Data Transformation Stage
The Data Transformation Stage in the Data Analytics Life Cycle focuses on converting processed data into forms that enhance analytical clarity and improve model performance. This stage applies structured methods to scale, consolidate, and optimize datasets so that they become both comparable and computationally efficient for the subsequent modeling phase.
Aggregation
Aggregation combines multiple data points or datasets into a unified or summarized form, allowing analysts to work with consolidated results rather than raw granular records.
• This approach can involve summing numerical values, calculating averages, or merging records across time periods or categories.
• Aggregation is particularly useful for high-volume datasets where working with individual entries is impractical, as it reduces complexity and highlights overarching trends.
Normalization
Normalization scales data to a regularized range, ensuring that variables with different units or magnitudes can be compared accurately.
• For example, values in the range of 1–1,000 can be normalized to fall between 0 and 1 without altering their relative relationships.
• This step prevents bias in algorithms sensitive to variable scale, such as clustering or distance-based methods, and ensures consistent weightings in model calculations.
Feature Selection
Feature selection is the process of identifying and retaining the most relevant variables for analysis while discarding those with little or no impact on the target outcomes.
• This improves computational efficiency and model interpretability.
• Selection may be driven by statistical measures (e.g., correlation, chi-square tests) or domain expertise evaluating variables’ business significance.
Discretization
Discretization pools continuous data into smaller intervals or categorical groups.
• Large ranges of numerical data can be divided into bins or segments—for instance, transforming customer ages into age groups.
• This is often used for simplifying patterns in data, making certain types of modeling (like decision trees) more effective.
• Discretization balances simplification with retention of important data variability.
In essence, data transformation is an optimization phase—ensuring datasets are comparable, reduced in complexity, and focused on what matters most. It bridges the gap between data cleaning and advanced analytics, enabling models to operate on structured, high-quality, and business-relevant information.