Why Visualize Data?
According to Friedman (2008) the “goal of data visualization is to communicate information clearly and effectively through graphical means.” Data visualization is to help understand of data by leveraging the human visual interactive system’s ability to see patterns, trends, and identify outliers far more quickly than tables of numbers and text. Well-designed visual representations can replace cognitive calculations with simple perceptual inferences and improve comprehension, memory, and decision making. By making data more accessible and appealing, visual representations may also help engage more diverse audiences in exploration and analysis.
With visualization, users can spot issues and problems needing attention at a glance and take appropriate action. In text-based reports and spreadsheets, the trends and issues remain hidden among dizzying arrays of numbers and text. Because of its power to communicate, data visualization is becoming more pervasive in business environments. Unlike reporting tools, visual discovery tools provide sub-second response times for any action taken against the data (e.g., filtering, drilling, calculating, sorting, ranking) because they store data in memory instead of remote databases (although some can query databases dynamically as well). They are faster to deploy than BI tools, because the IT department doesn’t need to create a semantic layer or implement specific types of database schema, also can be less expensive.
Success Factors of Data Visualization
Factors which make a good visualization are E⁵
- Effective : Ease of interpretation
- Exact : Strictly in accord with fact.
- Efficient : Exhibiting high output to input ratio
- Exquisite : Characterized by intricate and beautiful design or execution
- Expandable : Easily adjustable for user needs
What We See in a Visual?
We visualize information to meet a very basic need – to tell a story. It’s one of the most primitive forms of communication known to man, having its origins in cave drawings dated as early as 30,000 B.C., even before written communication, which emerged in 3,000 B.C. Vision is the single most important faculty we use to communicate information.
With the passing of time, we’ve found new ways to visualize information. Today, we’re familiar with the basic chart types like the line chart, bar chart, and pie chart. However, we rarely stop to think about why they’re more effective than bland tables, text, and numbers. Further, we can’t easily spot instances when they’re done wrong, and can be improved.
Two Goals in Data Visualization
In a business environment, visualizations can have two broad goals, which sometimes overlap.
Explain Data to Solve Specific Problems
Visuals that are meant to direct the viewer along a defined path are explanatory in nature. The bulk of business dashboards that we come across in day-to-day scenarios fall in this category.
This type of visualization is used in many scenarios for the following tasks:
- Answer a question. E.g., How much sales did we have last quarter?
- Support a decision. E.g., We need to stock more football jerseys as they were sold out on most days last week
- Communicate information. E.g., Revenue is on track for this quarter
- Increase efficiency. E.g., ‘Technical specifications’ is the most viewed section in the product page. It should be given more visibility.
Explore Large Data Sets for Better Understanding
Exploratory visuals offer the viewer many dimensions to a data set, or compares multiple data sets with each other. They invite the viewer to explore the visual, ask questions along the way, and find answers to those questions.
Exploratory analysis can be cyclical without a specific end point. Viewers can find many insights from a single visualization, and interact with it to gain understanding rather than make a specific decision. This type of visualization can accomplish the following tasks:
- Pose new questions
- Explore and discover
Though they’re not as popular as the previous category, exploratory visualizations have gained prominence in recent years with the rise of big data. The high volume of data, and varied data sets that have become common today lend themselves easily to exploratory analysis.
Now that we’re aware of what makes visualizations intrinsic to business, let’s dive into the mechanics of how we process visual information. We’ll understand the role of memory in perceiving visual information, and how to apply that understanding as we work with visualizations.