Data visualization involves converting raw data into visual representations like charts and graphs. This eases the identification of patterns, trends, and correlations that would otherwise be difficult to spot.
Data visualization techniques have become an essential part of every business and industry. They help to present data appealingly for ease of analysis and communication.
Let’s say you are trying to discover your client’s popular product. All you need is a visualization like a chart or a graph. It can help you compare sales and determine the top-selling item.
This is just one of the data visualization techniques numerous applications. You can use it for a wide variety of data analysis tasks. Tasks like analyzing customer data, sales trends, financial data, etc.
What are data visualization techniques?
Let’s get started.
Data visualization transforms raw data into visual representations such as graphs and charts. It enables quick identification of large data sets’ trends, relationships, and patterns.
Data visualization is essential for researchers, analysts, and business professionals. It helps you draw insights from the said data. Consequently, use them for informed decision-making and developing strategies.
Data visualization techniques are applicable in a variety of ways, such as:
A Sankey Chart visualizes the flow of data between multiple entities. It illustrates the size, amount, and direction disparities across distinct data categories.
Sankey Charts comprise a series of interconnected rectangles, each representing a distinct data category. Arrows connecting the rectangles indicate the direction of the data flow between the categories.
Let’s say you have the data below. You can map it on a Sankey Chart to ease the gleaning of insights.
Locations | Revenue | Profit & Cost | Details | Amount |
North America | Revenue | Expenses | Cost of Sales | 109310 |
North America | Revenue | Expenses | Salaries | 28278 |
North America | Revenue | Expenses | Cost of Marketing | 76772 |
North America | Revenue | Profit | Tax | 147231 |
North America | Revenue | Profit | Profit After Tax | 713117 |
Asia | Revenue | Expenses | Cost of Sales | 122371 |
Asia | Revenue | Expenses | Salaries | 127010 |
Asia | Revenue | Expenses | Cost of Marketing | 72919 |
Asia | Revenue | Profit | Tax | 161953 |
Asia | Revenue | Profit | Profit After Tax | 692948 |
Middle East | Revenue | Expenses | Cost of Sales | 153080 |
Middle East | Revenue | Expenses | Salaries | 93339 |
Middle East | Revenue | Expenses | Cost of Marketing | 182517 |
Middle East | Revenue | Profit | Tax | 78101 |
Middle East | Revenue | Profit | Profit After Tax | 453762 |
Below is the Sankey Chart representation of the data above.
A Treemap displays hierarchical data in a rectangular layout. It uses nested rectangles to represent different parts of the data.
Each rectangle also has a color associated with it. You can use this color to indicate the rectangle’s category.
Let’s map the restaurant order data below on a Treemap.
Food Items | Category of Items | No. of Orders |
Salads | Classic Greek Salad | 80 |
Salads | Pad Thai Salad | 70 |
Salads | Green Goddess Salad | 100 |
Salads | Fruity Pasta Salad | 50 |
Salads | Bulgur Wheat Salad | 40 |
Salads | New Potato Salad | 35 |
Salads | Garlicky Tomato Salad | 45 |
Salads | Apple and Sprout Salad | 45 |
Chicken Dishes | Greek Chicken with beans | 50 |
Chicken Dishes | Cooked Italian Chicken | 35 |
Chicken Dishes | Chicken Stroganoff | 70 |
Chicken Dishes | Chicken Rice | 60 |
Chicken Dishes | Thai Chicken Thighs | 85 |
Chicken Dishes | Cajun Chicken Lasagna | 30 |
Chicken Dishes | Chicken Stew | 90 |
Chicken Dishes | Smoky Spanish Chicken | 50 |
Mutton Dishes | Kabab | 45 |
Mutton Dishes | Mutton Stew | 30 |
Mutton Dishes | Minced Spring Rolls | 50 |
Mutton Dishes | Steaks | 25 |
Mutton Dishes | Mint Roasted | 40 |
Mutton Dishes | Chanfana | 70 |
Mutton Dishes | Navarin | 20 |
Mutton Dishes | Mutton Satay | 50 |
From the visualization below, you can assess food category performances at a glance.
A Word Cloud is a visual representation of data that conveys meaning using words or phrases. It indicates the frequency of words and phrases in a text.
Word Clouds place words or phrases in order of frequency. The more frequent words appear larger and more prominent in the cloud. You can use colors and fonts further to emphasize important words or phrases within the cloud.
