In today's data-driven world, Data visualization is a powerful tool for getting insights and trends that are effectively communicated by businesses, researchers, and individuals alike. The sheer volume of data generated every day makes it increasingly important for ways to be found to make sense of it all.
Hence, Data visualization is not just about creating pretty charts and graphs; it's about telling a story with your data. When done correctly, it can help you to:
- Identify areas for improvement
- Optimize processes
- Make predictions
- Inform strategic decisions
- Communicate complex ideas simply
In this article, we'll explore 12 useful ways to visualize your data, along with examples.
Understanding Data Granularity in Visuals
Before choosing a visualization method, it's crucial to understand data granularity—the level of detail in the dataset.
- High Granularity: Detailed data points, often at a transactional level.
- Medium Granularity: Summarized data, aggregated by a category (e.g., monthly sales, yearly profits).
- Low Granularity: Key insights and high-level summaries (e.g., KPI indicators, trends over time).
The following sections list visualization methods in increasing order of abstraction.
Data Granularity and Visualization Types
1. Table
Description: A table is a structured format for displaying data. It is a flat, two-dimensional structure that can display data in rows and columns. Tables are ideal for summarizing data in a straightforward manner, making them easy to read and interpret.
Example: The table below (Figure 1) shows countries with their respective total profit and cost. This type of visual helps clients to quickly summarize and compare key data points.
Data Granularity: High granularity (detailed data points).
Use Case: Summarizing detailed reports, displaying transactional data, and comparing multiple attributes.
2. Matrix
Description: A matrix is similar to a table but extends its capability to display data meaningfully across two or more dimensions. This makes it particularly useful for complex datasets that have multiple categories or groupings. Matrix visuals allow for calculations on the data, such as sums, averages, and percentages, which can help in summarizing data and identifying trends.
Example: The matrix below (Figure 2) shows the profit by year and country. This type of visual helps in understanding the performance across different dimensions simultaneously.
Data Granularity: High to medium granularity (multi-dimensional data).
Use Case: Analyzing relationships between multiple variables, summarizing data across different categories, and identifying trends.
What is the difference between table and matrix visual?
A table is a two dimensional visual to represent the irregular data whereas Matrix is multi dimension visual like an excel pivot table.
3. Stacked Barchart
Description: A stacked bar chart is the best choice when you want to show the contribution of individual parts to the total. It is particularly useful when you have a limited number of categories, as too many categories can make the chart difficult to interpret. The primary goal of this chart is to identify each part's contribution to the overall total.
Example: The chart below (Figure 3) contains 3 categories visualized according to their profit by year. It clearly shows the total profit value of each category within each year.
Data Granularity: Medium granularity (summary data for different categories).
Use Case: Comparing parts of a whole, visualizing composition over time, and identifying individual contributions within grouped data.
4. Clustered Barchart
Description: A clustered bar chart is great when you want to show multiple categories and sub-categories side by side. The primary goal of this chart is to compare the size of each category within each sub-category.
Example: The chart below (Figure 4) illustrates the total profit of each category in their respective year. It allows for easy comparison of the total profit value of each category.
Data Granularity: Medium granularity (comparison of different categories within sub-categories).
Use Case: Comparing multiple categories, visualizing differences within groups, and analyzing patterns across different sub-categories.
5. Stacked Column Chart
Description: A stacked column chart is similar to a stacked bar chart but uses vertical columns to show the composition and comparison of different categories and sub-categories. It is useful for visualizing how individual parts contribute to a total across different groups over time or categories.
Example: The chart below (Figure 5) represents the total profit of each category per year using vertical columns. It clearly shows the contribution of each category to the total profit within each year.
Data Granularity: Medium granularity (summary data for different categories within groups).
Use Case: Comparing parts of a whole over time, visualizing contributions within grouped data, and showing composition in a vertical format.
6. Clustered column chart
Description: A clustered column chart, also known as a columnar bar chart, is an ideal choice for comparing individual values across various categories. It provides a straightforward comparison of values, making it suitable for a large number of categories as it avoids the visual clutter that can occur in stacked column charts. It is similar to the clustered bar chart but uses vertical columns.
Example: The chart below (Figure 6) illustrates the total profit of multiple categories side by side using vertical columns for each year. It allows for an easy comparison of total profit values across different categories within each year.
Data Granularity: Medium granularity (comparison of individual values across categories).
Use Case: Comparing multiple categories, visualizing differences within groups, and analyzing patterns across various categories.
7. Card
Description: Card visuals help highlight critical data points such as key performance indicators (KPIs), totals, averages, or other single values that need to be prominently displayed. They typically display one key value or metric prominently, making it easy to quickly grasp essential information. The data in card visuals can be dynamic, updating in real-time or according to the latest data available.
Example: The visual below (Figure 7) indicates a card visual that displays the total profit, which is £46.88 million.
Data Granularity: High granularity (single data point).
Use Case: Highlighting important metrics, displaying KPIs, showing summary statistics, and providing quick insights.
8. Map Visual
Description: Map visualization plays a crucial role in displaying geographically related data in the form of maps. It helps to analyze and represent data expressions with more clarity, making it easier to identify geographic patterns and trends.
Example: The visual below (Figure 8) illustrates the total profit and average profit of their respective countries through a map representation.
Data Granularity: High granularity (geographic data).
Use Case: Analyzing geographic patterns, visualizing regional data, and identifying location-based trends.
