Business Analytics Chapter 1: Fundamentals of Prescriptive Analytics

Introduction to Prescriptive Analytics

• Definition: Prescriptive analytics combines data, mathematical sciences, and business rules to predict outcomes and suggest actions.
• Importance: Helps organizations create a competitive edge by leveraging big data for strategic decisions.

Overview of Business Analytics

• Business Analytics Process: Using statistical methods and technologies to analyze data for strategic decision-making.
• Three Types of Analytics:
  • Descriptive Analytics:
    • Analyzes past performance to understand reasons behind successes or failures.
    • Utilizes simple techniques like bar and pie charts for easy comprehension.
    • Examples: Annual revenue reports, social media analytics.
  • Predictive Analytics:
    • Forecasts future outcomes using historical data, rules, and algorithms.
    • Techniques include statistical modeling and machine learning.
    • Applications: Fraud prevention, credit scoring, marketing optimization.
  • Prescriptive Analytics:
    • Suggests actions based on predictions.
    • Uses algorithms to measure repercussions of decisions and recommend courses of action.
    • Benefits include time and cost savings while optimizing results.

Nature of Prescriptive Analytics

• Process:
  1. Anticipates future events and their implications.
  2. Suggests decision options with projected outcomes.
  3. Continuously updates predictions and recommendations as new data emerges.
• Big Data Role:
  • Involves large, complex datasets from various sources, exceeding traditional data processing capabilities.
  • Utilizes hybrid data and business rules for comprehensive analysis.

Components of Prescriptive Analytics

• Data Collection: Internal, external, structured, semi-structured, and unstructured data.
• Data Processing:
  • Uses models and rules from mathematical sciences and computational techniques.
  • Techniques include machine learning, natural language processing, and signal processing.
• Outputs:
  • Threefold: What and when, why, and how to act.
  • Emphasizes continual re-evaluation due to evolving data and contexts.

Challenges and Considerations

• Resources Needed: Human, computational, and temporal resources for accuracy and reliability.
• Algorithms vs. Human Judgment:
  • Algorithms aid decision-making but cannot replace human context and discernment.

Conclusion

• Value of Prescriptive Analytics: Provides data-driven recommendations for optimal decision-making, supported by machine learning algorithms.
• Management's Role: Essential in providing context and guiding algorithmic outputs to ensure effective implementation.

References

• Acknowledge all sources and references used in the lecture video.