Why You NEED a Data Vault For Your Data Vault

Create a MetaVault to transform Data Vault’s power

A month in the Chat GPT-4 age and its consequences for those people working in the Data Vault world already seem more like a generation for those following the “revolution.” An exaggeration? Well not if you were one of the very lucky people to have sat in listening to InfoVia’s Christopher Siegfried’s presentation to this month’s Data Vault User Group. Barely a month since the latest version of the open Artificial Intelligence-driven new chat bot – which is already revolutionising tools for developers when seeking solutions for coding problems, and eliminating errors through increased automation – Christopher has found exciting ways to make your Data Vault an even more powerful data analytics service. The data architect has identified and innovative way for organisations to make best use of their Data Vault by creating what he is calling a MetaVault. This is where your Data Vault uses the metadata from your data warehouse source feeds loaded into the raw vault.. He believes that a really good Data Vault requires its own MetaVault – using increased Data Vault vocabulary and an expanded semantic layer, to really make it sing with far more possibilities to increase its business value. Christopher started by explaining that while many Data Vault projects aim to model the business accurately with a deep understanding of the relationships between the different business users in the divisions and departments , can change, and normally weaken over time. A Data Vault relies on smart keys for object names and the standardised relationships between those objects But Christopher fears that much of the information used to build a typical Data Vault project is never captured. Knowledge of the organisation and knowledge of the Data Vault itself has to be tracked to keep the latter relevant and resourceful. And one of the most obvious problems, is when members of the design team, or key business users leave the organisation, resulting in that knowledge being lost. Christopher then looked at three clients he has helped using his domain-driven design principles. In every case, the ontology mapping – i.e. the semantics and language used in describing the enterprise – and metadata management have literally proved key in finding the right solutions. So if one department defines a sale as an invoice, while another regards it as merely a sale, your hubs need to have sub-hubs that can capture that database relationship. By adding those explanations in the business notes, your Data Vault’s hubs, links and satellites can identify those distinctions precisely. The descriptive metadata is information about a resource, used for discovery and identification while structural metadata about containers of data indicates where compound objects are put together. Christopher then explained how he had experimented with ChatGPT-4 over the past month to marry descriptive and structural metadata by creating bridge tables and tracking satellites for certain hubs as well as selecting a random satellite in the new MetaVault, to create a tracking object. The responses were there for all to see… there was a perceptible thud of jaws dropping even in the muted world of online Zoom webinars. Where will the technology take us over the next few months, let alone weeks? Who knows? So buckle up and enjoy the ride – our monthly meetings are bound to return to this most interesting development.