Introduction

Welcome to the ONA Dashboard documentation. This guide will help you get started with organizational network analysis and visualization.

What is ONA Dashboard?

ONA Dashboard is a powerful tool for visualizing and analyzing organizational networks. It helps you understand:

• Communication patterns between individuals and teams
• Information flow across your organization
• Key influencers and connection hubs
• Temporal trends in network activity

Key Features

Network Visualization

Create interactive 2D and 3D network graphs to visualize relationships between entities. Color-code nodes by department, adjust edge weights based on activity or trust scores, and tune physics parameters (repulsion, gravity, collision) to untangle dense networks.

Sankey Charts

Visualize flow and relationships between groups with weighted connections using Sankey diagrams.

OWL (Other Ways of Looking)

A configurable chart slot with multiple visualization types: Venn Diagrams for group overlap analysis, Heatmap for department-to-department communication density, and more coming soon (Radar, Box/Violin, 3D Mesh). Switch between chart types in Graph Settings.

Time Series Analysis

Track department activity over time with ridgeline plots and animated timeline playback.

Geographic Mapping

Overlay your network on a world map when latitude/longitude data is available.

AI Assistant

Ask natural language questions about your network and get intelligent analysis powered by LLMs. Pre-computed ONA metrics (centrality, structural roles, bridging scores, efficiency, clique analysis, suggested connections) provide rich context. An in-browser Python playground lets you run custom NetworkX, Pandas, and Matplotlib code directly in the chat.

Sample Datasets

Download these sample datasets to try the dashboard immediately:

Game of Thrones Network

Character interaction network from the book series — great for exploring community detection, centrality, and structural roles.

File	Description
got-edges.csv	352 weighted edges between characters (Source, Target, Weight)
got-nodes.csv	107 characters with House and Role attributes

Quick start: Import got-edges.csv as your edge file, then upload got-nodes.csv as the node file. Set Source, Target, and Weight columns, then Create Graph.

Contoso Email Network

Fictional corporate email metadata — realistic edge and node files for organizational analysis with timestamps.

File	Description
contoso.edge.csv	Email edges with sender, recipient, datetime, and weight
contoso_nodefile.csv	Employee directory with department and role

Quick start: Import contoso.edge.csv as your edge file, then upload contoso_nodefile.csv as the node file. Map sender → Source, recipient → Target, datetime → Time, then Create Graph.

Access Tiers

Tier	Access	How to Get It
Guest	Sample datasets, 2D visualisation	No login needed — start exploring immediately
Trial	All Pro features for 14 days	Sign up with email and password (free)
Pro	All features, unlimited	Paid subscription

Signing Up

1. Click Sign up for a free trial on the dashboard welcome screen, or click Sign In in the header
2. Fill in your company name, name, email, and password
3. A verification email will be sent to your address — this may take a few minutes to arrive. Check your spam/junk folder if you don't see it
4. Click the confirmation link in the email
5. Return to the dashboard and sign in with your credentials
6. Your 14-day Pro trial starts immediately after first sign-in

Guest Access

You don't need an account to explore. Guests can load sample datasets (Game of Thrones, Contoso) and visualise them in 2D. Sign up when you're ready to import your own data and unlock all features.

Getting Started

1. Download sample data (optional) — grab the sample datasets above to try things out, or select one from the sidebar as a guest
2. Import your data — upload CSV, TSV, or GraphML files via Settings → Import Data
3. Add node attributes (optional) — upload a separate node file with metadata like department, role, or group
4. Configure columns — map your data columns to source, target, and optional attributes in the sidebar
5. Adjust graph settings — customise display options via Settings → Graph Settings
6. Generate visualisations — choose your preferred visualisation type from the Analytics menu
7. Explore and analyse — use timeline controls, filters, and interactive features

Common Data Scenarios

Below are three real-world data sources and how to get the most out of each in the ONA Dashboard.

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Scenario 1: Email Metadata

Source: Microsoft 365 message trace, Google Workspace email logs, or any sender/recipient export.

Typical columns: Sender, Recipient, DateTime, Subject, Size

Sample: contoso.edge.csv + contoso_nodefile.csv

How to Import

1. Upload the email CSV as the edge file
2. Upload a directory CSV (email, department, role) as the node file
3. Map: Sender → Source, Recipient → Target, DateTime → Time Column, department → Color By

Best Visualisations

Tool	What It Reveals
Network Graph (Individual, Directed)	Who emails whom — shows communication hubs and isolated individuals
Network Graph (Aggregated Groups)	Department-level traffic — click a department to see its members
Cluster Hulls	Are real communication communities matching the org chart?
Sankey Chart	Volume of email flow between departments
Time Series	Daily/weekly email patterns — spot spikes, quiet periods, burnout risk
AI Assistant	"Who are the top 5 brokers?" "Which departments are siloed?"

