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Data Transformation and visualization in Einstein analytics

Data Transformation

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:

  • Perl
  • AWK
  • 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.

Visualizing Data

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.

Einstein Analytics offers a key to executives to make “data-driven” decisions, or interact with customers in an insightful way, also backed up by actionable insights from relevant and current data, and with recommended insights. Maybe you always want to have “key metrics” in your hand to stay ahead. Try to Remember or if you discovered this post valuable, Make beyond any doubt, you can look at our Web-based Learning courses for more tips, traps, and methods for successfully to reveal one of a kind bits of knowledge from your information. With a team of trainers who are expedition leaders, real-time professionals, who explained well also our specialists and advisor promises a profound study of the problem.


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