> Markdown version of [/videos/2051-your-organization-as-a-graph?t=1330](https://www.wearedevelopers.com/videos/2051-your-organization-as-a-graph?t=1330). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Your organization as a Graph

What happens when you map your company's communication and code commits as a graph? You uncover the hidden collaboration networks and silent bottlenecks that truly run your organization.

- **Speakers:** [Dunya Kirkali](https://www.wearedevelopers.com/@dunya-kirkali)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 2, 2026
- **Duration:** 33:43
- **URL:** https://www.wearedevelopers.com/videos/2051-your-organization-as-a-graph

## Summary

Traditional organizational charts outline structural accountability but consistently fail to capture how work is actually executed. As companies navigate reorganizations, layoffs, and shifting operational dynamics, leadership often relies on gut feelings rather than empirical data to restructure teams. By modeling an organization as a graph, engineering and people operations leaders can map the hidden networks of cross-functional collaboration, transforming static reporting lines into dynamic visualizations of communication, code interactions, and software system ownership.

This analytical approach relies on augmenting standard organizational data with multiple layers of relational telemetry. Mapping communication lines via messaging platforms or calendars reveals workflow bottlenecks and essential "bridge builders" connecting disparate departments. Overlaying service ownership data—utilizing documentation tools like Spotify's Backstage—exposes tight architectural couplings and highlights areas where teams are unbalanced, effectively visualizing Conway's Law in real time. Furthermore, analyzing pull requests and code review patterns uncovers the true technical boundaries, exposing domain experts who may have become critical workflow chokepoints and proving that the codebase fundamentally remembers how an organization operates.

To extract actionable organizational insights from these complex networks, specialized graph algorithms can be applied to databases like Neo4j. The Leiden community detection algorithm identifies densely connected clusters of developers, which is highly effective for discovering hidden functional teams or strategically breaking down software monoliths. Calculating betweenness centrality pinpoints individuals acting as vital conduits or single points of failure, while PageRank highlights the most influential services requiring architectural attention. By connecting these graph databases to LLM agents, leaders can query hypothetical scenarios—such as assessing the impact of a key developer's departure—enabling safe, data-driven experiments. Ultimately, mapping these networks requires strict ethical boundaries, ensuring that collaboration and interaction metrics are used for structural health rather than individual performance evaluation.

**Keywords:** organizational graph modeling, structural accountability analysis, communication network mapping, service ownership tracking, conway's law visualization, pull request analytics, software architecture dependencies, leiden community detection, betweenness centrality metrics, pagerank algorithm application, neo4j graph databases, engineering team restructuring, hidden network discovery, cross-departmental bottlenecks, llm graph querying, distributed team analytics

## Chapters

1. **Viewing organizations as complex relational data graphs** (00:00) — How representing teams as relational graphs clarifies collaboration impacts during reorganizations and technology shifts.
1. **Mapping traditional reporting structures and standard hierarchies** (02:23) — Establishing a baseline structural model using standard reporting lines for a fictional company.
1. **Enhancing organizational models with cross-functional communication data** (03:56) — Enhancing hierarchy models with chat and email data to reveal true cross-functional collaboration and bottlenecks.
1. **Visualizing service ownership and cross-team system dependencies** (07:55) — Mapping service ownership to identify tight coupling, unbalanced workloads, and architectural boundaries.
1. **Tracking code review interactions to uncover technical silos** (11:40) — Tracking code review interactions to uncover technical silos, domain experts, and true ownership behaviors.
1. **Incorporating trust surveys and work item interactions** (14:36) — Utilizing trust surveys and work tracking items to identify natural leadership and hidden communication channels.
1. **Detecting hidden collaborative clusters with the Leiden algorithm** (16:04) — Applying community detection algorithms to simplify monolithic structures into tightly connected functional teams.
1. **Identifying organizational bottlenecks using betweenness centrality calculations** (18:30) — Using shortest-path algorithms to spot critical communication connectors and single points of failure.
1. **Evaluating system influence using the PageRank algorithm** (20:06) — Highlighting heavily dependent software services and influential personnel that require strategic structural support.
1. **Simulating organizational changes via large language models** (22:10) — Querying graph databases through AI agents to test staffing scenarios and predict project resource needs.
1. **Constructing an iterative organizational data ingestion pipeline** (24:15) — Constructing a continuous data pipeline to transform organizational hypotheses into iterative graph database queries.
1. **Managing ethical concerns and analytical expectation boundaries** (29:07) — Handling employee interaction data responsibly while framing analytical insights as tools rather than absolute performance metrics.

## Related Moments

- [Leveraging connected teamwork graphs to generate massive AI returns](https://www.wearedevelopers.com/videos/100068-the-ai-fluent-team-a-playbook-for-driving-enterprise-ai-transformation) (from "The AI-Fluent Team: A Playbook for Driving Enterprise AI Transformation")
- [Utilizing graph analytics and Neo4j for structural dependencies](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) (from "Data Science on Software Data")
- [Building contextual layers with an organizational teamwork graph](https://www.wearedevelopers.com/videos/100004-the-ai-native-engineering-org-what-s-real-what-s-hype-what-s-next) (from "The AI-Native Engineering Org: What’s Real, What’s Hype, What’s Next")
- [Improving data governance and auditability with graphs](https://www.wearedevelopers.com/videos/1160-give-your-llms-a-left-brain) (from "Give Your LLMs a Left Brain")
- [Uncovering team dynamics through version control data analysis](https://www.wearedevelopers.com/videos/1342-your-code-as-a-crime-scene) (from "Your Code as a Crime Scene")
- [Modeling connected data using graph structures](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) (from "Cyber Sleuth: Finding Hidden Connections in Cyber Data")

## Related Articles

- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)
- [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)

## Related Jobs

- [Tribe Lead - ( Software) Engineering Centre of Excllence](https://www.wearedevelopers.com/jobs/ext/1475530-tribe-lead-software-engineering-centre-of-excllence) at **SD Worx**
- [Senior Software Engineer](https://www.wearedevelopers.com/jobs/ext/15942-senior-software-engineer) at **GitHub**
- [Principal Software Engineer, Enterprise AI Platform](https://www.wearedevelopers.com/jobs/ext/1467292-principal-software-engineer-enterprise-ai-platform) at **GitHub**
- [Staff Developer Advocate, GitHub Security Lab](https://www.wearedevelopers.com/jobs/ext/1921051-staff-developer-advocate-github-security-lab) at **GitHub**
- [Principal Software Engineer, Database Infrastructure](https://www.wearedevelopers.com/jobs/ext/1465908-principal-software-engineer-database-infrastructure) at **GitHub**
- [Lead Software Engineer - Data Engineering](https://www.wearedevelopers.com/jobs/ext/2000968-lead-software-engineer-data-engineering) at **Dynatrace**