What are common Data Transformation Techniques?
So far, we have understood what is data transformation and the common raw data generated by an internet business that needs transforming.
Now, let's understand some key data transformation techniques and their use cases in context of internet businesses.
Grouping and Aggregating Data:
- → Definition: Combining multiple data points into summarized groups, often to see broader trends or patterns.
- → Example: Grouping website visitors by demographic characteristics and aggregating their spending to identify which groups contribute most to sales.
Filtering and Sorting Data:
- → Definition: Selecting specific data based on certain criteria and arranging them in a meaningful order.
- → Example: Filtering customer reviews to show only those related to a specific product category and sorting them by date to track changes in customer feedback over time.
Normalization & Scaling:
- → Definition: Adjusting numerical data to a common scale, often to compare different datasets on equal footing or prepare data for machine learning algorithms.
- → Example: Scaling click-through rates from different advertising campaigns to a range of 0-1 for fair comparison across platforms.
Joining and Merging Data:
- → Definition: Combining data from different sources or datasets to create a comprehensive view.
- → Example: Merging sales data from an online store with inventory data to analyze sales patterns and inventory requirements simultaneously.
Takeaway:
These data transformation techniques are integral to converting raw data into insights that can drive decision-making in internet businesses.
In upcoming sections of this skilleton, we will go deeper into the 4 key data transformation techniques mentioned above.