clients.
Mammoth Growth’s expertise in query optimization significantly reduces computational demands and operational costs, a critical aspect managed by our DTaaS workstream. By optimizing data interactions and processing within environments like Snowflake, we manage resource utilization meticulously, ensuring cost-effective operations without compromising data performance.
We employ automated staging and review mechanisms to ensure data integrity before it impacts downstream systems. This proactive approach allows us to catch and correct data issues early, preventing them from reaching production and potentially disrupting business operations and analytics. This focus on preemptive data validation is a hallmark of how our DTaaS workstream safeguards the interests of our clients, reinforcing the reliability of their data ecosystems.
