Big Data

Big Data

1. Origin of Big Data.

   • Primitive Accounting
     • Data collection and analysis
       • Statistics - Base-decimal accounting method
         • Massive data storage
           • Data centers
             • Development of relational databases
               • computer algorithms
                 • Growth of databases, internet, digitalization.

2. Implications of Big Data

   • Technological
     • New architectures
     • Automation
     • Predictive analysis
     • IA
     • data governance.
   • Economic and business
     • Generate competitive advantage and new sources of income
     • Optimize costs and processes
   • Social and ethical
     • Impacts privacy; risks of algorithmic bias and discrimination.
     • Bridging the digital divide.
     • Service transformation
   • Legal and governance
     • Compliance with data protection regulations
     • Clear access, use, and licensing policies; auditing and accountability
   • Public policies and security
     • Improve analytics for health, mobility, security, and emergency management.
     • Risks of mass surveillance and manipulation/disinformation.

3. Main definitions of Big Data

   • Massive set of structured and unstructured data
     • Storage Architectures : - Data Warehouse - Data Lake
   • Volume: Large quantity Speed: Fast processing Variety: Multiple formats Truthfulness: Quality and reliability Value: Usefulness for decisions

4. Big Data motivations

   • Process optimization and cost reduction.
   • Anticipate trends and behaviors (prediction).
   • Product innovation
   • Evidence-based decision-making
   • Boosting artificial intelligence and machine learning.