Elicit the past

The only new thing in the world is the history you don’t know. —Harry Truman .

It is common to think of statistical software and data visualization as relatively modern developments in statistics. In fact, the graphic portrayal of quantitative information has deep roots. These roots reach into the histories of the earliest map-making like personalized maps online and visual depiction, and later into thematic cartography, statistics and statistical graphics, with applications and innovations in many fields of medicine and science that are often intertwined with each other. They also connect with the rise of statistical thinking and widespread data collection for planning and commerce up through the 19th century. Along the way, a variety of advancements contributed to the widespread use of data visualization today. These include technologies for drawing and reproducing images, advances in mathematics and statistics, and new developments in data collection, empirical observation and recording.

What is Data Visualization?

Today's data visualization tools like interactive visualization and visualization web go beyond the standard charts and graphs used in Microsoft Excel spreadsheets, displaying data in more sophisticated ways such as info graphics dials and gauges, geographic maps, spark lines. Heat maps and detailed bar pie and fever charts, also in BI dashboard tools. The images may include interactive capabilities, enabling users to manipulate them or drill into the data for querying and analysis. Indicators designed to alert users when data has been updated or predefined conditions occur can also be included.

Data visualization has become the de facto standard for modern business intelligence (BI) in intelligence applications. The success of the two leading vendors in the BI space, Tableau (toile design) and Qlik (quil sense cloud)-- both of which heavily emphasize visualization -- has moved other vendors toward a more visual approach in their software. Virtually all BI software has strong data visualization functionality. Data visualization tools have been important in democratizing data and analytic and making data-driven insights available to workers throughout an organization. They are typically easier to operate than traditional statistical analysis software or earlier versions of BI software. This has led to a rise in lines of business implementing data visualization tools on their own, without support from IT. Data visualization software also plays an important role in big data and advanced analytic projects. As businesses accumulated massive troves of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. Visualization tools were a natural fit. Visualization is central to advanced analytic for similar reasons. When a data scientist is writing advanced predictive analytic or machine learning algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of analysis of algorithms are generally easier to interpret than numerical outputs.

Examples of data visualization

Data visualization tools can be used in a variety of ways. The most common use today is as a BI reporting tool. Users can set up visualization tools to generate automatic dashboard that track company performance across key performance indicators and visually interpret the results. Many business departments implement data visualization software to track their own initiatives. For example, a marketing company 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.

Data Visualization Techniques

A picture is worth a thousand words – especially when you are trying to find relationships and understand your data, which could include thousands or even millions of variables. To create meaningful visuals of your data, there are some basic tips and techniques you should consider. Data size and composition play an important role when selecting graphs to represent your information. This paper, filled with graphics and explanations, discusses some of the basic issues concerning data visualization and provides suggestions for addressing those issues. From there, it moves on to the topic of big data and discusses those challenges and potential solutions as well. It also includes a section on SAS® Visual Analytics, software that was created especially for quickly visualizing very large amounts of data. Autocharting and "what does it mean" balloons can help even novice users create and interact with graphics that can help them understand and derive the most value from their data.

How Is It Being Used?

Regardless of industry or size, all types of businesses are using data visualization to help make sense of their data. Here’s how,

Comprehend information quickly
By using graphical representations of business information, businesses are able to see large amounts of data in clear, cohesive ways – and draw conclusions from that information. And since it’s significantly faster to analyze information in graphical format (as opposed to analyzing information in spreadsheets), businesses can address problems or answer questions in a more timely manner.

Pinpoint emerging trends
Using data visualization to discover trends – both in the business and in the market – can give businesses an edge over the competition, and ultimately affect the bottom line. It’s easy to spot outliers that affect product quality or customer churn, and address issues before they become bigger problems.

Identify relationships and patterns
Even extensive amounts of complicated data start to make sense when presented graphically; businesses can recognize parameters that are highly correlated. Some of the correlations will be obvious, but others won’t. Identifying those relationships helps organizations focus on areas most likely to influence their most important goals.

Communicate the story to others
Once a business has uncovered new insights from visual analytics, the next step is to communicate those insights to others. Using charts, graphs or other visually impactful representations of data is important in this step because it’s engaging and gets the message across quickly.


Success is better decision making. Previously with low volumes of data, intuitive decision making would work. As the data size has grown to incredible proportions, human ability to make completely intuitive decisions has been reduced. As a result, data-driven decision making has become more prevalent to ensure a reasonable path for success. This situation makes sense as it is easy to see that data are not diminishing but rather increasing. As organizations grow, evolve, and modernize, their requirements change accordingly, and they develop a need for improved BI and analytic solutions. Organizations are increasingly recognizing that to be competitive, they need to be data-driven, for which they need not only strong analytic and BI capabilities at all decision levels, but also an effective way to transform data into information and ensure its optimal delivery by delivery software. They need BI and business analytic solutions to deliver the right selection of information in the best package.

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