Reviving our data foundations is the most disruptive step to data maturity
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Abstract (arXiv)
The most disruptive step that enterprises of small-medium size and maturity can take to make the most of the latest technological advances in AI is to step back from the hype and focus on establishing or reviving a good knowledge foundation layer. It is a hard message to present to the executive team; therefore, it needs to be backed by evidence, and its implementation needs to be of minimal impact on the existing processes. In this vision statement, we discuss how we need to rethink what evidence speaks to the decision-makers and propose a low-impact data strategy that adapts to the existing and ever-changing data flows and processes across the company. We firmly believe that knowledge graph techniques will increasingly become non-negotiable in the data strategy of an AI-powered enterprise, provided that we approach their design in a modular, dynamic and cross-functional way.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 29 Aug 2026.
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5 of 13 references.
References · 5
- 2023LLMs4OL: Large Language Models for Ontology Learningcited by 77
- 2024End-to-End Ontology Learning with Large Language Modelscited by 13
- 2023Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementationscited by 2
- 2021A formalisation of BPMN in Description Logicscited by 1
- 2025Ontology-Driven Model-to-Model Transformation of Workflow Specificationscited by 1
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