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HomeBlogBusiness intelligence and Visualization15 Types of Data Visualization Charts with Examples
Data visualization charts are an invaluable tool in simplifying data analysis and presenting insights in visually appealing ways. This blog post aims to delve into the realm of data visualization charts, highlighting their significance, various types, recommended practices, and available tools to assist you in harnessing the potential of data visualization. For a deeper understanding and practical application of these concepts, consider going for BI & Visualization certification, which will enable you to leverage the power of data to make informed business decisions and position yourself for success in the rapidly expanding realm of sought-after data jobs.
Data visualization charts are graphical representations of data that help organizations understand and interpret complex information. These charts transform raw data into visual formats such as bar graphs, line charts, pie charts, scatter plots, and more. By visually presenting data, these charts enable organizations to identify patterns, trends, and correlations that might be difficult to recognize with raw data alone.
Data visualization charts facilitate effective communication and decision-making within an organization by presenting data clearly and concisely. They allow stakeholders to quickly grasp key insights, make informed decisions, and communicate findings to others. Additionally, interactive data visualization tools empower users to explore data dynamically, enabling deeper analysis and discovery of hidden insights. Overall, data visualization charts enhance data-driven decision-making, promote understanding, and foster better communication within organizations.
Use a column chart to compare various items or to compare various items across time, an event, or a milestone. This format could be used to display the revenue per landing page or the number of clients by the closing date.
Best Case Scenarios for Column Chart
Bar graphs and column charts can both be used to show data changes, however, column charts work best for negative data. Of course, the fundamental distinction is that while bar graphs display data horizontally, column charts do it vertically.
For instance, warehouses frequently keep track of how many accidents occur on the shop floor. A column chart can help presenters better understand the change in the number of incidences when it drops below the monthly average.
This column chart illustrates the number of clients by close date in the aforementioned example. It is simple to see how data evolves with column charts. This indicates that they have a variety of use cases, such as:
A lollipop chart is a combination of a column chart and a scatter plot. It represents data using a vertical line (the "stick") and a circle or dot (the "lollipop") placed at the end of the line. Lollipop charts are particularly useful for highlighting individual data points or comparing values within categories.
Best Case Scenarios for Lollipop Chart
Let's say you want to compare the sales performance of different products in a particular month. You can create a lollipop chart where the vertical sticks represent the sales values, and the lollipops at the end of each stick indicate the respective products. The length of the sticks would represent the sales figures, and the color or size of the lollipops can denote additional information like product categories or profitability. By using clear labels and tooltips, you can provide viewers with specific sales data for each product, enabling them to analyze and interpret the chart easily.
A dual-axis chart is a visual representation that displays two different sets of data on separate y-axes. It allows for easy comparison and analysis of the relationships between two variables that may have different scales or units of measurement. The hovering effect in a dual-axis chart enhances interactivity by providing additional information when the user hovers their cursor over a data point. This effect can be achieved using interactive charting libraries or tools, enabling the display of tooltips, pop-ups, or dynamic labels that show specific values for both axes. The hovering effect enhances the user experience, allowing for a more detailed exploration and understanding of the data presented in the dual-axis chart.
Best Case Scenarios for Dual Axis Chart
A real-life example of a dual-axis chart can be seen in the context of tracking stock market data. Suppose you want to analyze the performance of a particular company's stock price and its corresponding trading volume over time. On the chart, the primary y-axis (left side) could represent the stock price in dollars, and the secondary y-axis (right side) could represent the trading volume in millions of shares.
The x-axis would represent the timeline, usually in months or years. With this dual-axis chart, you can visually compare the stock price movements and trading volume side by side. It allows you to observe any potential correlations or patterns between the two variables. For example, you might notice that spikes in trading volume coincide with significant changes in stock price, indicating higher market activity during those periods.
By utilizing a dual-axis chart, you can effectively present and analyze multiple datasets that have different scales or units of measurement, enabling a comprehensive understanding of the relationship between them.
An effective technique for visualising data in a circular manner is the pie chart. They are particularly effective in illustrating the composition or distribution of a whole, showcasing the proportions of different categories or variables. A standard pie chart, also known as a 2D pie chart, uses a flat, two-dimensional circle to display data. The circle is divided into slices, with each slice representing a specific category or variable.
The size of each slice corresponds to the proportion or percentage it represents in to the whole. In contrast, a 3D pie chart adds a third dimension by using depth to create a visual effect. The slices of a 3D pie chart appear to be lifted or pushed out of the circle, giving a sense of depth and perspective.
While the 3D effect may enhance the visual appeal, it can sometimes distort the perception of the data, as the size of the slices can be challenging to accurately interpret. Therefore, 2D pie charts are generally considered more precise and commonly used for data analysis and presentations.
Best Case Scenarios for Pie Chart
A real-life example of a pie chart could be the breakdown of expenses in a household budget. Let's say a family wants to analyze their monthly expenses to determine how their income is allocated. They collect data for different expense categories such as housing, transportation, groceries, entertainment, and miscellaneous.