Assume you have orders from different cities, as shown below.
Cities | No. of Orders |
New York | 30343 |
Los Angeles | 14203 |
Chicago | 18563 |
Houston | 10902 |
Phoenix | 6295 |
Philadelphia | 8294 |
San Antonio | 713 |
San Diego | 2581 |
Dallas | 2423 |
San Jose | 24197 |
Austin | 13235 |
Jacksonville | 1860 |
Fort Worth | 11977 |
Columbus | 800 |
Charlotte | 4121 |
San Francisco | 41907 |
Indianapolis | 2768 |
Seattle | 16954 |
Denver | 9305 |
Washington | 670 |
Boston | 13200 |
El Paso | 20504 |
Nashville-Davidson | 23383 |
Detroit | 10108 |
Oklahoma City | 10755 |
Portland | 12213 |
Las Vegas | 24755 |
Memphis | 1539 |
Louisville | 15800 |
Baltimore | 4300 |
Milwaukee | 1935 |
Albuquerque | 8136 |
Tucson | 20762 |
Fresno | 3104 |
Mesa | 18367 |
Sacramento | 11069 |
Atlanta | 5987 |
Kansas City | 12998 |
Colorado Springs | 7268 |
Omaha | 19422 |
Raleigh | 2066 |
Miami | 8576 |
Long Beach | 235 |
Virginia Beach | 16860 |
Oakland | 955 |
Minneapolis | 2619 |
Tulsa | 24412 |
Tampa | 21184 |
Arlington | 8846 |
New Orleans | 16273 |
As shown below, you can use a Word Cloud to present the data predictably.
A Pareto Chart illustrates the relative significance of several data points. It depicts the 80/20 rule visually. According to this rule, 80% of the outcome results from 20% of the input.
This chart organizes data into classes or categories. Then plots a cumulative line graph of the relative frequency of each class or category.
Pareto Charts help to identify areas of improvement. They effectively show which items contribute the most to a problem or need attention.
This is data on sales from the current and previous years.
City | Current | Previous |
New York | 540 | 510 |
Chicago | 550 | 545 |
San Francisco | 415 | 399 |
Los Angeles | 572 | 533 |
Seattle | 193 | 185 |
Boston | 188 | 163 |
Phoenix | 497 | 485 |
Atlanta | 215 | 180 |
Philadelphia | 489 | 470 |
Miami | 387 | 267 |
Dallas | 7 | 5 |
Houston | 5 | 3 |
St Louis | 7 | 4 |
Columbus | 1 | 1 |
Fresno | 6 | 3 |
Mesa | 2 | 2 |
Oakland | 3 | 1 |
San Antonio | 1 | 2 |
San Diego | 1 | 1 |
San Jose | 2 | 3 |
Austin | 5 | 3 |
Jacksonville | 3 | 2 |
Fort Worth | 3 | 3 |
Charlotte | 2 | 1 |
Indianapolis | 1 | 2 |
Denver | 4 | 3 |
Washington | 5 | 2 |
El Paso | 7 | 5 |
Nashville | 5 | 3 |
Detroit | 3 | 2 |
Oklahoma City | 2 | 1 |
Portland | 2 | 3 |
Las Vegas | 3 | 2 |
Memphis | 3 | 1 |
Louisville | 4 | 1 |
Baltimore | 5 | 3 |
Milwaukee | 1 | 1 |
Albuquerque | 2 | 1 |
Tucson | 1 | 1 |
Sacramento | 4 | 3 |
Kansas City | 2 | 1 |
Colorado Springs | 4 | 3 |
Omaha | 2 | 1 |
Raleigh | 4 | 3 |
Long Beach | 2 | 1 |
Tulsa | 4 | 3 |
Tampa | 2 | 1 |
Arlington | 4 | 3 |
New Orleans | 2 | 1 |
Wichita | 4 | 3 |
Bakersfield | 2 | 1 |
Aurora | 1 | 2 |
Anaheim | 3 | 2 |
Honolulu | 1 | 3 |
Santa Ana | 3 | 2 |
Riverside | 1 | 5 |
Corpus Christi | 3 | 2 |
Lexington | 1 | 1 |
Henderson | 3 | 2 |
Stockton | 1 | 1 |
Saint Paul | 3 | 2 |
Pittsburgh | 1 | 2 |
Lincoln | 3 | 2 |
Anchorage | 1 | 1 |
Plano | 3 | 2 |
Orlando | 1 | 2 |
Irvine | 3 | 2 |
Newark | 7 | 3 |
Durham | 5 | 1 |
Chula Vista | 2 | 2 |
Toledo | 5 | 1 |
Fort Wayne | 2 | 1 |
Lubbock | 5 | 2 |
Jersey City | 2 | 2 |
