Kworld Trend / Data visualization techniques for research findings, There is a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.
Data visualization techniques for research findings
Becoming proficient in common data visualization techniques can help you reap the rewards of data-driven decision-making, including increased confidence and potential cost savings. Learning how to visualize data effectively may be the first step towards using data analytics and data science to your advantage to add value to your organization.
Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, plus tips to help you present your data effectively.
Data visualization is the process of converting textual information into graphical and explanatory representations. It is essential to think beyond the numbers to gain a comprehensive and comprehensive understanding of the research data. Hence, this technology has been adopted to help presenters communicate relevant research data in a manner that is easy for the viewer to interpret and draw conclusions.
What is data visualization?
Data visualization is the process of creating graphic representations of information. This process helps the presenter communicate the data in a way that is easy for the viewer to interpret and draw conclusions from.
There are many different approaches and tools that you can take advantage of to visualize data, so you’ll want to know which ones to use and when. Here are some of the most important data visualization techniques that all professionals should know.
Data visualization techniques
The type of data visualization technology you leverage will vary based on the type of data you’re working with, as well as the story you’re telling your data .
Here are some important data visualization techniques to know:
- Pie chart
- bar graph
- chart
- Gantt chart
- heat map
- Box and longitudinal plot
- Waterfall outline
- Area chart
- Scatter plot
- chart symbol
- timetable
- Highlight table
- Dot graph
- Choropleth map
- word cloud
- Network diagram
- correlation matrices
1. Pie chart
Pie charts are one of the most popular and basic data visualization techniques, used across a wide range of applications. The Pie charts are ideal for illustrating proportions or partial comparisons.
Because pie charts are relatively simple and easy to read, they are best suited for audiences who may not be familiar with the information or are only interested in the main takeaways. For viewers who need a more comprehensive explanation of the data, pie charts fall short in their ability to display complex information.
2. Bar chart
The classic bar chart , or bar graph, is another popular and easy-to-use way to visualize data. In this type of visualization, one axis of the graph shows the categories being compared, while the other axis shows a measured value. The bar length indicates how each group measures according to the value.
One drawback is that labeling and legibility can become problematic when there are many categories listed. Like pie charts, they can also be very simple for more complex data sets.
3. Chart
Unlike bar charts, graphs show the distribution of data over a continuous interval or a specified period. These visualizations are useful in determining where values are concentrated, as well as where there are gaps or unusual values.
Graphs are particularly useful for showing the frequency of a particular occurrence. For example, if you wanted to view the number of clicks your website received each day over the past week, you could use a histogram. With this visualization, you can quickly identify the days when your website saw the most clicks and the least.
4. Gantt chart
Gantt charts are especially popular in project management, because they are useful for showing a project timeline or task progress. In this type of chart, tasks to be performed are listed on the vertical axis and time periods on the horizontal axis. The horizontal bars in the body of the chart represent the duration of each activity.
Using Gantt charts to display schedules can be incredibly useful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, becoming familiar with Gantt charts can help you stay organized.
5. Heat map
A heat map is a type of visualization used to show differences in data through differences in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. A clear legend is necessary for the user to be able to read and interpret the heat representation map successfully.
There are many possible applications for heat maps. For example, if you want to analyze what time of day a retail store makes the most sales, you can use a heat map that displays the day of the week on the vertical axis and the time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of the day, you can identify trends in the data that allow you to pinpoint the exact times your store experiences the most sales.
6. Box and drinker plot
A box-and-filament chart , or box chart, provides a visual summary of the data through its own quartiles. First, a box is plotted from the first to the third quadrant of the data set. The line inside the square represents the median. Then the “whiskers” or lines extending from the box to the minimum (lower end) and maximum (upper end) are drawn. Outliers are represented by individual points that align with the whiskers.
This type of chart is useful for quickly identifying whether the data is symmetric or skewed, as well as providing a visual summary of the data set that can be easily interpreted. Data visualization techniques for research findings
7. Waterfall outline
A waterfall chart is a visual representation of how a value changes when it is affected by various factors, such as time. The main goal of this graph is to show the viewer how the value has grown or decreased over a specific period. For example, waterfall charts are popular for showing spending or earnings over time.
8. Area chart
An area chart , or area graph, is a variation of a basic line graph in which the area below the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.
