The Growing Influence of Customer Data Platforms in the Modern Data Ecosystem

Customer Data Platforms (CDPs) have now reached a significant tipping point, driven by the growing demand for valuable customer data throughout the data stack, alongside the emergence of significant data tooling innovations to meet this demand. Traditionally, CDPs were primarily designed for marketing activation use cases and were technologically limited in terms of integrating across the modern data stack. These systems were no longer able to deliver on their promises, such as providing a single source of truth or enabling real-time data activation.

However, we are now in a new era of customer data management ushered by the modern data stack. In this era, the data warehouse is at the center, and data teams build and manage the data layer on their own infrastructure. Valuable data from every touchpoint is made available across the stack, regardless of the tools used by downstream stakeholders, including marketing CDPs.

A Brief History: Defining the Customer Data Platform

In 2013, David Raab, the Founder of the CDP Institute, recognized the emerging confusion surrounding a new technology – tools that promised to provide a comprehensive customer 360 view. These tools involved building customer profiles by connecting data points from various sources and enabling predictive modeling on the resulting dataset. While they accelerated marketing with comprehensive customer information, there was significant variance in the features of each tool, resulting in uncertainty about their categorization. Against this backdrop of high excitement but low clarity, David published a blog post defining the emerging category and eventually launched the CDP Institute.

While the original intent of the traditional CDP was to create a function-agnostic software-as-a-service (SaaS) platform that added value by moving data across an organization’s digital ecosystem, these CDPs failed to unify data, creating a data silo. The complexity of the data stack amplified this problem. Traditional CDPs also fell short in sharing customer profiles with any system that needed it, as they were often closed systems that confined data value instead of making it available across the stack. This limitation led to an increased need for centralization and integration, prompting data teams to explore new architectures that could liberate data and offer integration flexibility for constantly changing toolsets.

Adhering to a new approach, companies are now building CDPs using a modern, warehouse-native architecture. This approach leverages the flexibility and accessibility of a data warehouse, offering more complete customer data sets and enabling more advanced use cases, while also enhancing data privacy, governance, and cost savings.

Built for Engineering vs. Built for Marketing

The traditional CDP was designed to simplify the process of gathering and using first-party data for non-technical marketing teams. However, it obscured the technical reality of data ingestion, movement, and unification, leading to a view where data teams were seen as necessary obstacles that non-technical teams had to cooperate with in order to access the data they needed.

In reality, data and engineering teams are best-suited to own the implementation and management of the CDP. This allows them to centralize all customer data in a modern data warehouse or data lake, integrate every tool in the organization’s data stack, and enable real-time use cases. Thus, at RudderStack, the focus is on building a CDP for data teams to empower them and provide every team with rich customer data.

The Future of Customer Data Management

With modern data warehouses and data lakes leading the way, the future of customer data management is witnessing a seismic shift. Over the next five years, significant innovations in the space are expected, and the modern data warehouse and data lake ecosystem will play a central role in this transformation.