# Data Abstraction

This section describes the role of data in information visualization.

**Data** is factual information (such as measurements or statistics) that is used as a basis for reasoning, discussion, or calculation.

Raw data may not be directly useful while creating visualizations; we might need to **transform** the data first to be able to use it to create a visualization.

The image below shows a modified pipeline, including the **data transformation** task. It shows how data is first **collected**, then **transformed** and finally **encoded** to create a visualization.

![](https://612062878-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RG_uqhxyMNjpMUW%2F-M5-0SvQ45Rarxg8L2lA%2F-M5-0VrQQ4oSFB_9SBHH%2FData%20Collection%20Transformation%20and%20Encoding%20for%20Visualization.PNG?generation=1586990828372748\&alt=media)**Data Abstraction** refers to describing the data in a way that is meaningful and useful to visualization design. It allows us to decide what operations and encoding methods are *available* and *appropriate*.

It is called data '*abstraction*' because it provides a way to recognize *common structures* in data coming from very *different domains*.

For example:

* **Network** visualizations can be used for representing Facebook friendships, interactions between proteins, connections between criminals etc.
* **Spatial distributions** can be used to visualize animal movements, election results by region, simulation of airflow in an aircraft etc.

*In other words, we can define data abstraction as the process of abstracting away from the domain the characteristics of the data that are useful to decide what visual representations are available and appropriate.*


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