9. Key Performance Indicator (KPI)
Description: Key Performance Indicators (KPIs) are specialized visuals used to track progress towards measurable goals, which can be critical business metrics and performance indicators. KPIs are very effective for organizations to make data-driven decisions, as they provide a clear and concise way to monitor performance against targets.
Example: The visual below (Figure 9) indicates the current level of revenue in bold green font, showing progress towards the revenue goal.
Data Granularity: High granularity (single or few key metrics).
Use Case: Tracking progress towards goals, monitoring performance metrics, and making informed business decisions.
10. Multirow Card
Description: A multirow card is a versatile tool used to display multiple values that correspond to different data points or scenarios. It allows you to present a compact summary of several key metrics at once, making it easier to view and compare multiple data points simultaneously.
Example: The visual below (Figure 10) indicates the multirow card which contains values for Revenue, Order Quantity, Return Rate, Return Quantity, Products, and Customers.
Data Granularity: High granularity (multiple key metrics).
Use Case: Displaying multiple KPIs, summarizing key data points, and providing an overview of different metrics in one visual.
11. Line Chart
Description: Line charts help present sequential values to identify trends over time. The horizontal axis (x-axis) typically represents time (days, months, years) in a continuous scale. Line charts are ideal for highlighting trends, patterns, and changes over time, making them an excellent choice for time-series data.
Example: The visual below (Figure 11) shows the revenue by month of a company. The x-axis of the graph shows time, with labels for every six months starting from January 2015 and ending in July 2017. The y-axis shows revenue in millions of dollars (m Revenue).
Data Granularity: Medium to high granularity (time-series data).
Use Case: Analyzing trends over time, tracking performance, and visualizing changes in data over a continuous timeline.
12. Treemap
Description: A treemap displays a hierarchical view of your data using nested rectangles. Each branch of the hierarchy is represented as a rectangle, and its sub-branches are shown as smaller rectangles within the parent rectangle. The size of each rectangle is proportional to a specific data value, and the color can be used to represent another dimension, such as category or magnitude. This makes treemaps particularly useful for visualizing the relative sizes of different categories within a hierarchy.
Example: The visual below (Figure 12) illustrates revenue sizes across various categories within the Education sector. Each rectangle's size represents the revenue amount for its respective category, providing a clear view of the distribution of revenue across different sub-categories.
Data Granularity: High granularity (hierarchical data).
Use Case: Visualizing hierarchical data, comparing sizes of categories, and understanding the proportion of different parts within a whole.
User Story: Creating a Comprehensive Dashboard
To illustrate the use of these charts in a cohesive manner, let's consider a user story where we create a dashboard for a retail company.
Objective: The dashboard aims to provide a comprehensive overview of the company's performance across different dimensions, including sales, profit, geographic distribution, and key performance indicators.
Charts Used:
- Table: Displays detailed sales transactions, including date, product, quantity, and sales amount.
- Matrix: Shows profit by year and product category to analyze trends and performance.
- Card: Highlights the total profit for the current year.
- Multi-row Card: Displays multiple KPIs, such as total revenue, order quantity, and return rate.
- KPI: Tracks the current revenue against the annual target.
- Line Chart: Shows monthly revenue trends over the past two years.
- Clustered Bar Chart: Compares profit by product category and year.
- Clustered Column Chart: Compares sales by region and quarter.
- Stacked Bar Chart: Illustrates the contribution of different product categories to the total sales.
- Stacked Column Chart: Visualizes the composition of expenses by category over time.
- Treemap: Represents sales distribution across various product categories and subcategories.
- Map Visual: Shows geographic distribution of sales and profit by country.
Dashboard Layout:
- Top Section: KPIs and Cards for quick insights.
- Middle Section: Line Chart and Clustered Column Chart for trend analysis.
- Bottom Section: Table, Matrix, and Treemap for detailed data exploration.
- Sidebar: Map Visual for geographic insights.
Best Practices for Effective Visualization Design
- Identify Suitable Visuals: Choose the right chart type for your data.
- Balanced Design: Use color, texture, shape, and negative space wisely.
- Highlight Information: Tailor visuals to your audience.
- Simplicity: Keep visuals clear and easy to understand.
- Interactivity: Add interactivity to enhance user experience.
- Consistency: Use similar colors and chart types to establish patterns.
- Alignment: Align data vertically or horizontally for easy comparison.
Common Pitfalls in Data Visualization
1. Chart Junk
- Chart junk describes the unnecessary or distracting elements in a visualization that do not add value or convey meaningful information.
- Challenges include locating and cutting off chart junk, thus ensuring that visualizations are crystal-clear, easy to comprehend, and productive in terms of answering questions.
Figure 15. Chart Junk
2. Compressing the Vertical Axis
- Vertical compression of a chart can cause data distortion and lead to misinterpretation of the data.
- It is necessary to be careful when picking the vertical axe scaling in order to correctly represent the data without any data compression which may exaggerate or hide the differences in it.
3. No zero point on the vertical axis
- There is no such a thing as zero point on a chart both on the horizontal and vertical axes of a graph that may lead to bigger gradients and thus, the result is the possibility of disillusionment of an observer.
- Actually, to make the graph more appropriate, the zero point must be positioned completely parallel to the z axis.
Conclusion
In conclusion, There are dozens of tools for visualizing the data. Not every tool is right for every person it maybe hard or easy according to their perspective. The good data visualization skills focus on best practices and the core concepts of visuals. So Explore your own personal style when it comes to visualizations and dashboards.
