5 common mistakes to avoid when you start data modelling with Data Vault

Data Vault 2.0 is a modern approach to data warehousing that supplies an efficient, secure, and flexible way to store and manage substantial amounts of data. When learning a new Data Vault modelling technique, it is part art, part science. As with any new technique, you learn through experience. But to get you going, here are 5 common mistakes to avoid when you start data modelling with Data Vault.

1 – Over-complicating the model

One of the most common mistakes people make when modelling their Data Vault is over-complicating the model. It can be easy to get lost in the complexity of the model, but it can make it difficult to maintain, understand, and modify. It’s essential to keep the model as simple as possible, and to only add complexity where it’s necessary.

2 – Not defining business keys

Business keys are unique identifiers that define a business objective. If you don’t define business keys correctly, it can lead to duplicate data. This can lead to inaccurate reporting and analysis. It’s crucial to identify the business keys for each entity, so you can avoid the issue.

3 – Not defining relationships correctly

Another common mistake is not defining relationships correctly. Relationships between entities are critical in a Data Vault model. If they’re not defined correctly, you may end up with inaccurate data, or your model may not perform as well as expected. You must define relationships between entities based on their business requirements.

4 – Over-reliance on attributes

Attributes are essential in Data Vault modelling. But it’s easy to over-rely on them. If you focus too much on attributes, you may end up with too many – making the model too complicated. It’s essential to keep the attributes as simple as possible and only include what is necessary.

5 – Not testing the model

One of the most significant mistakes you can make when building a Data Vault model is not testing it. Testing the model ensures that it’s working as expected and that there are no issues. It’s essential to test the model thoroughly to ensure that it’s performing as expected.

Summary

In summary, Data Vault modelling requires a willingness to understand the business requirements and the organisation’s data sources while being careful to avoid creating a source-centric data model. By avoiding these 5 common mistakes businesses make when implementing Data Vault, you can create a robust, scalable, and flexible Data Vault model that meets your business needs. It’s essential to keep the model as simple as possible, business-driven, and thoroughly tested.

How we can help

You don’t have to implement Data Vault on your own. We offer a unique range of services from consultancy to training. Apply for your free place in our fortnightly Data Vault Core Concepts training here, or get in touch with us about support here.