Using this data, they can create a pie chart to visually represent the proportions of each expense category. The chart would consist of slices, with each slice representing a specific expense category and its corresponding percentage of the total expenses. For instance, if housing expenses account for 30% of the total, the housing slice in the pie chart would be larger compared to other categories.
By looking at the pie chart, the family can quickly identify which expense categories consume a significant portion of their budget and make informed decisions about where to potentially cut back or allocate more resources. It provides a clear visual representation of the distribution of expenses, making it easier to understand and analyze the data.
An area chart displays data as a series of areas, with each area representing a category's value over time. The areas are stacked on top of each other, emphasizing the cumulative total. Area charts are useful for visualizing data progression and comparing trends between multiple categories.
Best Case Scenarios for Area Chart
A real-life example of an area chart could be the representation of monthly sales data for a retail store over a year. Each product category is assigned a different colored area, and the height of the area at any given point represents the cumulative sales for that category up to that month. This visualization helps identify trends, compare the performance of different categories, and make informed decisions regarding inventory management and marketing strategies.
A stacked bar chart in data visualization uses horizontal or vertical bars to represent multiple categories, stacked on top of one another. Each bar segment represents a category's contribution to the whole. Stacked bar charts are effective in illustrating the total and relative distribution of data across categories.
Best Case Scenarios for Stacked Bar Chart
One real-life example of a stacked bar chart is the breakdown of energy sources in a country's energy consumption. For instance, it can show the proportion of energy derived from coal, natural gas, renewable sources, nuclear energy, and petroleum. The length of each bar represents the total energy consumption, while the segments within the bar depict the contribution of each energy source.
By visualizing the data in this way, the stacked bar chart provides a clear and concise overview of how different energy sources contribute to the overall energy mix. This information can be valuable for policymakers, researchers, and the general public to understand the distribution of energy usage and identify trends or shifts in the energy sector.
A Marimekko chart, also known as a mosaic chart or a variable-width column chart, represents categorical data using rectangular bars of varying widths and heights. The width of each bar represents the proportion of data within a category, while the height represents a secondary variable. Marimekko charts provide a visual representation of the relationship between two categorical variables.
Best Case Scenarios for Marimekko Chart
Marimekko charts are commonly used in market analysis, sales and marketing, product management, and other business contexts where it is essential to understand the distribution and composition of data across multiple dimensions. They provide a visual snapshot of how variables intersect and interact, enabling data-driven decision-making and identifying areas for improvement or optimization.
By leveraging the power of visualization, Marimekko charts help stakeholders grasp the relative importance of different categories and make informed choices based on data insights.
A scatter plot chart displays the relationship between two numerical variables. Each data point is represented by a dot or marker on a 2D or a 3D grid[GU7][GU8], plotting one variable on the x-axis and the other on the y-axis. Scatter plots are useful for identifying correlations, clusters, outliers, and trends within data.
Best Case Scenarios for Scatter Plot Chart Chart
One real-life example is examining the correlation between a person's age and income. By plotting individuals' age on the x-axis and income on the y-axis, patterns, and trends can be observed. This information can be valuable for financial planning, retirement considerations, or career development.
A bubble chart extends the concept of a scatter plot by adding a third dimension. In addition to the x and y variables, a third numerical variable is represented by the size of the bubbles. Each bubble's position indicates the values of the two variables, while its size represents the third variable. Data Visualization Bubble charts are effective for displaying relationships between three variables simultaneously.
Best Case Scenarios for Bubble Chart
A bubble chart can be used to display the energy consumption, carbon emissions, and population of different countries. The x-axis represents energy consumption, the y-axis represents carbon emissions, and the size of the bubbles represents the population of each country. This visualization allows for quick comparisons and insights into how energy consumption and carbon emissions relate to population size.
By examining the bubble chart, patterns and outliers can be identified, helping policymakers and researchers make informed decisions about energy policies and environmental sustainability. The chart provides a visual representation of data, making it easier to understand complex relationships and spot trends.
A funnel chart illustrates the progressive reduction of data or stages in a process. The chart consists of a series of trapezoidal or conical shapes stacked on top of one another, with each shape representing a stage or category. Funnel charts are commonly used in sales and marketing to visualize customer conversion rates or the sales pipeline.
Best Case Scenarios for Funnel Chart
Funnel charts are commonly used in business and marketing to analyze various processes, such as sales pipelines, customer conversion rates, website traffic, or lead generation. They help identify bottlenecks, track the progression of data, and highlight areas that require attention or optimization.
By visually presenting data in a funnel chart, it becomes easier to understand the drop-off rates or conversion rates at different stages. This allows businesses to make data-driven decisions, allocate resources effectively, and improve overall efficiency. Funnel charts are a valuable tool for businesses seeking to streamline processes and enhance their performance.
A waterfall chart demonstrates the gradual rise or fall of a starting value to a final value. It visually represents positive and negative contributions, displaying the cumulative effect of each contribution. Waterfall charts are useful for analyzing financial data, budgeting, and highlighting the components of a total value.
