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About This Course

Aria the Analytics Ant

Why Organizational Analytics Matters Now More Than Ever

Organizations generate vast amounts of data every day — emails, chat messages, meeting patterns, project assignments, device logs — yet most of this data sits untapped. Traditional HR information systems track org charts, payroll, and performance reviews, but they miss the hidden dynamics that truly drive how work gets done.

The challenge:

  • The org chart tells you who reports to whom — but not who people actually go to for answers
  • Annual engagement surveys are 11 months stale by the time you act on them
  • Relational databases store entities and attributes, but the most important questions are about relationships, paths, and patterns
  • A query like "find the shortest communication path between the CFO and the product team" requires recursive self-joins in SQL that are painful to write and catastrophically slow at scale — in a graph database, it's a one-line traversal

What this course unlocks:

  • Recognition — surface hidden contributions that deserve leadership attention
  • Alignment — see which teams are aligned with organizational strategy
  • Influence — discover who shapes decisions, regardless of formal authority
  • Innovation — find boundary-spanning interactions where novel ideas emerge
  • Vulnerability — expose single points of failure before they become crises
  • Mentoring and Placement — match people to roles and mentors based on actual knowledge flow

This course teaches you to combine graph databases, AI, natural language processing, and graph algorithms to reveal the hidden networks inside any organization.

Aria Says

Did you know that most organizations have no idea how information actually flows through their teams? They have an org chart that says "queen at top, everyone else below" — trust me, I've seen that chart, and it's a fiction. The real story lives in the communication data.

I grew up in a colony of 500,000 ants and discovered that mapping our communication network saved us 40% in lost productivity. If that works for ants, imagine what it can do for your organization.

Let's dig into this together!

Who This Course Is For

This course is designed for three audiences:

  1. Information systems professionals learning to manage human resource data with AI
  2. Human resource professionals exploring advanced analytics and graph-based insights
  3. Enterprise architects interested in how graph databases and AI work together to find deep organizational insights

No prior experience with graph databases is required. If you can think about relationships between people, you can learn organizational analytics.

Learning Through Interactive Visualization

This course takes a hands-on approach to teaching organizational analytics. Instead of only reading about graph algorithms and data pipelines, you will build intuition through interactive MicroSimulations. These browser-based visualizations let you experiment with graphs, networks, centrality metrics, and community detection in real-time.

Watch how removing a single node changes information flow across an organization. See community detection algorithms reveal hidden silos. Explore how different centrality measures identify different kinds of influence. These are not passive animations — they are hands-on laboratories where you control the parameters and discover the concepts yourself.

What Makes This Course Different

Traditional courses on HR analytics focus on spreadsheets and SQL queries against relational databases. This course starts from a fundamentally different premise: organizational data is relationship data, and relationship data belongs in a graph.

By the end of this course, you will be able to:

  • Design graph data models for employees, organizations, and communications
  • Load employee event streams into a graph database
  • Apply centrality, pathfinding, and community detection algorithms
  • Use NLP and sentiment analysis to interpret communication patterns
  • Build dashboards that visualize real-time organizational health
  • Navigate the ethical responsibilities that come with access to communication data

Intelligent Textbook Classification

This is a Level 2.9 intelligent textbook — it emphasizes interactivity through MicroSimulations and a concept-level learning graph, but does not store student records for hyper-personalization. The classification follows the five-level framework described in:

McCreary, D. (2025). A Five-Level Classification Framework for Intelligent Textbooks: Lessons from Autonomous Vehicle Standards. DOI: 10.35542/osf.io/sh2yu_v1. Licensed under CC BY-NC-ND 4.0.

The paper and supporting materials are available at: https://github.com/dmccreary/intelligent-textbooks/tree/main/papers/five-levels

Background

This intelligent textbook was generated using Claude Code Skills in February 2026. We put a strong focus on creating high-quality MicroSims that bring abstract graph concepts to life and on developing Aria the Analytics Ant as a friendly guide who makes organizational analytics approachable and fun.

— Dan McCreary, February 2026

About the Author

Dan McCreary is an AI education researcher specializing in knowledge representation and the use of learning graphs and large language models to create intelligent textbooks.

Dan holds a B.A. in Physics from Carleton College and an M.S.E.E. from the University of Minnesota. He has also completed 30 of the 33 credits required for his MBA at the University of St. Thomas.

His career began at Bell Labs as a VLSI circuit designer, where he collaborated with the original creators of UNIX. At NeXT Computer, he worked alongside Steve Jobs, building a foundation in computing innovation that continues to shape his work today.

Dan's entrepreneurial journey led him to establish a consulting firm that grew to over 75 employees. His career has allowed him to work in many areas such as scale-out enterprise knowledge graphs, high-performance computing, and advanced databases that augment AI capabilities. During his tenure at UnitedHealth Group's Optum division, he played a key role in building the world's largest healthcare knowledge graph — work that directly informs this course's approach to modeling organizational relationships and communication patterns.

He is the co-author of Making Sense of NoSQL and a frequent contributor to articles helping education leaders understand the strategic implications of accelerating AI technologies. An avid blogger on AI strategy, Dan remains at the forefront of knowledge graphs and generative AI's evolutionary path.

Dan believes that AI technologies will make high-quality education accessible to everyone on the planet. This free, open-source textbook — with its learning graph of interconnected concepts and interactive MicroSims — represents his commitment to that vision.

How to Cite This Book

If you use this textbook in your teaching, research, or coursework, please cite it using one of the following formats:

APA (7th Edition)

McCreary, D. (2026). Organizational Analytics with AI: An interactive intelligent textbook. https://dmccreary.github.io/organizational-analytics/

MLA (9th Edition)

McCreary, Dan. Organizational Analytics with AI: An Interactive Intelligent Textbook. 2026, dmccreary.github.io/organizational-analytics/.

Chicago (17th Edition)

McCreary, Dan. Organizational Analytics with AI: An Interactive Intelligent Textbook. 2026. https://dmccreary.github.io/organizational-analytics/.

BibTeX

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@book{mccreary2026organalytics,
  author    = {McCreary, Dan},
  title     = {Organizational Analytics with AI: An Interactive Intelligent Textbook},
  year      = {2026},
  publisher = {Self-published},
  url       = {https://dmccreary.github.io/organizational-analytics/},
  note      = {Open-source textbook with interactive MicroSimulations}
}