Data Visualization Basics

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What is data visualization?

Using graphs or charts to present data is easier than having decision-makers sweat over spreadsheets or reports. Data visualization or visual analytics is the presentation of data in a pictorial or graphical format. Data visualization allows decision makers to understand data analytics better, grasp and convey difficult concepts quickly or identify new patterns.

This history of using pictures to present complex data has an oft-mentioned seminal example from the 17th century; Charles Minard’s visualization of Napoleon’s invasion of Russia - a map that graphically simplified complex data on the advance of the Napoleonic troops across the Polish-Russian border. Since this seminal age, data visualization has since been a staple of analyzing and communicating information with Venn diagrams, graphs, pie charts, infographics and the like for business, marketing, and every field of study requiring data analysis.

With computer and digital technology, data visualization has had an exponential boost in power and capabilities. With computers now processing large amounts of data at high speeds, data visualization technology has become considerably interactive allowing decision makers to drill down into these charts and graphs for more detail, interactively changing details as huge amounts data are continually collected, stored, processed and updated.

Why is data visualization important?

The answer is big data. Visualization has become an important component of big data analytics, helping to achieve insights quickly from the big data found in many of today’s globally competitive enterprises.  In big data such as the customer and the supply chain data of corporations, data sets have become so large and complex that traditional data processing applications are inadequate to visualize, analyze, capture, curate, search, share, store, transfer and protect them. The high volume, velocity and variety nature of this big data makes traditional architectures and infrastructures ill-suited to those tasks and thereby ill-suited to extracting any competitive business advantages and growth opportunities from them. Data visualization or visual analytics overcomes the challenges from big data’s bewildering nature with the power of computing technology.

Current data visualization or visual analytics technology is capable of processing big data and visually presenting insights in a way that business leaders can quickly understand and use.  Visual Analytics can access huge data sets and automatically select the best way to present the data, so that patterns emerge quickly, exceptions and outliers are made obvious, and changes (data over time) are analyzed quickly.

What is ‘good data visualization’?

Data visualization is the visual display of measured quantities b means of the combined use of a coordination system, points, lines, shapes, digits, letters quantified by visual attributes. It can be static, animated or interactive. However, good data visualization has three qualities:

  • Good data visualization establishes hierarchies; making them clear through the visual layering of data.
  • Good data visualization challenges the viewer to think about the substance rather than about the methodology, graphic design, or the technology used to make the visualization.
  • Good data visualization encourages comparisons, revealing data at several levels of detail, from a broad overview to minute statistics.

What are the benefits of data visualization?

Lastly, great data visualization enables decisions to understand trends and patterns, helping them make correlations, as well as:

  • Absorb information in new and constructive ways
  • Visualize relationships and patterns between operational and business activities
  • Identify and act on emerging trends faster
  • Identify exceptions and outliers
  • Manipulate and interact directly with data
  • Foster a new business language

Reporting of analytics is not enough. Methods or techniques that reveal and update the most relevant aspects of analytics findings for that moment are necessary for non-IT/data science decision makers to make truly informed decisions for the business. This is data visualization, an essential process in obtaining big data insights or bringing into focus the right data for critical future actions.

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