Data and Competitive Advantage: Databases, Analytics, and Prepping Data for Use with AI

• Introduction to Data and Competitive Advantage

  • 90% of organizational data created in last 2 years; 2.5 quintillion bytes produced daily.
  • Big Data: Large, complex, unstructured datasets analyzed for organizational insights.
  • Decision-making driven by standardized corporate data and third-party datasets.

• Key Terms

  • Business Intelligence (BI): Reporting, data exploration, and analysis blend.
  • Analytics: Use of data and models for informed decision-making.
  • Machine Learning (ML): AI that improves its accuracy using data without additional programming.

• Competitive Advantage through Data

  • Companies like Amazon and Netflix leverage data for better products and decision-making.
  • Early data capture can distinguish market leaders from laggards.

• Dynamic Pricing in Entertainment and Sports

  • Disney used dynamic pricing successfully for "The Lion King".
  • Similar strategies apply in sports ticketing and services like Uber.
  • Tricky aspects of dynamic pricing: Customer perception, external alternatives, etc.

• Data, Information, and Knowledge

  • Data: Raw facts.
  • Information: Contextualized data for decision support.
  • Knowledge: Insights from experience and data.

• Data Organization Technologies

  • Database: Structured data storage.
  • DBMS: Software for managing databases.
  • SQL: Language for database manipulation.

• Transaction Processing Systems (TPS)

  • Record transactions (e.g., sales, withdrawals).
  • Enhance data collection through loyalty programs.

• Data Warehousing

  • Data Warehouse: Collection of databases for organization-wide decision-making.
  • Data Lake: Storage for structured and raw data; allows data exploration.

• Query and Reporting Tools

  • Tools for data interrogation and reporting (e.g., Python, R).
  • Canned reports vs. Ad hoc reports; Dashboards for visual KPIs.

• Data Mining

  • Identifying patterns in large datasets.
  • Areas: customer segmentation, fraud detection, etc.
  • Requires clean, consistent data and a skilled analytics team.

• Implementing Data Projects

  • Focus on relevance, quality, governance, and ETL processes.

• Emerging Technologies: Blockchain

  • Secure digital ledger with decentralized transactions.
  • Characteristics: Tamper-proof, cryptographic integrity.
  • Mining process: Validation via Proof of Work for new transactions.

Note: This summary captures critical concepts from various sections of the transcript. For deeper understanding, reference specific terms and frameworks as needed during exam preparation.