5 Mistakes to Avoid when Starting with Data Vault Modeling

Starting with Data Vault data modeling can be a daunting task, and it’s easy to fall into several common pitfalls. Here, we’ll explore five of the most frequent mistakes that can lead to problems in Data Vault projects and provide insights into how to avoid them.

Identifying just five mistakes when starting with Data Vault was difficult because there are so many avenues you can go down that can result in problems later. However, we have picked five that we commonly see with the clients we work with:

The Data Vault Color Palette

One of the strengths of Data Vault is that there are a limited set of table types to work with. Think of it like an artist’s palette. You’ve got a limited set of colors, and it’s up to you as the data modeler or data architect to work with those colors and build a picture of the business that you’re working with.

You have Hubs, Links, and Satellites – the three core components – but there’s a few others to factor into the model. Depending on the “artist” (or the data modeler at work), you might have an abstract model produced.

This data modeler might work at the high end of abstraction (parties, locators, events, etc.) modeled in your diagram.

The next data modeler to come along might be a bit more physical in modeling. They may use objects that you can relate to in the real world that you’re looking at in the business.

It’s interesting because you’ve effectively got the same building blocks but different results. Which is the best?

Just like art, you can’t really compare these together. They are both representations, it really is down to taste. So, the answer is – it depends.

Both models may be mathematically equivalent to each other, but there are other factors involved in there which may make you tend towards one or the other depending on the circumstances of the model that you’re working with.

Data modeling is an art, not a science. If you’ve got two different data modelers at work, you’re going to get two different data models out at the far end that’s either good or bad depending on your perspective.

Data vault modeling requires you to apply judgment and common sense.

When you look to for a decision on the best way to model a situation, you’ll often hear the words “it depends”. Some groups evaluate a model where one’s advocating for more abstract, and the other is advocating for a more physical version of the model. The conversation goes round in circles to decide which is the best picking way forward.

On the face of it, when you first learn Data Vault data modeling it looks easy, with simple building blocks and simple rules to work with. But that’s what you see above the water level. When you go below the water level you’re dealing with data.

Data Vault modeling rewards experience. You can’t replace experience to get a good data model out at the far end but beware of simply creating a source system-based model because this frequently creates problems later.

1. Getting Started Without a Plan

One of the biggest mistakes is diving into Data Vault modeling without a proper plan. You might be eager to begin modeling, especially when you have multiple source systems and tables to integrate. However, starting without a structured approach can lead to chaos. It’s tempting to just start from the beginning of your table list and work through it, but this mechanical approach can be detrimental.

Data modeling is not a purely mechanical process.

It’s crucial to lift the lid on the screens in front of users and understand the data models behind those systems. These source systems often contain remnants of terminology and business practices from the time of their original creation. Over time, as different developers work on the system, it becomes cluttered with redundant code, outdated comments, and non-existent documentation.

Therefore, instead of directly importing these messy and inefficient table structures into your Data Vault, you should create a clean, top-down business model (concept model). Identify business processes, events, and units of work to build an ideal view of your analytics. Then, resolve the differences between this ideal model and the actual data from your source systems. A balanced approach that combines top-down and bottom-up modeling will lead to a cleaner and more functional Data Vault.

2. Modeling Extremes

Another common mistake is modeling extremes. For instance, if you have a sales data set that includes roll-up patterns for calendars and geographies, you might end up with an overly complex model if you follow Data Vault rules too mechanically. This can result in an explosion of hubs and links, leading to a convoluted model that’s difficult to query and manage.

Instead of creating separate hubs and links for every component, you should look for opportunities to create assemblies or equivalences. For example, a calendar can be represented by a hub for days, with satellites for weeks, months, quarters, and years. Similarly, geographical data can be consolidated into fewer entities. In some cases, reference tables or hubs and satellites for largely static values might be more appropriate.

Choosing the right level of abstraction and avoiding overcomplication will result in a more streamlined and efficient Data Vault model. Remember, the goal is to reduce the volume of entities and focus on the essence of what you’re trying to model.

3. Ignoring Units of Work

Ignoring units of work and granularity can also lead to problems. Data Vault modelers, especially those with a background in enterprise data architecture, might be tempted to normalize and abstract excessively. While normalization and abstraction have their place, they should inform your modeling rather than dictate it.

For example, in an insurance business, creating a policy from a quote involves multiple related concepts. Instead of creating a complex web of two-way mappings for every concept, consider modeling the business event as a unit of work. This approach simplifies querying and improves performance by focusing on the key events and the hubs involved.

By modeling units of work and understanding the granularity of your data, you can create a more efficient and functional Data Vault that supports your reporting needs without unnecessary complexity.

4. Breaking the Standards

Some clients, especially those new to Data Vault, might be tempted to “improve” the standards. They might skip formal training and start modifying the building blocks, leading to project failures. Standards are in place for a reason—they ensure integration, auditability, incremental build, refactoring support, automation, and more.

If you deviate from the standards, you might miss out on these benefits and end up with a flawed model. It’s crucial to follow the standards, at least initially, to understand their purpose and gain the benefits they offer. For example, adhering to the standards can help you recover from mistakes, such as loading incorrect data, thanks to the built-in auditability and timestamps.

By following the standards, you’ll create a robust and reliable Data Vault that can handle various scenarios and deliver consistent results.

5. Misidentifying Hubs and Links

Finally, misidentifying Hubs and Links is a common mistake. Distinguishing between when to use a Hub and when to use a Link can be challenging. For instance, in a finance system with journals and transactions, it might be tempting to connect transactions and transaction details directly. However, this can disrupt the loading patterns and parallel nature of Data Vault.

Instead, consider the grain of the data and model accordingly. A transaction might be better represented as a Hub if it’s referenced elsewhere, while transaction details can be linked appropriately without physical foreign key connections. Semantics and the English language can also cause confusion, as the same word might describe both a document and an event.

Careful analysis and understanding of the concepts involved will help you decide the best way to model your data, ensuring a clean and functional Data Vault.

Conclusion

Data Vault modeling is a unique approach that requires a balance of top-down and bottom-up modeling, adherence to standards, and careful consideration of units of work and granularity. By avoiding these common mistakes, you can create a robust and efficient Data Vault that supports your analytics needs, delivering consistent results and provides flexibility for the long term.

You may want to consider formal training in the Data Vault method or coaching and mentoring by experienced practitioners.

Remember, data modeling is as much an art as it is a science, and experience will help you navigate the complexities and create successful models