Business Analytics

Business Analytics

1. is structured in

   • The nature of the data "V"
     • Variety
       • Diversity of formats (structured, semi-structured and unstructured such as text or video).
     • Velocity
       • Speed ​​at which they are generated and must be processed.
     • Volume
       • Magnitude of the data collected.
     • Veracity
       • Quality and reliability of information.
     • Value
       • The ultimate benefit to the business (the "why" we do it).
   • Statistical Methods
     • - Data mining
     • Machine Learning
   • The Analysis Cycle (Advanced Techniques)
     • models for transforming data into knowledge:
       • Descriptive Analysis: What happened? (Visualization, basic reports).
       • Predictive Analysis: What might happen? (Machine Learning, statistical models).
       • Prescriptive Analysis: What should we do? (Optimization, process automation)

2. Infrastructure and Tools

   • Storage: NoSQL and distributed file systems.
   • Processing: Hadoop and Apache Spark ecosystems.
   • Languages: Python and R are used for large-scale manipulation.

3. Applications and Results

   • organizational impact
     • Intelligent decision-making: Replacing intuition with data-driven evidence.
     • Operational efficiency: Process improvement and risk reduction.
     • New opportunities: Identifying hidden patterns and market trends.