The Complete Customer Data Stack | RudderStack

By neub9
3 Min Read

In this article, we explore the optimal architecture for a comprehensive customer data stack from the viewpoint of a data engineer. In a business environment flooded with new customer data software tools and unclear definitions of terms such as “customer data platform,” we argue that these individual tools are just one part of a more comprehensive customer data stack and should be managed by IT and engineering teams.

While terms like “unified customer data” and “360º view of the customer” are trendy marketing buzzwords in the business software world, most developers and IT professionals think of unified customer data in the context of data flows, storage, and processing. This disconnect is a result of vendors selling to marketing and sales departments and implementing them without considering the entire system, leading to siloed data instead of the promised integrated data.

We believe that the customer data functionality sold by all vendors is just a small part of a broader system for customer data infrastructure. This infrastructure includes a comprehensive set of tools and functions that collect, manage, and activate customer data across multiple business and technical systems. These systems are complex and serve various use cases across the organization.

We argue that IT and engineering teams are best equipped to build this customer data stack and acknowledge that collecting customer data is critical. This data comes in various forms, including user behavior, transactional events, and structured data from both internal and external systems, as well as data warehouses and AI/ML systems.

Once collected, the data frequently needs to be validated to ensure accuracy and quality, which is essential to ensuring successful downstream functionality. Transformation, enrichment, and identity resolution are also crucial components of the customer data stack, involving tasks such as enriching user records, removing sensitive data, and mapping identifiers to create a comprehensive customer view.

Finally, we recognize that while there isn’t a single vendor that solves every piece of the puzzle, many companies use multiple tools to manage different components of their data pipeline. We conclude that a comprehensive and scalable customer data stack requires careful consideration and integration of all these key components.

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