Data Preparation and Processing

Data Processing Pipeline 🔄

Overview of Data Flow 📊

graph TD
    A[Raw Data] --> B[Extraction]
    B --> C[Cleaning]
    C --> D[Standardization]
    D --> E[Validation]
    E --> F[Loading]
    style A fill:#f9f,stroke:#333
    style F fill:#bbf,stroke:#333

Data Extraction Methods 🔍

Automated Extraction Process

Method DataType Reliability
Excel Direct Read Structured Tables High
Pattern Matching Semi-structured Medium
Custom Parsers Complex Formats High

Format Standardization 📋

Standardization Rules 📏

  1. Date Formats 📅
    • ISO 8601 compliance
    • Timezone handling
    • Historical data conversion
  2. Numerical Values 🔢
    • Decimal standardization
    • Unit conversion
    • Range validation
  3. Text Fields 📝
    • Character encoding
    • Case normalization
    • Whitespace handling

Quality Control Checks ✅

Validation Framework

validation_rules <- tibble::tribble(
  ~Rule, ~Description, ~Severity,
  "Completeness", "Required fields present", "High",
  "Format", "Data format compliance", "High",
  "Range", "Values within bounds", "Medium",
  "Consistency", "Cross-field validation", "High"
)

validation_rules |> 
  knitr::kable()
Rule Description Severity
Completeness Required fields present High
Format Data format compliance High
Range Values within bounds Medium
Consistency Cross-field validation High

➡️ Next: System Architecture