Data Visualization Basics

by admin on March 10, 2011

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 term “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 data quickly.  Of course, how efficient this interpretation occurs depends 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 raw data, and making intelligent business decisions depends on how well a company analyzes and interprets that data.  It is possible to examine that data in a textual format such as tables and spreadsheets; however, this tends to be overwhelming to the analyst, as well as difficult to interpret.  Key trends may not be identified, resulting in the making of poor business decisions.  This is where the visualization of data comes to the rescue: large amounts of data can be displayed (via dashboards, scorecards, charts, dials, maps, gauges, graphs and other visual elements) and almost instantaneously absorbed by 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 business data.  The “stack” refers to the following components used to accomplish these objectives:

1. Presentation Layer: 

Consists of various methods used to display data to the end user.
Data visualization tools and elements  include:
     i.      Performance dashboards.
     ii.     Digital scorecards.
     iii.    Charts,  graphs and gauges.

2. Analytics Layer: 

The analytics layer is where the data is massaged and manipulated into a format that can be meaningfully  displayed and analyzed visually.
 Aspects of this layer include predictive analysis, data mining,  KPI (key performance indicator) creation  as well as third-party BI tools.

Data Layer: 

The data layer is comprised of all sources that contain the data being analyzed.
Data often comes from  OLAP, MS SQL, MySQL and Oracle databases, and even from spreadsheets such as Microsoft Excel.

From the information above you can see that data visualization is at the top of the BI stack.  It should be noted that all three layers are critical when it comes to making good decisions utilizing business intelligence.  Presenting a well-designed dashboard to end users is of little value if the data it is displaying is poorly organized.  Conversely, looking 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 data is extremely important when making intelligent business decisions.  When properly done, mass amounts of data can be analyzed and interpreted quickly and efficiently, which is a good thing when it comes to any sort of company management!

Martin Eising currently works with Dundas Data Visualization.

Dundas Data Visualization is a world leader in data visualization and dashboarding solutions. The company’s products include Dundas Dashboard, an easy-to-integrate, turnkey dashboard-creation solution.

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