Data Weaver-AI-powered data graphing tool

Example

A user inputs a raw dataset with many interrelated variables, such as a list of tasks for a large software project. Data Weaver analyzes these variables and breaks them down into distinct categories, such as tasks, deadlines, dependencies, and team members.

Scenario

In a software development environment, where task dependencies must be mapped out, Data Weaver identifies the critical path of the project by analyzing which tasks depend on each other.

Example

After processing data, Data Weaver generates node and edge files in CSV format, representing the data in a graph structure. Each node represents a distinct element of the dataset, and the edges define the relationships between those elements.

Scenario

For a logistics company managing shipments, Data Weaver creates a node graph of supply chain routes and uses edge files to show dependencies and connections between shipping locations, delays, or costs.

Example

Data Weaver automatically recognizes patterns in processes (e.g., project steps, workflows) and enhances them by adding implicit relationships and new attributes to the data.

Scenario

In a manufacturing setting, Data Weaver detects implicit relationships between production phases and suggests optimizations by introducing new nodes that represent bottlenecks or parallel workflows.