All Eyez On Data

Visual Analytics

Visual analytics is the process of making visual intelligence and analysis methods work together. They use interactive technology to get relevant information about data.

Why do we need visual analytics?

The modern world is full of data. And to get through all of it, we have to use a navigator. We need to know where we should begin. This is where visual analytics comes into play.It guides us through the never-ending sea of data.

Data visualization and Visual Analytics

Visual analytics coexists with data visualization. It can help your business grow in unimaginable ways.

Data visualization comes in the form of graphs, charts, and reports. For example, it can show what are sales and profits in different regions and of different courses of time. This is a visual way to identify data problems and anomalies.

Visual analytics, on the other hand, helps you find answers and solutions and to tell stories. Once you have your visual analytics you can change what kind of information you are looking at. Not only that but you can change the way of how you see the presented data as well.

We process visual information faster than text or statistical data.

Visual analytics applications

Visual analytics has a broad impact on everyday life and situations. It can be used for many things. For example, it can be used to:

• To analyze Business Intelligence

• To support commercial contexts

• To view emergency alerts

• To monitor health and epidemic supervisions

• To analyze and visualize urban planning and other engineering systems

How does Visual Analytics work?

Principles and processes

For starters, visual analytics incorporates several tools into one another.
That process uses both visual and automatic analysis techniques that use targeted human interaction to gain some kind of information or knowledge.

When data analytics is applied in visual analysis presentations, there first has to be data coming from the same origins.

This has to be the case before some automatic and visual methods are applied.
Hence it is important to have a pre-process before the start of the actual process. The data has to be converted to be ready for use.

This is not the only pre-process that can happen. For example, the data has to be cleaned, normalized, grouped or integrated into other data sources.

After the data has been transformed it is time for the next step. The job of the analyst is now to choose which method will be used.

When the model of the original data is created, the human analyst has to improve the same model. To do that best he has to interact with the data.

Then the visualizations come into play. This allows the analyst to work with the used method. They allow them to modify the parameters and to select alternative analysis algorithms.

Then a model visualization is used to analyze those generated models. Visual analytics allows us to move between visual and automatic methods. This process can lead us to select and verify the results.

If there are any misleading rest results, they can be discovered fairly easily and fast. The fast selection of false outcomes gives better confidence in the site.

On the other hand, if visualization analysis is done first the analyst has to run the data again but this time by using an automated method.

What is SAS Visual Analytics?

SAS is the newest technology on the rise. It is a web-based product. It is easy to use and drives high website performances. This is a plugin that allows huge organizations to control and monitor big data. It gives them the ability to quickly identify patterns, trends, and opportunities for further analysis.
SAS is used to make data ready to be explored and mined. The users of this program can create dashboards and reports on huge amounts of data. It is a flexible tool that both web and mobile users can utilize.

In conclusion, data analytics visualization mainly focuses on analytical reasoning techniques. These techniques help users have better knowledge about their decisions and decision-making processes. How we visually represent data and what kind of techniques we use is a key step to data transformation.