Lecture Notes: From Ancient Oracles to Modern Data

Introduction to Oracles and Prophecies

• Ancient Greece: Decisions made with the help of oracles, e.g., marriage, voyages, military advances.
• Oracles went into trances to provide answers.
• Historical significance: Prophecies used across cultures (Greece, China, Mayans) for decision-making.

Modern Day Oracle: Big Data

• Big Data as the new oracle, including technologies like Watson, deep learning, neural networks.
• Examples of questions asked today: Shipping logistics, genetic disorder probabilities, sales forecasts.
• Big Data industry valued at $122 billion, but returns often low; 73% of projects not profitable.

Challenges with Big Data

• Companies like Palantir losing clients due to lack of results.
• Issues with employees not making better decisions or breakthrough ideas.
• Ethnographic insights: Importance of qualitative data alongside quantitative data.

Case Study: Nokia's Missed Opportunity

• Research with Nokia on low-income consumers in China.
• Insight from qualitative research: Demand for smartphones despite economic constraints.
• Nokia’s reliance on Big Data led to missed market trends.

The Nuance of Big Data

• Big Data's strength in contained systems vs. dynamic systems (e.g., human behavior).
• Importance of understanding the quantification bias: Valuing measurable data over qualitative insights.

The Oracle’s Historical Context

• Oracle of Delphi: Sat over petrochemical fumes causing trance-like states.
• Temple guides’ role in interpreting the oracle’s messages, emphasizing the need for qualitative insights.

Integrating Big Data and Thick Data

• Example: Netflix’s ethnographic study on binge-watching.
• By integrating thick data insights, Netflix transformed their business model and viewing experience.

The Broader Impact

• Potential life or death implications in areas like policing and national security.
• Risks of quantification bias in automated systems affecting health, employment, etc.

Call to Action

• Need to use modern tools effectively by combining Big Data and thick data.
• Importance of enhancing data algorithms and decision-making to avoid missing critical insights.