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Organizational Analytics Course Description

Title: Organizational Analytics with AI

Audience

  1. Information systems professionals learning about managing human resource data with AI
  2. Human resource professionals learning about advanced analytics and AI
  3. Enterprise architects that are interested in how graph database and AI work together to find deep insights in organizations

Overview

In the past, human resources information systems were all about traditional tasks such as tracking the organizational chart, doing payroll, tracking performance reviews and answering questions about employee benefits.
Today, most of this work can be cost-effectively outsourced.
However, there is a gold mine of untapped data about your staff stored in everyday tools like your email system, you internal chat history and the event logs that monitor desktop activity. Companies almost never tap into this hidden resource.
This course is all about mining that treasure and turning it into valuable insights such as:

  1. Recognition - what hidden events should be recognized by leadership
  2. Alignment - which tasks created by various teams aligned with the organizational strategy
  3. Ideation - how ideas are generated, refined, and recombined across many contributors
  4. Influence - reveals who shapes decisions, regardless of formal authority
  5. Efficiency - reflects how quickly and directly information flows to accomplish work
  6. Innovation - highlights boundary-spanning interactions where novel ideas emerge by connecting otherwise separate groups
  7. Mentoring - how do you match junior employees with senior employees that can help their careers
  8. Placement - how to find the perfect person in your organization for a demanding task
  9. Sentiment - how can management get an overall sense of the changing attitudes of staff
  10. Silos - reveals organizational fragmentation where communication remains trapped within groups
  11. Training Gaps - when are people in roles that they don't have sufficient training or background
  12. Vulnerability - exposes single points of failure where the organization depends heavily on one individual

This course is about using state-of-the art AI, natural language processing (NLP), and large-language models (LLMs) to help teams get a real-time sense of what is going on in their organization. At the heart of this is a graph database that can efficiently store the complex relationship-rich data that surrounds people.
Through efficient graph algorithms we find that some of the most complex problems can be quickly solved.

One of the greatest struggles people have in HR today is the limits of their old relational database. After taking this course, HR staff will be freed of many of the constraints of the past and be able to answer difficult questions they only dreamed of answering before.

Questions a Traditional Relational HRIS Cannot Answer

Retention & Flight Risk

  • Who is quietly disengaging? (declining communication frequency, shrinking network, withdrawal from cross-team interactions — all invisible in a relational system)
  • When a high performer resigns, who else is likely to follow? (relational DBs don't model influence contagion)

Hidden Leadership

  • Who do people actually go to for answers, regardless of title? (the org chart is a lie — the communication graph tells the truth)
  • Who are the informal bridge-builders connecting otherwise siloed teams?

Succession & Knowledge Risk

  • If this director leaves tomorrow, which projects stall? Which relationships break? (relational systems know the reporting line but not the knowledge flow)
  • Where is institutional knowledge concentrated in a single person with no backup?

Onboarding & Integration

  • Is a new hire actually building a communication network, or are they isolated 90 days in? (traditional systems only know whether they completed orientation checklists)
  • After a merger, are the two legacy teams actually collaborating or just coexisting?

Reorganization Impact

  • If we restructure department X, which communication pathways break? (relational DBs can move boxes on a chart but can't predict what connections are severed)
  • Which teams that should be talking based on shared goals are not?

Real-time Culture

  • Is sentiment shifting in engineering this month compared to last? (annual engagement surveys are 11 months stale)
  • Are certain managers creating communication bottlenecks that frustrate their teams?

Inclusion Beyond Headcount

  • Are diverse employees central to decision-making networks or peripheral? (demographics in a relational DB tell you who you hired; the graph tells you whether they're included)

Optimal Matching

  • A critical project needs someone who understands both the finance domain and the new API platform — who in the organization has that intersection of experience? (relational systems search by job title; graphs search by demonstrated knowledge flow)

Why Relational Databases Fail Here

The fundamental problem is that relational databases store entities and attributes — employee name, title, department, salary. But the questions above are all about relationships, paths, and patterns across networks. A query like "find the shortest communication path between the CFO and the product team" requires recursive self-joins in SQL that are both painful to write and catastrophically slow at scale. In a graph database, it's a one-line traversal.

