Concept Taxonomy for Organizational Analytics with AI

This taxonomy organizes the 200 concepts into 12 categories for color-coded visualization in the learning graph.

Categories

1. Foundation Concepts (FOUND)

Foundational prerequisites and introductory concepts that establish the context for the course. Includes traditional HR systems, database paradigms, and the motivation for graph-based analytics.

2. Graph Modeling (GMOD)

Core graph database modeling concepts including nodes, edges, properties, schema design, and query languages. These are the building blocks for representing organizational data as a graph.

3. Graph Performance (GPERF)

Concepts related to graph database performance, scalability, and indexing. Covers the operational considerations for running graph analytics at enterprise scale.

4. Event Streams (EVENT)

Employee event stream concepts including event logs, timestamps, normalization, and the various sources of organizational data (email, chat, devices, calendar, business processes).

5. Data Pipelines (DPIPE)

Data engineering concepts for moving event data into graph databases. Covers ETL, staging, batch vs. stream processing, data quality, and latency management.

6. Organizational Modeling (OMOD)

Concepts for modeling the organizational domain: employees, departments, positions, communication patterns, projects, and task assignments within the graph.

7. Ethics and Privacy (ETHIC)

Ethical considerations, privacy frameworks, consent, anonymization, bias, and transparency. Sets the boundaries for responsible use of employee analytics.

8. Security (SECUR)

Technical security concepts including access control, encryption, audit trails, record retention, and data minimization.

9. Graph Algorithms (GALG)

The algorithmic core of the course: centrality measures, pathfinding, community detection, similarity, and network metrics used to extract insights from organizational graphs.

10. NLP and Machine Learning (NLPML)

Natural language processing and machine learning concepts applied to organizational data. Includes sentiment analysis, topic modeling, LLMs, graph neural networks, and embeddings.

11. Organizational Insights (INSGT)

The analytical insights derived from graph and NLP techniques: influence detection, silo detection, vulnerability analysis, flight risk, retention, and information flow analysis.

12. Applied HR Analytics (APPHR)

Applied HR use cases that combine multiple techniques: mentoring, placement, career guidance, onboarding effectiveness, merger integration, and inclusion analytics.

13. Reporting and Dashboards (RPTDASH)

Concepts for presenting insights: reporting, dashboards, visualization, real-time discovery, pattern and anomaly detection, and alerting.

14. Capstone and Integration (CAPST)

Capstone-level concepts that integrate multiple skills: building graph libraries, end-to-end pipelines, organizational health scores, AI event detection, and continuous improvement.