data visualization

What is data visualization?

Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets. The term is often used interchangeably with others, including information graphics, information visualization and statistical graphics.

Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made. Data visualization is also an element of the broader data presentation architecture (DPA) discipline, which aims to identify, locate, manipulate, format and deliver data in the most efficient way possible.

Data visualization is important for almost every career. It can be used by teachers to display student test results, by computer scientists exploring advancements in artificial intelligence (AI) or by executives looking to share information with stakeholders. It also plays an important role in big data projects. As businesses accumulated massive collections of data during the early years of the big data trend, they needed a way to get an overview of their data quickly and easily. Visualization tools were a natural fit.

Visualization is central to advanced analytics for similar reasons. When a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.

The history of data visualization
A timeline depicting the history of data visualization

Why is data visualization important?

Data visualization provides a quick and effective way to communicate information in a universal manner using visual information. The practice can also help businesses identify which factors affect customer behavior; pinpoint areas that need to be improved or need more attention; make data more memorable for stakeholders; understand when and where to place specific products; and predict sales volumes.

Other benefits of data visualization include the following:

  • the ability to absorb information quickly, improve insights and make faster decisions;
  • an increased understanding of the next steps that must be taken to improve the organization;
  • an improved ability to maintain the audience's interest with information they can understand;
  • an easy distribution of information that increases the opportunity to share insights with everyone involved;
  • eliminate the need for data scientists since data is more accessible and understandable; and
  • an increased ability to act on findings quickly and, therefore, achieve success with greater speed and less mistakes.

Data visualization and big data

The increased popularity of big data and data analysis projects have made visualization more important than ever. Companies are increasingly using machine learning to gather massive amounts of data that can be difficult and slow to sort through, comprehend and explain. Visualization offers a means to speed this up and present information to business owners and stakeholders in ways they can understand.

Big data visualization often goes beyond the typical techniques used in normal visualization, such as pie charts, histograms and corporate graphs. It instead uses more complex representations, such as heat maps and fever charts. Big data visualization requires powerful computer systems to collect raw data, process it and turn it into graphical representations that humans can use to quickly draw insights.

While big data visualization can be beneficial, it can pose several disadvantages to organizations. They are as follows:

  • To get the most out of big data visualization tools, a visualization specialist must be hired. This specialist must be able to identify the best data sets and visualization styles to guarantee organizations are optimizing the use of their data.
  • Big data visualization projects often require involvement from IT, as well as management, since the visualization of big data requires powerful computer hardware, efficient storage systems and even a move to the cloud.
  • The insights provided by big data visualization will only be as accurate as the information being visualized. Therefore, it is essential to have people and processes in place to govern and control the quality of corporate data, metadata and data sources.

Examples of data visualization

In the early days of visualization, the most common visualization technique was using a Microsoft Excel spreadsheet to transform the information into a table, bar graph or pie chart. While these visualization methods are still commonly used, more intricate techniques are now available, including the following:

  • infographics
  • bubble clouds
  • bullet graphs
  • heat maps
  • fever charts
  • time series charts

Some other popular techniques are as follows:

Line charts. This is one of the most basic and common techniques used. Line charts display how variables can change over time.

Area charts. This visualization method is a variation of a line chart; it displays multiple values in a time series -- or a sequence of data collected at consecutive, equally spaced points in time.

Scatter plots. This technique displays the relationship between two variables. A scatter plot takes the form of an x- and y-axis with dots to represent data points.

Treemaps. This method shows hierarchical data in a nested format. The size of the rectangles used for each category is proportional to its percentage of the whole. Treemaps are best used when multiple categories are present, and the goal is to compare different parts of a whole.

Population pyramids. This technique uses a stacked bar graph to display the complex social narrative of a population. It is best used when trying to display the distribution of a population.

