21-5-2024: DTACT started a strategic partnership with Axite aiming to jointly develop innovative data solutions. Read More

Raven Fusion goes beyond technological boundaries and allows the creation of an information landscape in which data remains in its original source.

Combine this with a virtual mesh of all connected systems, infrastructures or sources is created for instant insight discovery and action purposes.

Mix data like a mad scientist and create a robust common operational picture

DTACT developed a unique and fully agnostic technology to break the data silos, to speed up any Data Warehouse project, or even to render them completely unnecessary.

By following a traditional Extract, Transform, and Load (ETL) process or by creating a Data Mesh, we allow you to tap into any data source, in any language or any format using our RAW data and NONTOLOGY™ methodology.

The RAW data principle

Owning many different types of data sources require more flexibility when it comes to fusing data. Ideally, you want to access data 'as- is' and not worry about reformatting it. With our technology, you'll always have the opportunity to go back to the source with the following benefits:

  • Keeping your data source in its original raw format
  • Defining and labelling granular access and retention controls from the start
  • Extensive knowledge management options supporting decision-making between RAW data formats, queries, AI, or insights produced by humans

The NONTOLOGY™ principle

We're not saying having an ontology isn't useful at certain points in time but what if it would be completely unnecessary to define this upfront? Wouldn't that save up a lot of time and frustration?

By using DTACT's NONTOLOGY™ principle, it is no longer necessary for partners, internal departments or distributed teams to agree on the standardization of data models first before having meaningful data integration. Book a demo with us to see how this works together with our RAW data principle.

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Fusion Mesh:

A data search engine

With Raven Fusion Mesh, you can search through connected, structured, unstructured, and siloed data sources to find answers based on the query command. And, like well-known search engines, you'll have immediate results without building a data lake or datawarehouse first.

Think of your organization's data like a big city with different neighborhoods. Each neighborhood represents a different aspect of the data. In a data mesh, instead of trying to cram everything into one central downtown area, you decentralize. Each neighborhood manages its own data, making it easier to navigate and control.

Want to start with our Raven platform?