It’s true that a picture is worth a thousand words, especially when it comes to comprehending data and deriving useful insights from it. Leveraging BI tools to create visuals can help you find relationships between variables and determine their relative importance to the big picture. Following data visualization best practices can help convey information in a simple manner.

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This Guide Covers:

• What Is Data Visualization?
• Why Is It Important?
• Visualization Types
• Visualizing Big Data
• Common Challenges
• Data Visualization Best Practices

What Is Data Visualization?

Data visualization is the practice of representing data or information graphically in the form of charts, graphs, maps and more. Data visualization tools like Tableau, Power BI, SAS Visual Analytics, Qlik Sense and more empower you to see and gauge trends, patterns and outliers in underlying data to extract useful insights and make data-driven decisions.

Follow data visualization best practices to create robust visuals and convey business metrics through dashboards. Monitor events, activities or operations at a glance by providing insights on single or multiple screens. Present real-time information by pulling complex data points from large data sets. Create interactive dashboards to sort, filter and drill down into information.

Why Is It Important?

• See the Big Picture: You can unearth patterns and insights within complex data sets without relying on IT teams.
• Identify Correlations: You can identify correlations between variables with robust data visualizations. In other words, with the knowledge of how one variable affects the other, you can make better decisions.
• Market Analysis: Obtain insights into market dynamics to understand which ones to focus on to sell your products and services.
• Democratize Data: Provide a single source of truth for the organization.
• Make Quick Decisions: Comprehend complex information with intuitive visuals to make faster decisions.

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Data Visualization Types

The different visualization types are as follows:

Line Charts

Line charts show relationships between two variables. They are often used to track changes over time. They are useful when comparing multiple variables over the same period. Leverage line charts to view data trends over a period. For example, stock price changes over ten years or website views per month.

A line chart depicting server CPU load by days. Source

Bar Charts

Bar charts commonly compare quantities of categories or groups. They represent each category’s values using vertical or horizontal bars, with the length and height of each bar representing a particular value.

Bar graph showing sales and expenses by month. Source

If you’re representing distinct values, a simple bar chart is the right choice. However, if you’re displaying values close to each other or extremely large numbers, a bar chart isn’t a suitable option.

Another form of the bar chart is the progressive bar or waterfall chart. It displays how the initial value increases or decreases with successive operations.

Scatter Plots

Scatter plots are useful for determining the relationship or correlation between two variables. Variables are correlated if they are dependent on or influenced by each other. For instance, the relationship between revenue and profits is direct; profits are likely to increase if revenue increases.

Scatterplot depicting a positive correlation between economy and happiness score. Source

You can also apply statistical analysis with correlation and regression in a scatter plot.

Histogram Plot

A histogram plot is a widely used data visualization technique in machine learning. A histogram represents the distribution of continuous variables over a given interval. It plots data by breaking it down into intervals called bins. These bins are consecutive and non-overlapping intervals of a variable.

Histogram plot showing employee salary distribution of a particular organization Source

Histograms help understand the underlying frequency distribution of the data set, skewness and outliers.

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Visualizing Big Data

While visualizing big data, you need to take column cardinality and the size, speed and diversity of data into consideration. Big data is defined as the data with high volume, variety and velocity where:

• Volume refers to data size.
• Variety describes whether the data is structured, unstructured or semi-structured.
• Velocity is defined as the speed at which data generates and how frequently it changes.

Big data analytics involves analyzing big data to uncover hidden patterns, correlations, market trends and outliers.

Here are some different ways to visualize big data:

Big Data Visualization Techniques

Box Plot

A box plot is a graphical representation of five statistics, including minimum, lower quartile, median, upper quartile and maximum, that summarize data sets. The lower edge of the box represents the lower quartile, while the upper edge represents the upper quartile.

A boxplot depicting fruits and grains sales based on temperature. Source

The central line identifies the median that divides the box into two sections. The whiskers that extend from the edges of the box show outliers. The primary objective of the box plot is to detect outliers.

Word Clouds and Network Diagrams

Word clouds are useful in text analytics. They display the frequency of words within a text body with their relative size in the cloud. This technique displays high or low-frequency words in unstructured data.

Wordcloud displaying word frequency in clouds. Source

Another visualization technique used for semi-structured or unstructured data is the network diagram. The nodes in the network diagram represent individual actors within the network, and the ties represent relationships between actors.

