What does Data Transformation mean?
Data transformation is the process of converting data or information from one format to another, usually from the format of a source system into the required format of a new destination system. The usual process involves converting documents
Explains Data Transformation
Data transformation involves the use of a special program that’s able to read the data’s original base language, determine the language into which the Data that must be translated for it to be usable by the new program or system, and then proceeds to transform that data.
Data Transformation involves two key phases:
Data Mapping: The assignment of elements from the source base or system toward the destination to capture all transformations that occur.
Code Generation: The resulting data map specification is used to create an executable program to run on computer systems.
Commonly used transformational languages:
- XSLT: An XML data transformation language
- TXL: A prototyping language mostly used for source code transformation
Template Languages and Processors: These specialize in data-to-document transformation
Data transformation best practices:
- Start with the End in Mind: Design the Target
- Speed Date your Data with Data Profiling
- Cleanse: When Your Data Needs a Bath
- Confirm Data to the Target Format
- Build Dimensions Then Facts
- Record Audit and Data Quality Events
- Continually Engage the User Community
Normal Distribution and Skewness in Data
One of the most frequently-encountered assumptions of statistical tests is that data should be Normally distributed. You may have heard of the normal distribution referred to as a “bell curve” before; this is because a normal distribution takes the shape of a bell, with the data spread around a central value.
Skewed data tend to have more observations either to left side or to the right side. Right skewed data have a long tail that extends to right whereas left skewed data will have a long tail extending to the left of the mean value. When data are very skewed, it can be hard to see the extreme values in a visualization
Understanding Transformations Using Sample Data
How transformations work on actual data. The first step in transformation is to evaluate the distribution of the data. Then you can decide what transformation is appropriate.
The histogram above shows that the distribution of population values is right-skewed.
You finally have everything collected, prepped, and cleaned. Now it’s time to cook! When you’re combining ingredients, some will stand out in your dish, some will play a supporting role, and some will seemingly fade away. Despite these differences, you still need to make sure each ingredient is incorporated properly in order for the dish to succeed as a whole.
How to present the dish in terms of emphasis, functionality, and appropriateness; similarly, deciding which graph types and presentations to use depends on the data behind your visualization and the narrative it supports. We’ll talk about crafting visualizations that help you best tell the story of your data. We’ll cover tips on choosing what to visualize, deciding which graph types make sense, and giving the right finishing touches to a beautiful and accurate presentation.
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