Approaches to Big Data Visualization
Data visualization is when you manually or otherwise organize and display data in a pictorial or graphic format in an attempt to enable your audience to:
This concept of using pictures–typography, color, contrast, and shape–to communicate or understand data is not new and has been around for literally centuries, from the manual creation of maps and graphs in the 17th century to the invention of the pie chart in the early 1800s.
Today, computers can be used to process large amounts of data lightning fast to make visualizations tremendously more valuable. Going forward, we can expect the data visualization process to continue to evolve, perhaps as more of a mixture of art and science rather than a numbers crunching technology.
An exciting example of the data visualization evolutionary process is how the industry has moved data visualizations past the process of generating and publishing charts and graphs for an audience to review and deliberate on to now having set up an expectation for interactive visualizations.
With interactive visualization, we can take the concept of data visualization much, much further by using technology to allow the audience to interact with the data; giving the user the self-service ability to drill down into the generated pictures, charts, and graphs (to access more or specific details), interactively in real time (or near real time) to change what data is displayed (perhaps a different time frame or event) and how it’s processed and/or presented (maybe select a bar graph rather than a pie chart).
This allows visualizations to be much more effective and personalized.
In Chapter 5, Displaying Results with D3, we will go through the topic of displaying the results of analysis on big data using a typical web browser using Data Driven Documents (D3) in a variety of examples. D3 allows the ability to apply pre-built data visualizations to datasets.
Data Driven Documents is referred within the open community as D3.
D3 is an open source library written in JavaScript. The objective is to allow for easily manipulating documents based upon data using standard web browsing technologies (such as HTML or CSS). Its value-add is to provide you with full capabilities without having to build your own or strapping yourself to some proprietary framework.
These library components give you excellent tools for big data visualization and a data-driven approach to DOM manipulation. D3’s functional style allows the reuse of library code modules that you’ve already built (or others have already built) adding pretty much any particular features you need or want (or don’t want) to. This creates a means that can become as powerful as you want it (or have the time to make it) to be, to give a unique style to your data visualizations, manipulate and make it all interactive–exactly how you want or need it to be.
As discussed earlier in this chapter, big data is collecting and accumulating daily, in fact; minute-by-minute and there is a realization that organizations rely on this information for a variety of reasons.
Various types of reporting formats are utilized on this data, including data dashboards.
As with everything, there are various apprehensions as to the most accurate definition of what a data dashboard is.
“A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.”
Refer to the following link for more information: http://www.dashboardinsight.com/articles/digital-dashboards/fundamentals/what-is-a-dashboard.aspx.
Whatever the definition, any dashboard has the capacity for supplying timely, important information for its audience to use in decision making, if it is well designed and constructed.