Network diagrams are used in many applications or disciplines. For example, organizations analyze their social networks to gauge their interactions with customers or display the relationship of product sales across geographies.

Correlation Matrices

Correlation matrices serve as a handy tool to identify and assess relationships between variables. A correlation matrix is a table displaying correlation coefficients between variables or a color-coded table where darker boxes represent strong relations, and lighter boxes indicate weaker relationships.

Correlation matrix showing multi-collinearity of different apartment features. Source

Common Challenges

Some common data visualization challenges include:

Lack of Data Understanding

No matter how appealing visualizations look, they don’t make sense if they fail to tell the right story from the underlying data. It is vital to understand the data first to avoid telling inaccurate, incomplete or misleading data stories. Ensure that you spot and resolve data issues before creating visuals.

Clutter

Trying to stuff too much information into visualizations or dashboards can leave people confused and frustrated. Instead, limit the number of KPIs on the dashboard, use fewer colors, leverage pie charts where needed and use simple formats to avoid clutter.

Lack of Data Governance

While some people may be comfortable with spreadsheets and ungoverned analytics tools to create visualizations, they may cause unforeseen challenges. Implement robust data governance practices to avoid non-standard visuals, inaccurate analysis and incomplete data stories.

Dependency on Manual Processes

When you create visualizations by manually preparing data in spreadsheets, they are bound to contain errors, waste productive hours and distribute incorrect information that leads to faulty decisions. Data visualization tools help you leverage machine learning and AI capabilities to automate data cleaning and transformation processes to overcome the aforementioned challenges.

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Best Practices

Let’s look at some data visualization best practices:

Determine the Audience and Address Their Needs

When creating visualizations, it is important to keep your audience in mind. Make sure you answer the following questions: Who will be looking at the data? What challenges does the audience face? How can they overcome challenges with dashboards? Resist the temptation to create generic dashboards and ensure they serve decision-makers’ needs.

Choose the Right Visuals

Choose a specific format to create visualizations for depicting different types of information.

So, which chart type should you choose?

• Bar Charts: They are the most commonly used visualization techniques to compare measures.
• Line Charts: Line charts display trends over time and the relationship between two or more variables.
• Bullet Charts: Compare values to display progress against goals. They serve as a replacement for meters, thermometers and gauges.
• Maps: Maps aid geographical exploration and answer specific questions. For example, voters per state or sales per region.
• Pie Charts: Pie charts compare parts of a whole, for example, monthly income distribution.

Structured Layouts

Leverage layouts to convey information rationally. For instance, if you use multiple charts or graphs in your dashboard, ensure the order of the charts is clear and straightforward to communicate the data story effectively.

Leverage Color Hues

Use different color hues to portray information in a meaningful way without directly communicating with words. Leverage colors to highlight data. Make sure you don’t overuse colors in a chart or graph to avoid confusion.

A single color, or shades of the same color, can cause data amalgamation. Use natural colors to help your audience process information faster. For example, use red for hot and blue for frosty areas on a map.

Use legends to define color schemes. Avoid using legends if you have a single data category.

Use Formatting Style

Use gridlines to compare thresholds and labels to display exact values. They enhance visual representation and readability. Format axes to control intervals. Make sure there is proper spacing between values for correct interpretation.

Focus on Key Areas

Ensure that key areas are highlighted to draw attention. You can choose to place key data points in popular areas.

Direct attention with conditional formatting, reference lines, trends or forecasts to increase dwell time and facilitate better data understanding.

Keep It Simple

Ensure visuals are simple and easy to comprehend. Adding unnecessary information can make it complicated and confusing for non-specialists. Keep in mind that the ultimate goal of creating visualizations is to communicate complex information simply. You may build additional visuals to portray a multi-faceted story.

Incorporate Interactivity

Incorporate interactivity into graphs and charts so your users can deep dive into data to unearth useful insights, clarify doubts or queries, and make data-driven decisions.

Add Clarity

Use robust chart titles to communicate the story, purpose and meaning of charts to end users. Use descriptions to convey additional information about the visualizations. Add context and perspective by leveraging annotations or adding comments.

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Conclusion

It’s important to follow data visualization best practices to convey information clearly and concisely to the target audience. Interactive data visualizations help identify trends, patterns and outliers while telling a story through data.

What data visualization best practices do you follow? Please let us know in the comments below.