Organizations that are data-driven and have a comprehensive view of their data can use it to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes. And, as we see business environments becoming increasingly volatile, these organizations need enterprise data architectures to be flexible and adaptable to the dynamically changing world around us.

One common challenge with dimensional and normalized data modeling techniques is that they were not designed to respond to fast changes. Data Vault modeling helps to address this challenge and equips organizations with superior flexibility for their analytics.

Data Vault modeling: addressing business needs

Data Vault is a data modeling approach that is detail-oriented – keeping track of data and its history. It gives organizations more agility and flexibility when their data volumes grow significantly, and their data environment becomes more complex and distributed. If they can address these challenges in their data model, they will be able to use their data to make more informed business decisions.

Dan Linstedt created the Data Vault approach in the 1990s, before releasing it to the public in 2000. In 2013, Data Vault 2.0 was released, introducing enhancements around Big Data, and NoSQL, as well as integrations for unstructured and semi-structured data.

With the Data Vault approach, Linstedt wanted to enable data architects and data engineers alike to build a Data Warehouse faster i.e. with a shorter implementation timeframe, and in a way that more effectively addresses the needs of the business.

Wide-reaching benefits

Shorter implementation cycles save time and costs. They also help organizations ensure that the business requirements for the Data Warehouse and for ongoing updates and enhancements to the data model (e.g. due to new data sources) are still valid when projects are completed, rather than having shifting goal posts that could negatively impact timelines and budgets.

Other business benefits of adopting a Data Vault approach include:

  • Flexibility and scalability: a lot of organizations choose the Data Vault approach because of the flexibility it provides. The popular agile approach to project management is closely aligned with the concepts behind Data Vault and the resulting nimbleness businesses can apply to their data strategy – scaling as required. Given the potential cost implications for increasing data storage and processing, it is key to have a data model that can accommodate any required changes in a way that benefits the organization.
  • Parallelization: with data vault modeling, loading data into the Data Warehouse results in fewer points where data needs to be synchronized. This means faster data loading processes which are important for very large data volumes and real-time or near real-time data inserts.
  • Auditability: as Data Vault has a strong focus on historical tracking of data, its data models can be audited easily and effectively. With data security regulations in place to protect people’s data, having an auditable data model supports compliance with requirements.

Like the other data modeling approaches, Data Vault does have some limitations that organizations need to consider.

Understanding the challenges

The approach Data Vault takes when modeling data results in a significantly larger amount of data objects compared to other approaches – for example, tables and columns. This is because Data Vault separates information types. 

Consequently, the up-front modeling effort can be larger compared to other approaches, but will result in the benefits mentioned above. It also means that during the modeling process there can be larger numbers of manual or mechanical tasks involved to establish the flexible and detailed data model with all its components.

Overcoming the hurdles with automation

To avoid time-consuming manual tasks during the modeling process, architects can automate parts of the model, making it more efficient to create, update, and maintain in the long-term.

Within data vault, there are layers of data:

  • Source systems where the data originates;
  • Staging area where data arrives from the source system, modeled according to the original structure;
  • Core data warehouse, which contains the raw vault, a layer that allows tracing back to the original source system data;
  • Business vault – a semantic layer where business rules are implemented;
  • Data marts, which are structured based on the requirements of the business – for example, there could be a finance data mart or a marketing data mart, holding the relevant data for analysis purposes.

Out of these layers, the staging area and the raw vault are best suited to automation – therefore saving architects a lot of time, while enabling efficiency at the same time.

A robust data strategy

Data inefficiencies should no longer be holding businesses back. Organizations can build a sustainable data ecosystem with technology and software elements that support their data strategy for years to come. Complementary tools that support the chosen data modeling technique can enhance the work of the analytics teams and individuals whose day-to-day work relies heavily on a performant data environment that delivers answers for the business.

With a robust approach in place businesses can benefit from the advantages Data Vault brings to their data modeling. They can feel confident that their data can be audited effectively at any point, that they can reproduce historical queries and load large data volumes into the warehouse. Practitioners will experience improved performance when running workflows and analytical models. This will enable organizations to make insight-driven and informed business decisions that can lead to better outcomes for the business and the customers it serves.