Data Fundamentals for AI Systems | 2.0 | AI GOVERNANCE COURSE

AI Governance Course | Lesson 2: Data Fundamentals & Managing Data in AI Systems
Welcome to Lesson 2 of our comprehensive AI Governance course! Unlock the secrets of AI by mastering its most critical component: data. In this lesson, we dive deep into the data lifecycle, from collection to deletion, and reveal how to manage it for safe, reliable, and compliant AI systems.
Data is the fuel for AI, but poor-quality data leads to biased and unreliable systems. This lesson gives you the foundational knowledge to govern that fuel responsibly.
📚 What You’ll Learn in This Video:
The Three Types of AI Data: A clear breakdown of Structured, Unstructured, and Semi-Structured data.
Labeled vs. Unlabeled Data: Why this distinction is the key to machine learning and a major governance challenge.
Features & Labels: Understanding the fundamental building blocks of any AI model.
The 6 Stages of the Data Lifecycle: A complete roadmap from collection to secure deletion.
Feature Engineering Explained: The art and science of turning raw data into powerful, model-ready inputs.
Data Quality & Provenance: How to measure and ensure the data you use is trustworthy using key metrics.
A Practical Data Risk Map: Identifying vulnerabilities at every stage of the data journey.
👥 About the Creators
This course is developed by the team at EthicaLogic — a consulting firm specializing in AI governance, privacy compliance, and responsible technology deployment - ethicalogic.com
It is led by Viacheslav Gasiunas, a legal and governance expert with over 15 years of experience in data protection, AI policy, and ethical risk management.
This lesson is essential for:
AI Governance, Risk, and Compliance (GRC) Professionals needing to manage data-related risks.
Data Scientists & ML Engineers looking to build more robust and ethical models.
Product Managers & Business Leaders responsible for AI-driven products.
Anyone preparing for CIPP, CIPT, or other AI governance certifications.
➡️ If AI systems are buildings, this lesson is about ensuring the foundation is solid.
🧠 Bonus: End-of-Lesson Knowledge Check Quiz
Test what you’ve learned and get instant feedback on core data concepts!

🕒 Chapters & Timestamps

00:00 — Introduction: Data as the Fuel for AI
00:50 — What is Data in the Context of AI?
01:59 — Classification by Structure (Structured, Unstructured, Semi-Structured)
03:20 — Classification by Label (Labeled vs. Unlabeled Data)
04:21 — Key Terms: Features vs. Labels
05:39 — The 6-Stage Data Lifecycle: From Collection to Deletion
06:55 — Feature Engineering: The Heart of Data Preparation
10:27 — Data Governance: Data Quality & Provenance
12:48 — The Basics of Data Provenance & Lineage
14:26 — A Data Risk Map for AI Governance
15:23 — Takeaways: Data as the Foundation of Responsible AI
16:13 — Bonus Quiz: Test Your Knowledge

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