Data vault 2.0: The dawn of a new era in data warehousing

It’s no surprise that more than 85% of businesses want to turn into data-driven organizations. However, very few (<37%) actually manage to succeed in the end. The main reason is that even modest-sized businesses often struggle to store, understand and leverage the internal and external data they are dealing with.

Organizations have traditionally adopted the Kimball or Inmon approach to build data warehouses for their data and insights needs. Unfortunately, the data load processes associated with these approaches are extremely labor intensive and costly. Furthermore, as they bring in new data, the associated business rules must be changed to coexist with the existing data. This means they are doing a lot of re-engineering work for integration, which is both time consuming and prone to errors. In the end, the result is a brittle data warehouse.

Why a better approach is needed

Organizations need to adopt more innovative approaches to achieve resilience, scalability and business agility in their data platform. Data vault 2.0 is a modern, agile way of designing and building efficient, effective data warehouses. Its hybrid (combining third normal and star schema) modeling technique and standard architecture pattern are designed for agile ways of working.

Data vault enables auditors to trace values in the data vault back to the source, thanks to features like raw vault and the separation of business rules. With standard architecture patterns, it is possible to create and load data vault 2.0, making the entire process less labor intensive and faster than ever.

If you are looking for a more modern data warehouse approach or want to learn more about data vault 2.0, please reach out to us!

Topics

• Tech Insights
• Data and Artificial Intelligence