This data visualization method is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to form a whole. Stacked area charts are effective in showing partial comparisons.
9. Scatter plot
Another commonly used way to display data is a scatter plot . A scatter plot displays data for two variables as represented by dots plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in the data.
Scatter charts are more effective for somewhat larger data sets, because it is often easier to spot trends when there are more data points. In addition, the closer the data points are together, the stronger the correlation or trend.
10. Graph symbol
Diagrams or picture charts are especially useful for presenting simple data in a more visual and attractive way. These charts use icons to visualize the data, with each icon representing a different value or category. For example, data related to time may be represented by icons of clocks or clocks. Each symbol can correspond to either one unit or a specific number of units (eg each symbol represents 100 units).
In addition to making data more attractive, schematic diagrams are useful in situations where language or cultural differences may be a barrier to an audience’s understanding of the data.
11. Schedule
Timelines are the most effective way to visualize the sequence of events in chronological order. They are usually linear, with major events located along an axis. Timelines are used to communicate information about time and to display historical data.
Timelines allow you to highlight the most important events that have occurred, or should occur in the future, and make it easy for the viewer to identify any patterns that appear during the selected time period. While timelines are often relatively simple line visualizations, they can be made more attractive by adding images, colors, lines, and decorative shapes.
12. Table highlighting
The accent table is a more attractive alternative to traditional tables. By highlighting the cells in your table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visuals are useful for comparing categorical data.
Depending on the data visualization tool you use, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For example, when using a feature table to visualize a company’s sales data, you can color the cells red if the sales data is below the target, or green if the sales are above the target. Unlike a heat map, the colors in a discrimination table are discrete and represent a single meaning or value.
13. Dot graph
A dot graph is a form of bar graph that can serve as an alternative to a dashboard metrics to represent performance data. The primary use of a dot graph is to inform the viewer of how a business is performing against established benchmarks for key business metrics.
In a bullet graph, the bold horizontal bar in the middle of the chart represents the actual value, while the vertical line represents the comparative or target value. If the horizontal bar crosses the vertical line, the target for that metric has been exceeded. In addition, the divided colored sections behind the horizontal bar represent the grades of the range, such as “Poor”, “Average”, or “Good”.
14. Choropleth Maps
A corrective map uses colors, shading, and other patterns to visualize numerical values across geographic areas. These visualizations use a progression of color (or shading) on a scale to distinguish high values from low.
Choropleth maps allow viewers to see how a variable changes from one area to another. A potential downside to this type of visualization is that exact numeric values are not easily accessible because the colors represent a range of values. However, some data visualization tools allow you to add interactivity to your map so that exact values can be accessed.
15. Word cloud
A word cloud , or tag cloud, is a visual representation of textual data in which word size is proportional to its frequency. The more often a given word appears in the dataset, the larger it is in the visualization. In addition to size, words often appear bolder or follow a particular color scheme depending on their frequency.
The word cloud is often used on websites and blogs to identify keywords of interest and compare differences in textual data between two sources. They are also useful when analyzing sets of qualitative data, such as the specific words consumers use to describe a product.
16. Network diagram
Network graphs are a type of data visualization that represent relationships between qualitative data points. These visualizations consist of nodes and links, also called edges. Nodes are individual data points connected to other nodes through edges, which show the relationship between multiple nodes. Data visualization techniques for research findings
There are many use cases for network diagrams, including depicting social networks, highlighting relationships between employees in an organization, or visualizing product sales across geographies.
17. Correlation Matrix
A correlation matrix is a table showing correlation coefficients between variables. Each cell represents the relationship between two variables, and the color scale is used to express whether and to what extent the variables are correlated.
Correlation matrices are useful for summarizing and finding patterns in large data sets. In business, the correlation matrix can be used to analyze how various data points about a particular product are related, such as price, ad spend, launch date, etc.
Other options for data visualization | Data visualization techniques for research findings
While the above examples are some of the most commonly used techniques, there are many other ways you can visualize data into a more effective means of communication. Some other data visualization options include:
- bubble clouds
- maps drawing
- Department opinions
- Dendrograms
- Point distribution maps
- Charts Open – High – Low – Close
- polar regions
- Radial trees
- pie charts
- Sankey chart
- Span charts
- Streamgraphs
- Treemaps
- Stack wedge charts
- violin plots
