Learning Objectives

By the end of this lesson, learners should be able to:

  • Explain data transformation.
  • Perform common transformation operations.
  • Explain data integration.
  • Identify integration challenges.
  • Prepare integrated datasets for analysis.

Meaning Of Data Transformation

Data transformation converts data into a format suitable for analysis.

Common Transformation Operations

  • Sorting,
  • Filtering,
  • Aggregation,
  • Creating derived variables,
  • Normalization,
  • Encoding categorical values.

Example

Profit = Revenue − Cost.

Data Integration

Data integration combines information from multiple sources into a unified dataset.

Example

Combine customer data, sales data, and marketing campaign data from branches in New York, London, Dubai, and Singapore to evaluate campaign performance globally.

Integration Challenges

  • Different customer identifiers,
  • Different currency codes,
  • Different date formats,
  • Missing matching records,
  • Duplicate records,
  • Conflicting values.

ETL Process

  1. Extract data from source systems.
  2. Transform data into the required format.
  3. Load data into the target database or warehouse.

ETL is a standard business intelligence process used by international organizations.

Business Example

A global e-commerce company integrates online sales, mobile app purchases, and physical store transactions to create a unified customer view and identify omnichannel purchasing behavior.

Learning Materials / Reference Materials

  • Kimball & Ross. The Data Warehouse Toolkit.
  • Microsoft Power Query Documentation.
  • Tableau Data Preparation Resources.

Lesson Summary

Data transformation and integration combine and standardize data from multiple sources, creating a unified dataset suitable for accurate business analysis and reporting.

PreviousNext