The data management plan for the future of the business

In today's era of massive and complex data, effective data management has become a priority for organizations. One methodology that has gained recognition and acceptance is the Data Vault, a revolutionary approach to data warehouse design and implementation.

In this article, we will explore what Data Vault is, what it is for and how it relates to data warehousing, data science and big data. We will add some concrete examples to help you better understand this concept.

What is the Data Vault?

Data Vault is a data management pattern that focuses on scalability, flexibility and agility. It provides an architecture and set of principles for building enterprise data warehouses.

Unlike traditional data warehouse design approaches, which are often rigid and costly to maintain, the Data Vault is highly adaptable to changes for business requirements and constantly evolving data.

The core concept of the Data Vault is to divide data into three main types:

HUBS (hubs)

LINKS (connections)


Hubs represent key business entities and are used to store unique records of these entities. Links connect Hubs and represent relationships between entities. Finally, Satellites contain additional attributes and metadata related to Hubs and Links.

What is the Data Vault for?

The Data Vault provides a number of significant benefits for data management. The most important of these are listed below.


The modular design of Data Vault allows new entities and relationships to be added without having to rebuild the entire data warehouse. This facilitates scalability as data volumes increase over time.


Data Vault allows the incorporation of data from different sources and formats without requiring significant changes to the existing structure. This provides flexibility to adapt to new data sources and changes in business requirements.


The Data Vault stores a complete history of data, allowing changes to be tracked and audited over time. This is especially valuable in regulatory or compliance environments.


The modular approach and flexibility of Data Vault enables faster and more agile implementation of new requirements and business changes. This accelerates the time to delivery of data management projects.

Relationship with Data Warehouse, Data Science and Big Data

The Data Vault relates directly to the data warehouse, data science and big data, playing an important role in each of these domains.

As for the data warehouse, the Data Vault provides a solid and scalable foundation for its construction. By separating data into entities and relationships, the Data Vault facilitates the integration of data from various sources into the data warehouse. In addition, its flexible structure allows for the expansion of new sources.

For data science, its modular structure facilitates the creation of complex analytical models. And for Big Data, the Data Vault adapts to handle large volumes and varieties of data, leveraging parallel processing capabilities for optimal performance.

When is the Data Vault applied? Examples


Imagine an e-commerce company that sells a wide variety of products online. Using the Data Vault approach, the company can create Hubs to represent key entities, such as customers, products and sales orders.

Links would be used to establish the relationships between these Hubs, such as the relationship between a customer and a sales order or between a sales order and products purchased.

Satellites can store additional information, such as transaction details, data changes and relevant metadata. This enables the company to perform sales analysis, customer tracking and detect buying behavior patterns, such as customer segmentation based on buying preferences.


Let's now think about a TELCO that offers cell phone, Internet and cable TV services. This company needs to manage large volumes of customer, billing, service usage and network equipment data.

Using a Data Vault approach, the company can design a data warehouse that reflects the complexity of its business and allows it to obtain valuable information for strategic decision making.

In this case, Hubs would represent key entities, such as customer services, contracted services, network equipment and geographic locations. Links would connect these Hubs to establish relationships, such as the relationship between a customer and a contracted service or between a network equipment and a geographic location.

Satellites would store additional attributes and metadata related to Hubs and Likns, such as billing information, service usage records and technical details of networked equipment.

This approach would allow TELCO to analyze customer behavior, identify service usage patterns, optimize network resource reduction and perform cost-effectiveness analysis by geographic location.

Thus, the Data Vault would provide the flexibility to add new services, adapt to changes in network infrastructure, and manage the increasing complexity of the telecommunications industry. In addition, this approach would enable the enterprise, for example, to detect fraud.

In summary, the Data Vault stands out as an approach of great relevance and value. Its importance lies in its ability to address the challenges inherent in managing complex, voluminous and changing data in different industries.