Topics Covered

Employee Event Streams Ethics of Privacy Business Process Mining Event Logs Universal Timestamps Summarizing Events Graph Databases Graph Database Performance at Scale Graph Data Models Nodes Edges Node Properties Edge Properties Modeling Employees Employee Attributes Modeling Organizations Organization Attributes Modeling Communication Models Activity Email Chats Devices Desktops Licenses Mobile Phones Software Applications Positions Projects Roles and Titles Onboarding Staff Task Assignments Loading Events to Graph Latency Staging Areas Graph Algorithms Graph Metrics Degree Indegree Outdegree Pathfinding Clustering Community Detection Labeling Communities Similarity Similar People Similar Roles Similar Events Natural Language Processing Sentiment Analysis Machine Learning Graph Machine Learning Building a Graph Library Summarizing Record Retention Security Role-based Access Control Auditing Reporting Developing a Dashboard Operational Reports Real-time Discovery Looking for Patterns Real-world Applications Career Guidance Detecting AI Events Backlog Task Assignment

Topics Not Covered

This course is not about using employee event streams as a "Big Brother" method of monitoring every mouse click of your staff.
It is about finding true insights that make an organization perform more efficiently.

How AI works in detail Details of machine learning Details of deep neural networks Details of natural language processing Regulatory concerns of HR systems

Bloom Taxonomy of Learning Objectives

After this course, students will:

Remember

  1. Define key graph database concepts including nodes, edges, and properties.
  2. List the types of employee event streams used in organizational analytics such as email, chat, and device logs.
  3. Identify common graph algorithms used in organizational analytics including degree centrality, community detection, and pathfinding.
  4. Recall the ethical considerations and privacy boundaries around mining employee data.
  5. Name the core graph metrics (indegree, outdegree, betweenness, clustering coefficient) and what each measures.

Understand

  1. Explain how graph databases differ from relational databases for storing relationship-rich organizational data.
  2. Describe how employee event streams are captured, timestamped, and staged for loading into a graph.
  3. Summarize how community detection algorithms reveal organizational silos and collaboration patterns.
  4. Explain the role of natural language processing and sentiment analysis in interpreting employee communications.
  5. Distinguish between formal organizational structure and the informal influence networks revealed through communication data.

Apply

  1. Load employee event data into a graph database from email, chat, and device log sources.
  2. Apply graph algorithms such as degree centrality, betweenness centrality, and PageRank to identify influential employees.
  3. Use NLP and sentiment analysis tools to assess communication tone and trends across an organization.
  4. Construct graph queries to explore organizational communication patterns and information flow.
  5. Build staging pipelines that transform raw event logs into graph-ready data with universal timestamps.

Analyze

  1. Analyze communication graphs to detect organizational silos and fragmentation.
  2. Identify single points of failure and vulnerability where the organization depends heavily on one individual.
  3. Compare formal authority structures with informal influence networks derived from communication data.
  4. Examine clustering results to discover and label communities within the organization.
  5. Assess information flow efficiency by analyzing path lengths and bottlenecks in communication graphs.

Evaluate

  1. Evaluate the ethical implications of mining employee event streams and recommend appropriate privacy safeguards.
  2. Assess the accuracy and reliability of graph-based metrics for measuring organizational health.
  3. Critique dashboard designs for their effectiveness in communicating organizational analytics to leadership.
  4. Judge the appropriateness of different graph algorithms for specific organizational questions.
  5. Evaluate record retention policies that balance analytical value with employee privacy.

Create

  1. Design a comprehensive graph data model representing employees, organizations, communications, and activities.
  2. Build an operational dashboard that visualizes real-time organizational metrics and trends.
  3. Develop a reusable graph library of queries and algorithms for organizational analytics.
  4. Create an end-to-end pipeline from raw employee event streams to actionable organizational insights.
  5. Construct similarity models to support mentoring matches and optimal task placement.