For years, data analytics has revolved around one powerful idea: To make data understandable. We built dashboards, crafted compelling data stories, and used visualisation as our superpower to bridge the gap between raw numbers and human insight. The rise of data storytelling helped organisations to see the data and feel what it meant, enabling action, understanding, and alignment across teams.
But as we’ve entered the AI era, the nature of data has changed. To leverage AI for generating more insightful discoveries and supporting better, faster decisions, it is no longer just about metrics in tables or charts. It is about context. And it has always been.
The shift: From data to context
AI thrives not on rows of numbers but on relationships, meaning how things connect, influence, and depend on each other. Modern AI systems need to understand what something is and how it fits into a broader ecosystem of knowledge.
Context, in this sense, is the surrounding information that gives meaning to data and allows it to be correctly interpreted and used. Knowledge is the structured understanding that emerges when these contextual pieces are connected. Together, they turn isolated facts into insight.
This is where knowledge graphs come in. They organise information into entities and relationships, providing the contextual backbone for AI, and enabling reasoning, discovery, and semantic understanding.
Knowledge graphs lay the foundation; ontology engineering gives them structure and meaning. Together, they enable AI to understand context, but the human-facing side, the way we see and tell these knowledge stories, has yet to catch up.
Where are the knowledge (graph) storytellers?
In analytics, we learned that visualisation transforms complexity into clarity. A well-crafted dashboard can reveal insights faster than a thousand spreadsheet rows.
However, when it comes to knowledge graphs, visualisation and storytelling are still in their infancy. Most graph visualisations today are technical tools such as node-edge diagrams, complex networks, and force-directed graphs designed for data scientists or ontology engineers.
They show connections, but they don’t tell stories. They rarely answer questions like:
- Why does this connection matter?
- What is the narrative emerging from these relationships?
- How can this knowledge help us make better decisions?
We still lack storytelling frameworks that make knowledge and context visualisation intuitive and actionable, the same way Tableau and Power BI revolutionised data storytelling.
The new frontier: Visualising context, not just data
Context is what gives meaning to data. It explains why things happen and how they relate to one another.
An emerging theme in the world of data and AI is context visualisation. It isn’t enough to visualise data; we must visualise how knowledge connects and evolves.
Imagine being able to:
- Navigate your enterprise knowledge like an interactive map of concepts and relationships.
- See how a single idea, customer, or process propagates through a network of dependencies.
- Build narratives that trace cause and effect across a dynamic web of knowledge.
This kind of visualisation would enable AI explainability, data lineage understanding, and strategic storytelling across complex ecosystems, from industrial operations to healthcare to organisational intelligence.
It turns abstract networks into narratives by making relationships, meaning, and consequences visible.
Toward knowledge storytelling
Just as data storytelling humanised analytics, knowledge storytelling will make AI more interpretable and meaningful. It is about moving from the question “What does the data say?” to “What does the knowledge mean?”.
And this is precisely where ontologists come in as the enablers of knowledge storytelling. By defining the meaning of concepts and the logic that connects them, ontologists create the foundation upon which meaningful narratives about knowledge can be built and visualised. Without their work, context remains invisible, fragmented, and difficult to communicate.
In the end, knowledge storytelling is about bridging human intuition and machine understanding. It’s about creating tools and languages that let both humans and AI navigate context intuitively together.
Ontology engineering gives us the foundation; knowledge storytelling gives it a voice.
We know how to visualise data. Now it’s time to visualise the meaning. Want to dive deeper? Read our blog series: Orchestrating agentic AI.