A Composable CDP differs from a traditional CDP in its architecture, although it addresses the same issues. The architecture of a Composable CDP is similar to that of a modern data stack. Modern data stacks are hosted in the cloud, which removes the burden of hardware maintenance.
The functionality of data stacks enables Composable CDPs to perform as a centralized analytics platform, allowing users to access all customer data fetched from the cloud data warehouse.
Users can activate data directly from the cloud data warehouse. By adding a composable approach, a Composable CDP provides an extensible solution to the existing CDP architecture. According to this approach, the architecture is divided into sub-components, enabling users to select the best individual components to analyze use cases. This modular behavior of Composable CDPs creates best-in-class products at each component level.
To understand how a composable CDP operates, we can break down the entire CDP architecture into three sectors.
An organization may have multiple data storage locations, each containing a large volume of data. The data integration process collects data from all of these locations and merges them into one central repository. Data integration is done by transporting data from the storage locations to the destination.
Obtaining real-time or customer behavioral data was a major challenge in data engineering. Data engineers utilized ETL as a solution to this problem and obtained a more reliable and comprehensive behavioral data set. Snowplow is one of the best event-tracking vendors that collect and ingests customer behavioral data.
The data modeling process transforms data models into consumable data models to be used in analytics. One of the best tools for data transformation is "dbt," a transformation workflow that compiles and runs analytics as opposed to data. "dbt" builds reusable data models that can be used in every analysis, instead of using raw data.
Business intelligence is the process of analyzing and visualizing data models to make business decisions. It visualizes data in the form of reports and dashboards.
Data activation is the final layer in the composable CDP architecture. As previously mentioned, it utilizes the Reverse ETL process to retrieve data from warehouses and write the query results back to an automation tool. With this approach, a composable CDP can synchronize the retrieved data in the business tool to create unified user profiles.
Compared to the traditional CDP, the composable CDP boasts several features that make it a more powerful architecture.
Having a source of truth in the organization allows all users to access the same set of data, rather than creating new silos. With the data warehouse acting as a centralized platform, teams can gain better insights into customer profiles.
Unlike traditional CDPs that have limited access to customer data and lock users into a single vendor, composable CDPs offer access to all data within an organization. This creates a more complete view of customer profiles.
The use of cloud platforms in composable CDPs eliminates the need for additional storage and reduces costs. With data being ingested directly into business tools, there is no double storage of data, leading to cost savings.
Composable CDPs function as an external layer to an organization's data warehouse, whereas traditional CDPs operate as a distinct unit within the warehouse. As CDP vendors may not provide access to all customer data, organizations may be unaware of changes to the data and have no control over them. However, composable CDPs do not store data in the warehouse and therefore do not alter data models. Additionally, they provide full access to and control over data.
Composable CDPs utilize data activation tools such as Hightouch, which rely on existing data warehouses to carry out the process. This eliminates the need for creating new data and allows for immediate data activation within the same technology stack.
Composable CDPs not only address the typical use cases of traditional CDPs but are also capable of tackling more complex use cases with greater flexibility. The following are some of the use cases that have been addressed by composable CDPs.
Composable CDPs utilize the data activation layer to retrieve data from existing data models in the data warehouse and sync it with a business tool. This allows for the creation of audience segments using synced data and customer data in storage. These segments identify individual users, enabling personalized services.
Data warehouse table data is represented in terms of objects, and the process of syncing one-row data set/object from one table to another is known as object syncing. These data can be sent to any business application, such as Salesforce or Marketo, providing users with access to all data for use in any analytics with greater flexibility.
Event syncing involves syncing behavioral data to its destination to build personalized customer experiences, particularly when access to critical data such as orders and shopping carts is necessary.
By utilizing composable CDPs, organizations can access their real-time data since the CDP activates the data that is stored in its data warehouse. Thus, with a tool like Hightouch, business teams can analyze their use cases with real-time data and build real-time customer experiences.
The most appropriate method for converting a data warehouse into a composable CDP is by utilizing a data activation tool like Hightouch. It uses the data that already exists in the warehouse and performs data activation on the same data to build real-time data.
As the volume of both first-party and third-party data continues to grow, it becomes increasingly challenging to gather and develop a complete 360-degree view of customer profiles. This is where the Customer Data Platforms come into play, providing assistance with data strategies.
