Data-Centric
What is Data-Centric?
Data-centric describes an approach to information management in which structured data, instead of documents, serves as the primary source of truthWhat Is a Source of Truth? A source of truth is the informat... More. Information about each asset is stored as individual attributes in a governed system, and drawings or reports can be generated from that data as needed.
Because each value is maintained in one place, every team and system that relies on it works from the same current information. Data-centric environments represent an advanced stage of digital maturityReVisionz · Digital Maturity - ReVisionz Terminology Glossar... More, where asset information is managed as a connected resource across the lifecycle instead of as a collection of separate files.
Document-Centric vs. Data-Centric
In a document-centricReVisionz · Document-Centric - ReVisionz Terminology Glossar... More environment, information lives inside files such as PDF files and scanned drawings. Locating one value means finding the right document and verifying it’s up to date requires digging through its revision history.
For example, if a pump’s design pressure changes during a project, a document-centric approach requires someone to find and update every file and drawing that lists it.
In a data-centric approach, however, the value is updated once at the source, and every document that references it reflects the change.
Where It Sits in Digital Maturity
Data-centric information management is the fourth of five stages in the hierarchical progression of digital maturity:
- Project Approach: Information is managed ad hoc, project by project, with heavy reliance on paper.
- Document-Centric: Information is digitized but unstructured, stored mainly in document management and maintenance systems.
- Tag-CentricReVisionz · Tag Centric - ReVisionz Terminology Glossary Wha... More: Documents and records are organized around tag numbers, creating semi-structured connections between related information.
- Data-Centric: Asset information is fully structured, with attributes governed and shared across systems.
- Generative AI Agents: Contextualized data supports AI agents and advanced analytics across the enterprise.
Key Characteristics
A few features set a data-centric environment apart from earlier stages of maturity:
Attribute-Level Control: Each piece of information is managed as a discrete attribute with a defined owner and format. Quality can be checked automatically, because the system knows what a complete and valid record should contain.
Documents as Outputs: Documents still exist, but they are views of the data instead of the place where it is kept. A file or line list is generated from current values, which removes the risk of two versions disagreeing.
Integration Across Systems: Structured data moves between engineering tools and enterprise systems through defined integrations. Each system reads from the same governed record instead of storing its own version, so a change made in one place is visible everywhere it applies.