Guide What is Data Visualization?

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What is Data Visualization?

Data VisualizationData visualization (or “data viz”) is a business intelligence tool that puts information in a visual context. At its core, data visualization helps people organize, understand, and use data to its fullest potential. This might include pie charts, bar graphs, sparklines, or any other visual aid that helps its audience find the answers they need. Effective data visualization communicates information clearly and easily, so users can not only find the data they’re looking for, but utilize it to make data-driven decisions that push their businesses, organizations, and companies toward success.

In many ways, data visualization is where business intelligence meets technology. While the science of communicating visually is integral to effective data viz, it isn’t the only benefit – especially when you couple visualization with the right technology. Once digitized, the benefits also include:

  • Reporting that’s immediate or updated at controlled intervals
  • Information that can be accessed from virtually any device
  • Data that’s easy to share with every stakeholder in a given organization

What Makes Data Visualization Effective?

In concept, data visualization is fairly simple; but executing it can be complex. In short, there are four integral elements that make visualized data successful. They are:

data visualization concept


data visualization Function


data visualization Information


data visualization Form


Data viz starts with a concept. Data tells a story and provides something useful to its consumers. What will your data convey? Is it meaningful and relevant? Is it new information? These questions define data concept. Without them, you run the risk of producing a report or dashboard that, while beautiful, may not serve a distinct purpose or accomplish a goal.

Your data visualization’s function is closely related to concept, but deals with additional, tangible factors like usability, efficiency, and context. Does your data visualization support its original concept? Does it use space, color, and chart-types efficiently? Is the data displayed with enough information to provide context without creating unnecessary clutter?

Data visualization is designed to communicate information. Information is data in its rawest form. Without visualization, data may seem like little more than an array of statistics, spreadsheets, and numbers. Without a clear concept and goal, this information doesn’t serve an obvious purpose. On the other hand, data visualization without information will fall short of “good” data visualization as well.

The final – and most recognizable – element of data visualization is form. These are the features and visual elements that make your dashboard or other form of data visualization appealing. But form is much more than simple eye candy; it’s the final piece of the data visualization puzzle that puts the “visual” in data visualization.

Data Visualization Best Practices

With a clear understanding of the practical elements that make data visualization effective, data analysts should use the following practices to fully empower their visual reporting:

Remember that simple is better.

Remember that simple is better.
Data visualization should make information easy to understand and implement. This means reporting should focus on high-level information first, not granular information. Even if the data involves complex sets of information, it should be displayed in a clean, understandable format.

Always give your data context.

Always give your data context.
Goals and key performance indicators (KPIs) give data context. Even relevant data may not seem important unless it’s displayed alongside KPIs and used to track progress, status, and the bottom line. When using data visualization, make sure your audience can see the context along with the actual information you’re reporting.

Find the best visualization method for each metric.

Find the best visualization method for each metric.
The charts and graphs you choose should never be arbitrary. Instead, they should be based on the information you need to convey. Consider the difference between a sparkline and a line graph, for example: Line graphs, generally speaking, convey more detail. The level of detail your audience needs for a given metric will determine which of these options is best.

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