摘要: With the business world in a constant state of flux, flexibility is more important than ever for organisations of every stripe. Data-driven organisations have fared best; those with an enterprise data architecture that allows them to understand change and adapt to the volatility of current markets and supply changes are more resilient than their counterparts.

 

 

However, most dimensional and normalised data modelling techniques aren’t designed to respond to fast changes like this. Data Vault modelling, on the other hand, helps to address this – equipping organisations with greater speed and flexibility for their analytics needs.

ORIGINS OF DATA VAULT MODELLING

Data Vault is a detail-oriented data modelling approach designed to provide flexibility and agility when data volumes grow, and/or when they become more distributed and sophisticated. Businesses that can address these challenges in their data model, are better placed to make faster, more informed business decisions.

The Data Vault approach, created by Dan Linstedt in the 1990s, was designed to make these benefits accessible to everyone. It was followed by Data Vault 2.0 in 2013, offering a suite of enhancements centred around NoSQL and Big Data as well as the introduction of integrations for unstructured and semi-structured data.

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轉貼自: dataconomy

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