From Siloed to Unified: How Conode Simplifies Data Fusion

In our last blog, we gave an overview of data ingestion—the process of bringing raw data into the platform– which you can check out here if you haven’t already ⏮️.

📍 Today, we’re diving into the next essential step in building a knowledge graph: Data Fusion.

Data fusion—the process of connecting siloed, complex, and heterogeneous datasets to uncover valuable relationships and insights.

Conode automatically fuses siloed data sources into a unified Knowledge Graph, ready for AI.

Why Is Data Fusion So Challenging?

Enterprise data is often scattered, inconsistent, and varied in structure, making it difficult to connect and analyse efficiently:

Siloed systems: Data often resides in isolated platforms, limiting visibility and preventing a unified view.
Heterogeneous structures: Data can take many forms—structured, semi-structured, unstructured, or even mixed-media—making it difficult to integrate seamlessly.
Semantic mismatches: Inconsistent terminology and conflicting data representations create barriers to connection.

Traditional integration methods struggle to address these issues—they're time-consuming, error-prone, and fail to provide timely, actionable insights.

Knowledge graphs overcome these challenges by connecting all types of data in a unified, navigable structure.

What if you could painlessly merge all your data into a single, unified view?

Knowledge graphs make this possible. They unify all types of data—structured, semi-structured, unstructured, or even mixed-media—into a single, navigable representation. For example, a knowledge graph can connect:

User activity logs—structured, timestamped actions.
Survey responses—unstructured, free-text data connected to a user.
User profiles—structured metadata linking out to external systems.

In Conode, these disparate data types coexist as nodes and edges, linked by shared identifiers such as a user ID.

The result? Relationships and patterns become immediately observable, enabling richer, more dynamic analyses than traditional approaches.

But achieving this unified view all starts with data fusion—the critical step of connecting siloed, complex datasets to uncover these valuable relationships and insights.

Why is Conode the Best Choice for Data Fusion?

At Conode, we’ve spent years solving complex data fusion challenges across diverse industries. Our experience equips us to handle the most fragmented, siloed, and heterogeneous datasets, no matter the use case.

Here’s what makes Conode unique, and how businesses are using it to drive results:

Automated Fusion for Efficiency: Conode’s AI-powered schema unification eliminates repetitive tasks, letting you harmonise datasets with ease.
- Example: A marketing team combines survey feedback with behavioural logs to uncover links between customer intent and actions.
Manual Refinement for Precision: Conode’s advanced tools ensure fine-grained control over your data. Resolve duplicates, align taxonomies, and perfect relationships as needed.
- Example: Autonomous vehicle developers link scenario-testing data to regulatory taxonomies like PAS 1883, identifying gaps for safer systems.
Scalable Across Complex Domains: Conode’s flexibility lets you integrate structured, semi-structured, unstructured, and even multimedia data into a unified graph.
- Example: Enterprises merge internal taxonomies with external standards for compliance while unlocking cross-industry insights.
Auditability and Transparency: Clear, traceable workflows ensure confidence and compliance in every connection you build.

Whether you’re driving innovation in R&D, enhancing operational workflows, or creating a foundation for AI and advanced analytics, Conode transforms raw, disconnected data into a powerful, unified graph—unlocking insights that help you stay ahead.

How Conode Enables Seamless Data Fusion

Automatically Fused Schema from Structured Data

Conode transforms structured data sources into a unified schema in seconds, ready for advanced analysis or AI applications.

Identifies overlaps between datasets, resolves inconsistencies, and merges schemas.
Automatically links relationships across tables to create a navigable, connected graph.
Outputs a unified graph for easy analysis in graph or tabular formats.

Example: UK Road Collision Database.

Using public STATS19 datasets, Conode merges data on accidents, vehicles, and casualties stored in PostgreSQL. In seconds, it links related entities, building a connected view of all road collision data.

Eliminate Duplicates across Data Imports

When merging datasets from multiple sources, duplicate entities can cause clutter and inconsistencies. Conode simplifies this process by detecting and merging these duplicates into a single representation.

Example: Cleaning Topic Tags of NASA JPL Scientific Papers.

We start with approximately 2,000 feature nodes representing topics of NASA’s Jet Propulsion Laboratory (JPL) papers, sourced from multiple imports. This redundancy arises because the same topics were imported from different collections. Using Conode’s Group by Label tool, we drag and drop feature nodes into groups based on their labels. After reviewing the groups to ensure all duplicates are accounted for, we apply the Merge by Group function to reduce the 2,000 redundant nodes to 1,700 unique ones, leaving a cleaner, streamlined dataset.

Group Features by Label Similarity

Some features across datasets may be similar in meaning but differ in spelling, punctuation, or formatting. Conode offers natural language based label similarity clustering to group and unify these features.

Example: Further Simplifying Topics of Fused JPL Papers.

Continuing with the JPL dataset, we take 1,700 topics and further group similar labels such as "Mars" and "mars." Using the Group by Meaning tool, we prompt the agent to ignore case sensitivity and punctuation. Conode identifies and clusters variations under a unique header, simplifying the taxonomy down to around 780 unique terms—ready for analysis and actionable insights.

Fuse Data Sources with Semantic Label Embeddings

Semantic embeddings go beyond label similarities, leveraging AI to identify deeper, conceptual patterns across unstructured data. This method is ideal for aligning entities or topics that vary significantly in naming conventions but share thematic relevance.

Example: Merging News Articles on Mining Commodities.

Here, we fuse datasets containing news articles about copper and iron commodities. Using Conode’s embedding tool, each label is placed within a semantic space where similar terms are closer together. By analysing this embedding space, articles covering related topics are easily grouped, enabling topic-based analysis and unification.

Build Clusters by Spatial Proximity

For data tied to physical locations, Conode enables clustering based on spatial relationships.

Example: Clustering Event Data Across London.

Event-based datasets, such as citywide incident reports, are mapped to a geographic view of London. Conode groups events based on proximity, creating clusters that can be used to visually highlight hotspots across the city. These clusters can be further analysed or exported to explore trends like high-activity areas or recurring event patterns.

Maintain Data Control: Manual Editing and Refinement

Beyond the data fusion tools, the core of Conode’s patented graph technology is the interactive visual editor which offers full manual control over data connections. Users can refine relationships, resolve overlapping clusters, and ensure a highly accurate graph that meets specific project needs.

What’s Next?

Stay tuned for the next blog in this series on how to how to build an Ontology from Unstructured Data, code free and naturally.

In the meantime, if you have any questions about how Conode could elevate your own data, feel free to reach out to info@conode.ai—we’re here to help you connect the dots. 🤓

Shona

Product Manager, Conode