Data Integration Process

Data Integration Process

• Data integration is the process of combining data from various sources
• It involves data sour identification, data extraction, data mapping, data validation, and data quality assurance
• Techniques used in data integration include Extract, Transform, Load (ETL) and Extract, Load, Transform

Data Transformation Process

• Data transformation is the process of transforming data into another format suitable for analysis
• It involves data transformation, data loading, and data synchronization
• Techniques used in data transformation include data discovery, data mapping, code generation, and execution

Importance of Data Integration

• Improved decision-making
• Compliance with regulations
• Enhanced employee insights
• Streamlined processes

Data Integration Techniques

• Normalization – adjusting data value
• Standardization – scaling data
• Encoding – converting from categorical to numerical
• Aggregation – combining multiple data points
• Filtering – removing redundant or irrelevant data
• Imputation – filling in missing values

Types of Data Integration

• Inner Join – matching values
• Left Join – left values and matching values from the right
• Right Join – right values and matching values from the left
• Outer Join – the union of all values

Data Transformation Techniques

• Normalization – scaling specific variable falls to normal
• Inferential statistics – drawing conclusions
• Sampling – law of large numbers and central limit theorem
• Data profiling – understanding the characteristics and quality of data
• Clear documentation – ensuring repeatability and quality
• Automation – streamlining and standardizing processes

Data Pre-processing

• Data pre-processing is improving the quality of data for secondary analysis
• Data cleaning – addressing noise in data to ensure accuracy and correctness
• Importance of data pre-processing:
  • Ensures data accuracy and reliability
  • Improves data quality
  • Reduces errors and bias in analysis
  • Supports effective decision-making

Data Quality Issues

• Missing values – incomplete
• Noise and outliers – data that deviate
• Inconsistencies – error, formatting
• Duplicate data – repeated values

Data Cleaning Process

• Handling missing values – imputing data
• Smoothing noisy data – eliminating outliers
• Detecting and deleting outliers – use box plot
• Fixing structural errors – all error in words
• Removing duplicates – avoiding redundancy
• Data validation – authenticating data

Data Transformation Techniques

• Data smoothing – helps predicting trends or seasonality
• Min-Max normalization – transforming into a standardized format
• Z-Score standardization – transforming into a standard normal distribution
• Binning – transforming numerical to categorical
• Feature creation – creating new attributes that capture important information
• Feature extraction – creating new features
• Mapping data or new space – lower-dimensional space to higher-dimensional space
• Feature construction – built intermediate features

This quiz covers the data integration process, including data source identification, data extraction, data mapping, and data aggregation. It also touches on data generalization, attribution construction, and data discretization.