In recent years, cloud-based data warehouses (DWHs) have become increasingly popular as a solution for storing and managing large sets of data. DWHs offer several benefits including simpler deployment, greater scalability, and better performance for a growing set of data-driven use cases.
Using Data Warehousing hubs lets companies avoid the hassle of managing on-premises infrastructure, which can be slow, expensive and time-consuming to maintain. Cloud-based solutions can also offer greater elasticity, scaling up or down based on demand. Additionally, they often have superior security measures in place that can be highly customizable.
Increased prevalence of data warehouses (DWHs) in enterprise tech stacks
The use of data warehouses (DWHs) as a solution for enterprise tech stacks has been on the rise. By integrating with other components of an enterprise’s tech stack, such as marketing automation platforms and customer relationship management (CRM) systems, DWHs can help create a centralized source of data to power decision-making across the organization.
Pros and Cons of Using a Data Warehouse (DWH) as a Customer Data Platform (CDP)
While some companies may choose to use their data warehouse (DWH) as a customer data platform (CDP), there are arguments both for and against it.
On one hand, using a Data Warehouse (DWH) as your Customer Data Platform (CDP) can lead to better data hygiene and governance. By having all of your customer data in one central store, you can ensure there is a single source of truth and reduce the potential for errors.
On the other hand, a DWH may not have been built with customer data in mind. As a result, it may not have some of the necessary features and functionalities that a true CDP would provide. This can lead to limitations and complications when it comes to implementing customer-centered marketing campaigns.
Limitations of data warehousing (DWH) teams in supporting customer-centered use cases
Sometimes, an enterprise DWH team may not have the time or resources to support customer-centered use cases. Data analysts and engineers in these teams may be focused on other priorities, such as financial or operational data.
As a result, they may not be as knowledgeable or invested in the needs of marketers who are looking to implement campaigns using customer data.
Lack of self-service for marketers in data warehouses compared to customer data platforms (CDPs)
One of the crucial selling points of CDPs is that they often support marketer self-service. This means marketers can access the customer data they need without the intervention of a data analyst or engineer.
However, Data Warehousing Hubs (DWHs) typically don’t support this feature, which can make the integration process more involved and time-consuming, requiring a significant amount of technical expertise.
Need for Additional Capabilities When Using a Data Warehouse as Part of a Customer Data Stack
When you’re using a data warehouse as part of a customer data stack, you need to consider whether it has the features and functionality you require to meet your needs. For example, does it offer real-time data processing or machine learning capabilities?
A data warehouse alone may not provide these capabilities, so you will need to source them elsewhere. This can make things more complicated and time-consuming overall.
The role of a Data Warehouse (DWH) in a Customer Data Stack and its limitations
While we acknowledge these caveats, a DWH can still play a role as part of a customer data stack. By serving as a central data repository, it can provide marketers and data analysts with a means to access the data necessary for decision-making and reporting.
However, using a DWH as your CDP means recognizing limitations in functionality and investing in additional resources to implement customer-centered marketing campaigns.
Reverse ETL platforms for data transfer from data warehouses to marketing platforms
Reverse ETL platforms can be used to pull data from your data warehouse (DWH) and send it to marketing platforms after undergoing transformations. These platforms can help mitigate some of the limitations of using a DWH as your customer data platform (CDP) or as part of your customer data stack.
However, the use of reverse ETL platforms may be limited depending on the capabilities of your data warehouse and marketing platforms.
Using an Enterprise Data Warehouse (EDW) as a Customer Data Infrastructure Layer for Customer Data Platforms (CDP)
One approach to using a data warehouse (DWH) as part of a customer data stack is to use it as a customer data infrastructure layer that supplies data to your customer data platform (CDP), among other endpoints.
However, this approach still requires significant engineering and data modeling work to ensure that the data is correctly formatted and integrated with your CDP.
Trade-offs to consider when evaluating the use of a data warehouse (DWH) as a customer data platform (CDP) or as part of a customer data stack.
Ultimately, each pattern of using a DWH as a CDP or part of a customer data stack has its trade-offs to keep in mind while evaluating your options. The key is to be clear about your organization’s needs and how various solutions can help you meet them effectively.
In conclusion, there isn’t an easy answer to whether or not you should use your DWH as your CDP or as a part of your customer data stack. It requires careful consideration of the pros and cons, as well as some investment in additional resources to make sure your solution can meet your needs.