Common data visualization use cases

Common use cases for data visualization include the following:

Sales and marketing. Research from market and consumer data provider Statista estimated $566 billion was spent on digital advertising in 2022 and that number will cross the $700 billion mark by 2025. Marketing teams must pay close attention to their sources of web traffic and how their web properties generate revenue. Data visualization makes it easy to see how marketing efforts effect traffic trends over time.

Politics. A common use of data visualization in politics is a geographic map that displays the party each state or district voted for.

Healthcare. Healthcare professionals frequently use choropleth maps to visualize important health data. A choropleth map displays divided geographical areas or regions that are assigned a certain color in relation to a numeric variable. Choropleth maps allow professionals to see how a variable, such as the mortality rate of heart disease, changes across specific territories.

Scientists. Scientific visualization, sometimes referred to in shorthand as SciVis, allows scientists and researchers to gain greater insight from their experimental data than ever before.

Finance. Finance professionals must track the performance of their investment decisions when choosing to buy or sell an asset. Candlestick charts are used as trading tools and help finance professionals analyze price movements over time, displaying important information, such as securities, derivatives, currencies, stocks, bonds and commodities. By analyzing how the price has changed over time, data analysts and finance professionals can detect trends.

Logistics. Shipping companies can use visualization tools to determine the best global shipping routes.

Data scientists and researchers. Visualizations built by data scientists are typically for the scientist's own use, or for presenting the information to a select audience. The visual representations are built using visualization libraries of the chosen programming languages and tools. Data scientists and researchers frequently use Open Source programming languages -- such as Python -- or proprietary tools designed for complex data analysis. The data visualization performed by these data scientists and researchers helps them understand data sets and identify patterns and trends that would have otherwise gone unnoticed.

The science of data visualization

The science of data visualization comes from an understanding of how humans gather and process information. Daniel Kahn and Amos Tversky collaborated on research that defined two different methods for gathering and processing information.

System 1 focuses on thought processing that is fast, automatic and unconscious. This method is frequently used in day-to-day life and helps accomplish:

  • reading the text on a sign;
  • solving simple math problems, like 1+1;
  • identifying where a sound is coming from;
  • riding a bike; and
  • determining the difference between colors.

System 2 focuses on slow, logical, calculating and infrequent thought processing. This method is used in one of the following situations:

  • reciting a phone number;
  • solving complex math problems, like 132 x 154;
  • determining the difference in meaning between multiple signs standing side by side; and
  • understanding complex social cues.

Data visualization tools and vendors

Data visualization tools can be used in a variety of ways. The most common use today is as a business intelligence (BI) reporting tool. Users can set up visualization tools to generate automatic dashboards that track company performance across key performance indicators (KPIs) and visually interpret the results.

The generated images may also include interactive capabilities, enabling users to manipulate them or look more closely into the data for questioning and analysis. Indicators designed to alert users when data has been updated or when predefined conditions occur can also be integrated.

Many business departments implement data visualization software to track their own initiatives. For example, a marketing team might implement the software to monitor the performance of an email campaign, tracking metrics like open rate, click-through rate and conversion rate.

As data visualization vendors extend the functionality of these tools, they are increasingly being used as front ends for more sophisticated big data environments. In this setting, data visualization software helps data engineers and scientists keep track of data sources and do basic exploratory analysis of data sets prior to or after more detailed advanced analyses.

The biggest names in the big data tools marketplace include Microsoft, IBM, SAP and SAS. Some other vendors offer specialized big data visualization software; popular names in this market include Tableau, Qlik and Tibco.

While Microsoft Excel continues to be a popular tool for data visualization, others have been created that provide more sophisticated abilities:

  • IBM Cognos Analytics
  • Qlik Sense and QlikView
  • Microsoft Power BI
  • Oracle Visual Analyzer
  • SAP Lumira
  • SAS Visual Analytics
  • Tibco Spotfire
  • Zoho Analytics
  • D3.js
  • Jupyter
  • MicroStrategy
  • Google Charts
This was last updated in December 2022

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