A CDP is a software designed to collect and store vast amounts of customer data related to their interactions with a service. It serves as a comprehensive customer database and supports the customization of customer experiences in near real-time. CDPs also offer valuable insights into customer data, strengthening operational use cases while delivering personalized experiences. CDPs have a lasting impact on marketing technology, and they can be identified as all-in-one platforms that combine all the necessary functionalities to create unified customer profiles.
Below are the complex problems that the CDP platform has provided solutions for.
Most CDPs can retrieve data from various SaaS tools and combine collected events to facilitate personalized experiences. As unified customer databases, CDPs utilize exposed APIs to ingest data into operational tools.
CDP links the user's interactions by unifying different customer data sets. It employs identity graphs to uniquely identify a customer profile.
Audience management is responsible for dividing customers into specific segments based on their interactions and interests with the service. CDPs accomplish this with a visual user interface that defines audience segments without the need for SQL.
CDPs activate data by integrating with third-party APIs and tech stack. The activated data is then used to deliver personalized experiences.
Although a traditional CDP purports to be the single source of truth for customer data, it may not contain all the data required for comprehensive analytics and may not be future-proof. Therefore, a data warehouse is not replaced by a CDP but is instead used in conjunction with it. The data from the CDP is copied to the warehouse. However, a data warehouse is not appropriate for use cases requiring near real-time processing.
The emergence of cloud data warehouses like Databricks, Snowflake, and BigQuery has expanded the capabilities of existing CDP solutions to support more complex use cases. Prior to the availability of modern data warehouses, only larger companies had access to such infrastructure, making it challenging for other organizations to establish reliable and flexible data analytics systems.
Snowflake is a SaaS-based data warehouse that supports critical workloads such as data warehousing, data lakes, data engineering, and business intelligence. It utilizes SQL to query data and can be deployed on any cloud data infrastructure. Snowflake's auto-scaling feature allows it to handle a large number of queries. It collects and consolidates data into a centralized database for analytics in CDP solutions. The pricing for Snowflake is based on individual warehouse usage. As several warehouse sizes are available, the price increases as the warehouse size increases.
Databricks is a cloud-based platform that is often considered more of a data lakehouse than a traditional data warehouse. The architecture of a data lakehouse emphasizes open data management, and as a result, Databricks can store vast amounts of unstructured data for use in analytics. In comparison to Snowflake, Databricks is more affordable. When it comes to pricing, you can either select pay as you go model or Committed-use discounts.
Snowflake and Databricks aim to unify siloed data into a centralized repository for use by various business teams, including marketing, sales, and customer support. However, access to this data is often restricted to data science team users who are proficient in SQL, which can be a hindrance for non-technical users. To address this issue, Hightouch was introduced with the process of Reverse ETL.
Reverse ETL is a process that provides business team members with access to customer data stored in a centralized data warehouse. This process copies data from the centralized data sources to the user's operational systems and SaaS tools, enabling personalized experiences.
In Reverse ETL, ETL pipelines (which fetch data from various sources and transport it to a data store) read data from cloud data warehouses and write it to an automation tool. This is one way in which Reverse ETL differs from Traditional ETL. Additionally, while traditional ETL is used in data integration to improve use case analytics, Reverse ETL is used in data activation. Once the data is written to the data storage, it is available to all users within the organization.
Hightouch is a platform that fetches data directly from data warehouses like Snowflake and Databricks and writes it to marketing tools. This includes model data, customer/audience segment data, and product usage data. Hightouch's functionality eliminates the need for data teams/engineers to create separate CSV files and send them to the marketing team.
Without this platform, data teams in an organization would need to create separate pipelines for each tool in the technology stack. Therefore, Hightouch enables a single source of truth within the organization while ensuring that marketing teams have access to updated data at any time.
composable CDPs are a modern approach to centralizing and activating customer data that address the limitations of traditional CDPs. By leveraging existing data warehouses and using data activation tools like Hightouch, organizations can access real-time data and gain greater flexibility in their data strategies.
The composable architecture allows users to select the best individual components for their use cases, resulting in best-in-class products at each level. With key features like real-time data access, identity resolution, and visual audience builders, composable CDPs enable organizations to build real-time customer experiences without requiring extensive SQL knowledge. As data continues to grow in importance, composable CDPs offer a powerful solution for organizations seeking to stay ahead of the curve in the competitive world of customer experience.