Best Case Scenarios for Waterfall Chart
It is commonly used in situations where there is a sequential flow or progression involved. In real life, a waterfall chart can be used to analyze changes in a company's cash flow, project management, sales performance, or budget allocation.
Presenting a step-by-step breakdown of factors, helps stakeholders understand the contributions of each element and how they impact the outcome. This visual tool simplifies complex data and enhances decision-making by providing a clear and concise representation of the flow and impact of various factors.
A bullet chart provides a compact visualization of performance against a target or comparative measure. It combines a bar representing the actual value, a reference line indicating the target or benchmark, and additional markers or bands to provide context. Bullet charts are often used in dashboards and scorecards to represent key performance indicators (KPIs).
Best Case Scenarios for Bullet Chart
It is commonly used in performance management systems to assess and compare performance across different categories or individuals. The chart consists of a horizontal line indicating a target or desired performance level, and a vertical bar that represents the actual performance. The bar is divided into segments to indicate different performance levels, such as poor, satisfactory, and excellent.
By plotting the actual performance on the chart, managers can quickly identify areas of success or improvement. Bullet charts offer a visual and intuitive way to communicate data and facilitate data-driven discussions and decision-making.
A matrix chart displays data in a grid-like structure, with rows and columns representing different variables or categories. The intersection points between rows and columns contain data points, which can be represented using colors or numerical values. Matrix charts are effective for visualizing relationships and comparisons between multiple categories.
Best Case Scenarios for Matrix Chart
Matrix charts are widely used in various fields, including project management, organizational planning, decision-making, and data analysis. They provide a concise and easy-to-understand format for presenting complex information.
For example, in project management, a matrix chart can be used to assign resources to different tasks or projects. It helps project managers visualize resource allocation, identify gaps or overlaps, and make informed decisions regarding resource distribution.
In organizational planning, a matrix chart can depict the reporting structure within a company, showing lines of authority and responsibility. It can help clarify roles and relationships, facilitate communication, and improve overall coordination. In data analysis, a matrix chart can be utilized to present correlations or comparisons between multiple variables. It allows researchers or analysts to identify patterns, trends, or dependencies in the data.
Overall, matrix charts are versatile tools that aid in organizing, analyzing, and presenting information in a structured manner. They enable efficient decision-making, improve communication, and enhance understanding of complex relationships.
A table chart presents data in a structured tabular format, with rows and columns representing different variables or categories. It is particularly useful for presenting precise numerical values and facilitating direct comparisons between data points. Table charts are commonly used for data exploration, reporting, and data summarization.
Best Case Scenarios for Table Chart
| Country | Population |
| China | 1,409 |
| India | 1,366 |
| United States | 332 |
| Indonesia | 276 |
| Pakistan | 225 |
This table chart showcases the populations of five countries: China, India, the United States, Indonesia, and Pakistan. The "Country" column lists the names of the countries, while the "Population" column displays the population of each country in millions. The table chart provides a clear overview of population sizes, allowing for easy comparison between the different countries.
A donut chart resembles a pie chart in terms of data visualisation, but it has a hole in the middle, giving it a ring-like look. It represents proportions or percentages of a whole, with each slice's size proportional to the value it represents. Donut charts are useful for displaying data composition while providing space for additional information or annotations in the center.
Best Case Scenarios for Donut Chart
A real-life example of a donut chart is the breakdown of a company's revenue by product category. Let's say you have a company that sells electronic devices, including smartphones, tablets, and laptops. You can use a donut chart to visually represent the percentage of total revenue generated by each product category.
Below is an example breakdown:
In this case, the donut chart would have three segments, each representing a product category. The size of each segment would be proportional to the percentage of revenue generated by that category. The chart would have a hollow centre, giving it the appearance of a donut.
This visualization allows stakeholders, such as executives or investors, to quickly understand the revenue distribution across different product categories and identify the most significant contributors. It provides an intuitive and visually appealing representation of the data, making it easier to grasp the proportions and make comparisons between the categories.
Data visualization charts play a crucial role in effectively communicating and understanding data. To emphasize their significance, consider the following main reasons:
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Data visualization charts are invaluable tools for conveying complex information in a visual and easily understandable format. They transform raw data into meaningful representations, enabling users to identify patterns and make informed decisions. These charts enhance data comprehension through the use of visual elements like color and shape.
They promote effective communication, making it easier to share findings and collaborate. With various chart types available, customization is possible to meet specific needs. As the demand for data-driven decision-making grows, data visualization charts will continue to drive innovation across industries. Obtaining KnowledgeHut's BI & Visualization certification will help you turn data into opportunities with BI and visualization and get job ready.
The choice of charts for data visualization depends on the type of data and the insights you want to convey. Commonly used charts include bar charts, line charts, pie charts, scatter plots, and heat maps, among others.
Types of charts in data visualization are Bar Chart, Line Chart, Pie Chart, and Scatter Plot.
The roles of charts in data visualization are to visually represent data, enable data comparison and trend analysis, illustrate data relationships, summarize information, aid decision-making, enhance storytelling, facilitate exploration and discovery, support communication and collaboration, and improve presentation and reporting.
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