# Reviving our data foundations is the most disruptive step to data maturity

arXiv: https://arxiv.org/abs/2608.29368 · HTML: https://mlsanity.com/paper/2608.29368/

## 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.

## Builds on (most cited first, 5 of 5)

- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [End-to-End Ontology Learning with Large Language Models](https://mlsanity.com/paper/2410.23584/) (2024)
- [Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations](https://mlsanity.com/paper/2302.01713/) (2023)
- [A formalisation of BPMN in Description Logics](https://mlsanity.com/paper/2109.10716/) (2021)
- [Ontology-Driven Model-to-Model Transformation of Workflow Specifications](https://mlsanity.com/paper/2511.13661/) (2025)

## 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.