In this chapter, we introduced the idea of data architecture and explained how to group responsibilities into capabilities that help manage data throughout its lifecycle. We explained that all data handling requires a level of due diligence, whether this is enforced by corporate rules or otherwise, and without this, analytics and their results can quickly become invalid.

Having scoped our data architecture, we have walked through the individual components and their respective advantages/disadvantages, explaining that our choices are based upon collective experience. Indeed, there are always options when it comes to choosing components and their individual features should always be carefully considered before any commitment.

In the next chapter, we will dive deeper into how to source and capture data. We will advise on how to bring data onto the platform and discuss aspects related to processing and handling data through a pipeline.