BIG DATA LECTURE


🌍 CLIMATE CHANGE & TECHNOLOGY — EXAM STUDY NOTE

(Slides + Lecture Integration)

1. Threat of Climate Change

Lecture + Slide Integration

  • Anthropogenic climate change = climate change caused by human activity.
    (Your professor emphasized this definition as key.)

  • Scientific consensus: 97%+ of climate scientists agree, IPCC calls warming unequivocal.

  • Main drivers of emissions:

    • Rising populations

    • Higher energy use per capita
      (Especially in emerging economies like China and India.)

  • Biggest emitter today: China

  • Biggest historical cause: USA (due to consumption habits → “they buy what China makes”)

  • CO₂ trends: Rising population (projected 10B by 2050) + rising GDP = higher energy demand and emissions.

Key Risks (Slides + Lecture)

  • Physical harm: hurricanes, floods, heat waves

  • Crop failure, ecosystem collapse, biodiversity loss

  • Human migration, climate refugees, and conflict

  • Business impacts:

    • Property damage (e.g., hurricanes in Florida)

    • Supply chain disruption

    • Insurance losses

    • Firms reconsidering where to locate operations

⭐ Likely Exam Question

“Define anthropogenic climate change and explain why scientific consensus matters.”
“Describe two business risks from climate change.”

2. Stakeholders: Who Must Respond?

Lecture Emphasis

  • Everyone has a role, but government and business have more responsibility because of scale.

  • Shift from shareholder capitalism → stakeholder capitalism.
    Stakeholders include:

    • Employees

    • Customers

    • Communities

    • Suppliers

    • Shareholders

The planet - stakeholder capitalism views the planet as a stakeholder

Shareholder capitalism prioritizes short-run profit maximization for owners, while stakeholder capitalism balances the long-run interests of workers, communities, consumers, and the environment. Climate change makes this shift necessary because it creates systemic risks—physical, regulatory, and reputational—that threaten long-term profitability, meaning firms can no longer externalize environmental costs without damaging their own future operations and shareholder value.

Slides

  • Business Roundtable statement supporting stakeholder value (181 CEOs).

  • Larry Fink (BlackRock) 2020 letter: climate change is reshaping finance. Firms ignoring climate risks will underperform.

Lecture Insight

Sustainability is not optional — investors now price climate risk.

⭐ Likely Exam Question

“Compare shareholder vs. stakeholder capitalism. Why does climate change make this shift necessary?”

3. Sustainability Models

Slides

Old model: Sustainability = intersection of environment, society, economy (3 circles)
New model: Environment as the outer circle, supporting society → economy inside it.
(If the environment collapses, society & economy fail.)

Lecture Example

During COVID: “If the workforce is sick, the economy doesn’t work.”
Environment → Society → Economy are dependent.

4. Options to Respond

(Slides + Lecture)

Firms must:

  • Minimize their own negative impact (decarbonize, reduce waste)

  • Prepare for unavoidable consequences (adaptation, relocation)

Investment trends:

  • Sustainable funds skyrocketing

  • COP29 focuses on climate finance

  • IRA in the U.S. subsidizes clean energy investment

5. Role of Technology in Addressing Climate Change

Slides list three categories:

Obvious technologies:

  • EVs, solar, wind

Less obvious:

  • Nuclear

  • Hydrogen fuels

  • Carbon capture/sequestration

  • Lab-grown meat

Out of left field / speculative:

  • Eating insects

  • Refreezing the Arctic

  • Stratospheric sulfur dioxide injection (geoengineering)

  • Turning poop into power (Shift case)

⭐ Likely Exam Question

“List and explain three categories of climate-related technologies, giving examples from each.”


Obvious technologies: solar power, EV

Less Obvious - lab grown meat, hydrogen fuels

Out of left field - eating insects, turning poop into power

6. Big Data, Machine Learning, and AI

A. What is Big Data?

  • Big data = extremely large, fast-moving, diverse datasets. (5 V’s: Volume, Variety, Velocity, Veracity, Value.)

  • Data alone ≠ information.
    Lecture emphasized: information = interpreted data that becomes meaningful.

Sources:

  • Transactions, viewership patterns

  • Smartphone sensors (GPS, barometer, accelerometer)

  • “Internet of Things”: home cameras, smart appliances, etc.

Prof’s Example

“Businesses even look at cameras inside your home (IoT).”

B. Uses of Big Data

(Slides + Lecture)

  • Predict demand (e.g., Pop-Tarts during emergencies)

  • Identify gaps (missing products on shelves)

  • Turnout analysis (speed up problem resolution from 3 weeks → 20 min)

  • Online vs in-store retail insights

  • Targeted advertising

    • Lecture note: Target identifying a pregnant woman → violated her privacy.

C. Challenges of Big Data

(Expect test questions here — professor emphasized this list.)

  • Capital intensive (costly systems)

  • Too much data → difficult to extract relevant information

  • Recruitment challenges → need specialized talent

  • Training and development of teams

D. CIA Triad (Important Lecture + Slide Overlap)

This is a star exam topic (professor explicitly said so).

CIA =

  1. Confidentiality – data is protected from unauthorized access

  2. Integrity – data is accurate, unaltered, trustworthy

  3. Availability – data can be accessed when needed

E. Security Risks

  • Data breaches

  • Cyberattacks

  • Unauthorized access

  • System failures and downtime

F. Role of the CIO (Huge exam focus — professor said EXAM QUESTION!)

CIO = Chief Information Officer
Main responsibilities (from lecture + slides):

  1. Strategic Planning

  2. Technology Management

  3. Cost Management

  4. Tech Budgeting

  5. Stakeholder Communication

  6. Compliance and Security


S T a C C S
✔ Strategic Planning
✔ Technology Management
✔ a (and)
✔ Cost Management
✔ Communication (Stakeholders)
✔ Security & Compliance


Tech Budgeting

Team Leadership


Lecture example: Uber’s Chief Security Officer went to jail → emphasizes responsibility for compliance.

G. Machine Learning & AI

(Slides + Lecture)

  • Big Data = input for machine learning models.

  • ML allows computers to detect patterns and make predictions.

  • Generative AI (e.g., ChatGPT):

    • Will it inevitably be part of business?

    • Moral implications (bias, privacy, misinformation).

Lecture emphasis

  • Growing field for young professionals.

  • “The veneer will fade” → AI will be part of daily life.

  • Future of AI → self-study, likely reflective discussion question.

7. Ethical & Privacy Issues

(Slides + Lecture)

  • Target predicting pregnancy (violating privacy)

  • Data collection → risk of surveillance

  • Companies using Big Data must respect:

    • Consent

    • Privacy laws

    • Fairness (avoid bias)

    • Transparency in AI use

8. Case Study: Shift (Nonprofit)

Concepts:

  • Turning “poop into power” (out-of-left-field climate tech)

  • Circular economy model

  • Emphasis on innovation to address climate challenges

9. High-Probability Exam Topics

🔥 Expect short-answer or definition questions on:

  • Anthropogenic climate change (definition + causes)

  • Stakeholder vs shareholder capitalism

  • New model of sustainability

  • CIA Triad (guaranteed exam question per lecture)

  • Role of the CIO (also guaranteed)

  • Big Data vs information

  • Challenges of Big Data

  • Examples of tech addressing climate change (obvious, less obvious, out of left field)

🔥 Potential longer discussion questions:

  • “How can technology and government regulation jointly address climate change?”

  • “What are the moral risks of Big Data and AI?”

  • “How does stakeholder capitalism change how firms respond to climate risk?”

  • “Evaluate the benefits and risks of Big Data for consumers and businesses.”