Your organization as a graph

by Dunya Kirkali

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Dunya Kirkali Your organization as a Graph The hidden network of work behind every org chart

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Org Chart

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Team Chart

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Real Chart

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🌍 • Team Leader at • Co-author of the • Engineering Manager’s Compass • Blogger on • blog.incrementalforgetting.tech • Graphs ❤ Dünya Kırkalı

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🤔 Why

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Why graphs Isn’t everything a graph? • Relational Data • Relationships are as important as entities • Combine datasets • It let’s you ask interesting questions

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Why now Just because Dünya likes graphs? • Layo ff s • Reorgs • Hiring freezes • E ffi ciency programs • Platform consolidation • AI-era productivity pressure

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Gut feeling 🫀

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Can we 🧠

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⭐ Northstar Systems

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⭐ Northstar Systems • CEO • 1 Chief Product O ffi cer • 1 Chief Technology O ffi cer • 4 teams • Core Work fl ows • Growth & Mobile • Platform Experience • Executive Leadership

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⭐ Northstar Systems • 4 services • rider-service • regions-service • car-service • payments-service

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Boring 💤

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🪜 Augmentations

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Communication

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Communication • Chat • Slack • Teams • E-mail • Survey • Meetings Input

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Communication w/o

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Communication w/

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Communication w/

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Communication • Some people act as bridges • Coupling between areas • Too little communication Takeaways

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“The org chart shows accountability. Communication shows execution” Communication Quote

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Ownership

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Ownership Input • Services • Backstage by Spotify • SERVICEOWNERS by GitHub • In-House • CODEOWNERS • GitHub or GitLab

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Ownership w/o

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Ownership w

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Ownership • Some boundaries are unclear • Not every team has balanced amount of ownables • Some teams depend heavily on others Takeaways

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“Things that change together should be closer together” Quote Ownership

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Pull Requests

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Pull Requests Input • GitHub • GitLab • Bitbucket • Etc.

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Pull Requests w/o

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Pull Requests w/

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Pull Requests Takeaways • Incorrect ownership • Technical bridges • Domain kings

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“The codebase remembers how the organization actually works” Quote Pull Requests

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Other

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Other • Trust • “Who do you go to when you need help” • “Who do you think I should talk to” • “Who would hurt the most if they leave” • Documentation • Notion • Con fl uence • Work Items • Jira • Linear

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🧮 Techniques

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Leiden Community Detection • Finds densely connected clusters • Allows you to take a step back • Breaking down monoliths • Designing teams

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Leiden Community Detection

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Betweenness • Finds nodes that sit on many shortest paths between other nodes • Identi fi es bridges • Find bottlenecks • Coordination chokepoints

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Betweenness • Lena Martinez • Marcus Chen • Fatima Ibrahim • Nina Okafor • Matteo Rossi • Jules Bennett • Zara Ali 35.48 35.01 28.40 27.79 25.30 23.20 20.81

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Betweenness

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PageRank • Rank in fl uence based on incoming connections • Identify the most in fl uential people, services, or teams in the graph • Highlight points that require more care

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PageRank • regions-service • Lena Martinez • car-service • payments-service • Elena Petrova • Growth & Mobile • Marcus Chen • rider-service 1.205 0.660 0.658 0.646 0.615 0.598 0.590 0.584

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PageRank

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LLMs What would happen if we loose Developer X?

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LLMs Which team is becoming the organization bottleneck?

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LLMs Who should work together on a new strategic project?

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🏭 Pipeline

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Pipeline 1. Make your hypothesis

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Pipeline 1. Make your hypothesis 2. Collect data

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Pipeline 1. Make your hypothesis 2. Collect data • Script • Run daily • Save as CSV

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Pipeline 1. Make your hypothesis 2. Collect data • Ingestion pipelines • Data Lake

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Pipeline 1. Make your hypothesis 2. Collect data 3. Convert them into Cypher queries and import into Neo4j

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Pipeline 1. Make your hypothesis 2. Collect data 3. Convert them into Cypher queries and import into Neo4j • Each dataset in it’s own DB • Then create a combined DB where you associate all data • Install the Neo4J CLI

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Pipeline 1. Make your hypothesis 2. Collect data 3. Convert them into Cypher queries and import into Neo4j 4. Use your favourite agent harness

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Pipeline 1. Make your hypothesis 2. Collect data 3. Convert them into Cypher queries and import into Neo4j 4. Use your favourite agent harness 5. Correlate with other data sources

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Pipeline 1. Make your hypothesis 2. Collect data 3. Convert them into Cypher queries and import into Neo4j 4. Use your favourite agent harness 5. Correlate with other data sources 6. Experiment

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Pipeline 1. Make your hypothesis 2. Collect data 3. Convert them into Cypher queries and import into Neo4j 4. Use your favourite agent harness 5. Correlate with other data sources 6. Experiment 7. Repeat

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📓 Closing

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Considerations • Ethics • Con fi dentiality • Anonymity • Expectations • It’s not a 💊, it’s a 🔍 • You always need to interpret the data • Leverage LLMs to pose your questions

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Acknowledgements • 📖 Hidden power of social networks  by Andrew Parker and Rob Cross • 📖 Linked  by Albert-László Barabási • 🎤 Engineering Leadership through a Social Network Lens  by Gabriel Ramirez • 🫶 Support  by Maxim Schepelin

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Resources • SERVICEOWNERS by GitHub • CodeScene • Backstage by Spotify • Understanding the Leiden Algorithm • Pi Coding Agent • Neo4j