Data architecture principles

Everyone knows what we are talking about when we talk about the architecture of a building. It is different with a data platform. For some, data architecture is a picture, for others it is a whole book. Good data architecture can make or break a project. What does this architecture look like? How do you set it up?

Let’s return to the analogy of the architecture of the building. Suppose you want to build a large building. You will probably build this with many different people. Perhaps even with workers from different subcontractors or groups that build different parts separately from each other. So how do you ensure that all these people have the same building in mind? Do the different parts fit together? Exactly, that’s what architecture is for. It forms the framework for everyone building the solution and defines the building blocks, what materials they are made of and how they are connected.

The definition of a (data architecture) principle:

Architectural principles are guiding formulations that indicate what is desirable for an organisation. An architectural principle can be seen as a policy statement that relates exclusively to the structure of the company, its processes and the provision of information.

Principles often have the same structure:

Statement, a short, powerful formulation of the principle. Rationale, a description of why this principle has been drawn up. Implications, a description of the consequences of this principle for the project. The statement and rationale are generally applicable, while the implications are specific to the project. Drafting these principles can be a difficult task. How do you formulate powerful statements and ensure that the principles cover the entire policy? An additional challenge is how to draft principles that are independent of transformations within the policy.

Starting with principles is usually a good step towards a supported data-driven vision within an organisation. Data principles allow the vision to be further developed and, on the other hand, ensure that everyone within the company can understand this vision. A good start on formulating data principles can often be made within a number of sessions. Internally, there is often less experience in formulating data principles.

Towards a supported data-driven vision with data principles

When working with data principles, it becomes easier to talk about data architecture. Make sure it does not become a fundamental discussion about open source versus closed source. Nowadays, it is precisely a hybrid architecture that helps companies move towards a more data-driven approach. It must be an integral architecture that can support both structured queries and dashboards and also offers the possibility to

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