Semantic data readiness for standardized industrial collaboration

About this project

The potential and effectiveness of industrial data flows depend on the extent to which it is possible to create meaning and significance from data. In practice, this means that data must not only be accessible, correct and technically integrated, but also understandable, unambiguous and contextualized across system, organizational and life cycle boundaries. Without a common semantic foundation, data risks remaining fragmented information – contextless, contradictory, difficult to interpret, combine and reuse.

As automation, digitalization and the use of advanced analysis and AI increase, this need is further strengthened. Algorithms and decision support systems require not only large amounts of data, but data whose meaning is explicitly defined and machine-interpretable. Lack of semantic clarity leads to misinterpretations, inconsistent analyses and limited scalability, which in turn hinders innovation and business benefit. This can lead to misjudging the possibilities of, or perhaps even not daring to get started with, AI, which in itself is a danger to Swedish industry.

Addressing this challenge means ensuring that data is structured, described, and related in a way that makes its meaning unambiguous and shareable. This means formalizing concepts, relationships, and assumptions—often using common information models and ontologies—so that both humans and machines can interpret the data in the same way. Only then can industrial data flows fully support cross-functional processes, reliable decisions, and future-proof digital ecosystems.

Read more on: www.semanticdatareadiness.org

Challenges

Across Swedish industry, data sits in incompatible systems with the context that gives it meaning stripped away. Engineers misread each other's records, partners can't reconcile data across organisational boundaries, and AI systems fail silently on inputs they can't truly interpret. We're researching and disseminating the methods, means needed, maturity, and shared understanding that let any Swedish industrial actors make their data interpretable — by humans and machines alike. 

Objectives and expected outcomes:

A national capability shift: shared methods, standards, and ways of working that let Swedish industry actually realise the value of its data. 

For more information about this project, visit semanticdatareadiness.org or reach out to: