DATA VIZ — THE NITTY GRITTY
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. It is also the study of visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text. It is related to infographics, scientific visualizations, and mapping.
From an academic point of view, this representation can be considered as a mapping between the original data (usually numerical) and graphic elements (for example, lines or points in a chart). The mapping determines how the attributes of these elements vary according to the data. In this light, a bar chart is a mapping of the length of a bar to a magnitude of a variable. Since the graphic design of the mapping can adversely affect the readability of a chart, mapping is a core competency of Data visualization. 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. It has been said that “a picture is worth a thousand words.” And today, in the era of big data, when businesses are inundated with information from varied data types and from on-premises and cloud-based sources, that old saying has never been more relevant.
Sifting through information to understand what matters and what doesn’t is becoming more difficult. Visuals make analysis easier and offer the ability to see at a glance what matters. What’s more, most people respond far better to visuals than text — 90 percent of the information sent to the brain is visual, and the brain processes visuals at 60,000 times the speed of text. Those points make a strong case for the use of data visualization for analyzing and conveying information.
How data visualization works
Most data-visualization tools are capable of connecting with data sources such as relational databases. This data, which may be stored on-premises or in the cloud, is retrieved for analysis. Users can then select the best way to present the data from numerous options. Some tools automatically provide display recommendations based on the type of data presented.
Our eyes are drawn to colors and patterns. We can quickly identify red from blue, and square from the circle. Our culture is visual, including everything from art and advertisements to TV and movies. Data visualization is another form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we quickly see trends and outliers. If we can see something, we internalize it quickly. It’s storytelling with a purpose. If you’ve ever stared at a massive spreadsheet of data and couldn’t see a trend, you know how much more effective a visualization can be.
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 the information they can understand.
- an easy distribution of information that increases the opportunity to share insights with everyone involved.
- an increased ability to act on findings quickly and, therefore, achieve success with greater speed and fewer mistakes.
The different types of Visualizations
The earliest form of data visualization can be traced back to the Egyptians in the pre-17th century, largely used to assist in navigation. As time progressed, people leveraged data visualizations for broader applications, such as in economic, social, and health disciplines. When you think of data visualization, your first thought probably immediately goes to simple bar graphs or pie charts. While these may be an integral part of visualizing data and a common baseline for many data graphics, the right visualization must be paired with the right set of information. Simple graphs are only the tip of the iceberg. There’s a whole selection of visualization methods to present data in effective and interesting ways. Common general types of data visualization:
- Charts - These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.
- Tables - This consists of rows and columns used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-level trends.
- Graphs - These visuals show changes in one or more quantities by plotting a series of data points over time. Line graphs utilize lines to demonstrate these changes while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.
- Maps - This displays hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.
- Infographics - Infographics are graphic visual representations of information, data, or knowledge intended to present information quickly and clearly.
- Dashboards - A dashboard is a type of graphical user interface that often provides at-a-glance views of key performance indicators relevant to a particular objective or business process.
Data visualization best practices
Visual communication should be simple and deliberate to ensure that your data visualization helps your target audience arrive at your intended insight or conclusion. The following best practices can help ensure your data visualization is useful and clear:
Set the context: It’s important to provide general background information to ground the audience around why this particular data point is important. For example, if e-mail open rates were underperforming, we may want to illustrate how a company’s open rate compares to the overall industry, demonstrating that the company has a problem within this marketing channel.
Know your audience(s): Think about who your visualization is designed for and then make sure your data visualization fits their needs. What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns?
Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time-series data well. Ensure that the visual actually assists the audience in understanding your main takeaway.
Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention.
There are dozens of tools for data visualization and data analysis. These range from simple to complex, from intuitive to obtuse. Not every tool is right for every person looking to learn visualization techniques, and not every tool can scale to industry or enterprise purposes. If you’d like to learn more about the options, feel free to read up here Also, remember that good data visualization theory and skills will transcend specific tools and products. When you’re learning this skill, focus on best practices and explore your own personal style when it comes to visualizations and dashboards. Data visualization isn’t going away any time soon, so it’s important to build a foundation of analysis and storytelling, and exploration that you can carry with you regardless of the tools or software you end up using.