Recommended Settings

• Edge Direction: Directed — shows who initiates vs who receives
• Node Labels: On — see names
• Graph Dimension: Start Individual, switch to Aggregated for the big picture
• Physics: Repulsion -200, Link Distance 120, Collision 8

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Scenario 2: Microsoft Teams Activity

Source: Teams admin reports, Graph API activity exports, or message/call/meeting logs.

Typical columns: Sender, Recipient, Timestamp, ActivityType, ChannelOrChat, TeamName, Duration

Sample: TeamsActivity_contoso.csv + contoso_nodefile.csv

How to Import

1. Upload the Teams activity CSV as the edge file
2. Upload a directory CSV as the node file (the TeamName column only covers channel messages — a node file gives department for all activity types)
3. Map: Sender → Source, Recipient → Target, Timestamp → Time Column, department → Color By

Best Visualisations

Tool	What It Reveals
Network Graph (Individual, Directed)	Chat/call/meeting relationships — thicker edges = more interactions
Network Graph (Clustered Layout)	Visual department clusters while keeping individual nodes
Cluster Hulls	Cross-team collaboration patterns detected by Louvain algorithm
Sankey Chart	Flow between departments — which teams talk most?
Venn Diagrams	Overlapping relationships between 2–3 departments
Time Series	Hourly/daily activity patterns — when are people most active?
AI Assistant	"Who bridges Engineering and Sales?" "Find the most isolated people"

Recommended Settings

• Edge Direction: Directed — shows who initiates conversations
• Node Labels: On
• Physics: Repulsion -250, Link Distance 150, Collision 8 (Teams data tends to be denser)
• Timeline: Use the time slider to focus on specific days or hours

Tips

• Teams data includes external contacts (e.g., ext1@gmail.com) — these appear as nodes without department colours, making it easy to spot external collaboration
• The ActivityType column (ChatMessage, Meeting, Call, ChannelMessage) is preserved — you can filter in the AI Assistant to analyse specific interaction types
• Meetings and calls have a Duration column — longer durations suggest stronger working relationships

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Scenario 3: Calendar / Meeting Data

Source: Microsoft 365 calendar export, Google Calendar, or any Organizer/Attendees format.

Typical columns: Organizer, Attendees (semicolon-separated), Subject, Start, End, Duration, Location, IsOnline

Sample: Calendar_contoso.csv + contoso_nodefile.csv

Auto-Detection

Calendar CSVs are automatically detected by the dashboard. When the system finds Organizer and Attendees columns, it explodes each meeting into individual Organizer → Attendee edges. Declined meetings are filtered out. No manual pre-processing needed.

How to Import

1. Upload the calendar CSV as the edge file — the dashboard auto-converts it to an edge list
2. Upload a directory CSV as the node file
3. Map: Source → Source, Target → Target, Start → Time Column, department → Color By

Best Visualisations

Tool	What It Reveals
Network Graph (Individual, Directed)	Who organises meetings with whom — reveals meeting "hubs" and power brokers
Network Graph (Aggregated Groups)	Cross-department meeting patterns at a glance
Sankey Chart	Which departments hold the most meetings together
Time Series	Meeting density over time — are calendars getting busier?
AI Assistant	"Who organises the most meetings?" "Which people are in the most cross-team meetings?"

Recommended Settings

• Edge Direction: Directed — the organiser is the source, showing who drives meetings
• Node Labels: On
• Physics: Repulsion -200, Link Distance 100 (calendar data creates hub-and-spoke patterns around organisers)

Tips

• Each meeting with N attendees becomes N edges — a meeting with 8 people creates 8 Organiser → Attendee connections, making frequent organisers appear as large, highly-connected hubs
• The Subject column is preserved on each edge — useful for context when hovering over nodes
• IsRecurring meetings indicate established working relationships vs one-off interactions
• Sensitivity (Normal/Private/Confidential) lets you assess which meetings are visible organisationally
• Combine with the Teams Activity dataset for the same organisation to compare scheduled (calendar) vs ad-hoc (chat/call) communication patterns

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Combining Multiple Data Sources

For the richest analysis, import multiple datasets from the same organisation:

1. Email — shows formal, documented communication
2. Teams Activity — captures informal chat, calls, and channel discussions
3. Calendar — reveals meeting structures and who convenes whom

Each dataset creates a different view of the same network. Switch between them in the sidebar dropdown to compare patterns. The AI Assistant can analyse each independently.

Node File Reuse

The same node file (employee directory with email, department, role) works across all three data types. Import it once with each dataset to get consistent department colouring.

Import Options

The dashboard supports multiple import workflows:

• Single file import — upload a CSV/TSV with edge data
• Calendar import — upload a calendar CSV with Organizer/Attendees columns (auto-detected and exploded)
• GraphML import — upload .graphml files from Gephi, NetworkX, or other tools
• Edge + Node import — upload separate edge and node files for rich metadata
• Update nodes — add or update node attributes on existing datasets

Continue to the Data Format guide to learn how to prepare your data.