Data Visualization Basics

This article will provide a top-level overview of data visualization, and is divided into the following sections:

Definition of data visualization.

Purpose of data visualization.

The role of data visualization within the business intelligence stack.

Definition of Data Visualization:

The time period "data visualization" is self-descriptive, in that it literally means the visualization of data. Information is displayed in a clear, graphical manner to a user, who can then assimilate and interpret that information quickly. Of course, how efficient this interpretation occurs relies upon on how well the data has been analyzed and then visualized. 

Purpose of Data Visualization:

The purpose of data visualization is to communicate information in a clear, concise, graphical manner to an intended audience.

Almost all companies deal with a huge amount of uncooked data and making intelligent business selections depends on how well an enterprise analyzes and interprets that data. It is possible to examine statistics in a textual format such as tables and spreadsheets; however, this tends to be overwhelming to the analyst, as well as tough to interpret. Key trends may no longer be identified, resulting in the making of poor commercial enterprise decisions.

This is the place the visualization of data comes to the rescue: large quantities of data can be displayed (via dashboards, scorecards, charts, dials, maps, gauges, graphs, and other visible elements) and almost instantaneously absorbed by means of the user. Key trends can be quickly identified, thereby resulting in intelligent business decisions. 

The old maxim "a picture is worth a thousand words" says it all!

The Business Intelligence Stack and Data Visualization:

Data visualization is actually one component of the "business intelligence stack." Business intelligence refers to technological methods of gathering, manipulating, and then analyzing commercial enterprise data. The "stack" refers to the following components used to accomplish these objectives: 

Presentation Layer:

Consists of various methods used to display data to the end-user.

- Data visualization tools and elements include:

- Performance dashboards.

- Digital scorecards.

- Charts, graphs, and gauges.

Analytics Layer:

The analytics layer is the place the data is massaged and manipulated into a format that can be meaningfully displayed and analyzed visually.

- Aspects of this layer consist of predictive analysis, data mining, KPI (key performance indicator) introduction as well as third-party BI tools. 

Data Layer:

The information layer is comprised of all sources that contain the data being analyzed. -

Data frequently comes from OLAP, MS SQL, MySQL, and Oracle databases, and even from spreadsheets such as Microsoft Excel. 

From the facts above you can see that data visualization is at the top of the BI stack. It needs to be noted that all three layers are critical when it comes to making exact decisions utilizing commercial enterprise intelligence. Presenting a well-designed dashboard to end-users is of little cost if the data it is displaying is poorly organized. Conversely, searching at a poorly designed dashboard is of little use even if the data it is displaying has been well-mined and organized. 

In conclusion, the visualization of information is extremely important when making clever business decisions. When properly done, mass quantities of data can be analyzed and interpreted quickly and efficiently, which is a suitable thing when it comes to any sort of business enterprise management!