Scottsdale | 5 | 3 |
Reno | 2 | 3 |
Glendale | 5 | 3 |
Norfolk | 2 | 1 |
Irving | 5 | 2 |
Garland | 1 | 2 |
Hialeah | 4 | 1 |
Richmond | 1 | 3 |
Boise | 5 | 2 |
Tacoma | 1 | 1 |
Fontana | 5 | 1 |
Birmingham | 1 | 1 |
Frisco | 2 | 1 |
Augusta | 1 | 1 |
Tempe | 2 | 1 |
Little Rock | 1 | 1 |
Overland Park | 2 | 1 |
Grand Prairie | 1 | 1 |
Ontario | 2 | 1 |
Brownsville | 1 | 1 |
Santa Rosa | 2 | 1 |
Eugene | 1 | 1 |
Lancaster | 2 | 1 |
Palmdale | 1 | 1 |
Joliet | 2 | 1 |
Midland | 1 | 1 |
Below is the Pareto Chart visualization of the data.
A Comparison Bar Chart compares two or more items’ relative sizes or values. It is a graphical representation of the differences between two or more items.
This chart plots the values of the items on separate bars. Then compares the heights of each bar to determine the difference.
Let’s use the data below to illustrate a Comparison Bar Chart.
Quarters | Items | Orders |
Q1 | HP | 923 |
Q1 | Dell | 524 |
Q1 | Apple | 607 |
Q1 | Lenovo | 814 |
Q2 | HP | 571 |
Q2 | Dell | 968 |
Q2 | Apple | 971 |
Q2 | Lenovo | 578 |
Q3 | HP | 864 |
Q3 | Dell | 552 |
Q3 | Apple | 421 |
Q3 | Lenovo | 971 |
You can see how the chart has mapped the differences in the data in the visualization below.
A Scatter Plot displays the relationship between two variables, such as age and income. It connects the data points with a line or trendline to highlight the correlations.
Let’s visualize the correlations between the data points in the store inventory and sales data below.
Product Types | Products | Sales | No. of Orders | In Stock |
Furniture | Beds | 90 | 10 | 26 |
Furniture | Cabinets | 70 | 12 | 16 |
Furniture | Chairs | 190 | 11 | 12 |
Furniture | Clocks | 870 | 16 | 21 |
Furniture | Desks | 900 | 25 | 25 |
Furniture | Tables | 600 | 23 | 23 |
Furniture | Chests | 600 | 42 | 38 |
Furniture | Seating | 1200 | 18 | 43 |
Stationary | Staplers | 590 | 38 | 32 |
Stationary | Sticky Tapes | 390 | 11 | 35 |
Stationary | Scissors | 590 | 41 | 22 |
Stationary | Desk Tidy | 390 | 18 | 40 |
Stationary | Pen Cups | 260 | 15 | 42 |
Stationary | Paper Clip | 210 | 2 | 19 |
Stationary | Stapler Pins | 170 | 23 | 44 |
Stationary | Pencils Box | 270 | 13 | 25 |
Grocery | Bakery | 140 | 26 | 21 |
Grocery | Bread | 110 | 13 | 40 |
Grocery | Seafood | 310 | 12 | 38 |
Grocery | Pasta | 760 | 6 | 35 |
Grocery | Rice | 1500 | 7 | 38 |
Grocery | Cheese | 1100 | 19 | 39 |
Grocery | Eggs | 150 | 12 | 25 |
Grocery | Oils | 280 | 14 | 15 |
Below is the Scatter Plot visualization of the data.
A Likert Scale Chart measures views or attitudes. It is a graphical representation of survey question responses. It plots survey responses on a continuum from “strongly agree” to “strongly disagree.”
Assume you have the following website and product survey data.
Questions | Scale | Responses |
How satisfied are you with the product descriptions? | 1 | 205 |
How satisfied are you with the product descriptions? | 2 | 214 |
How satisfied are you with the product descriptions? | 3 | 150 |
How satisfied are you with the product descriptions? | 4 | 375 |
How satisfied are you with the product descriptions? | 5 | 927 |
How satisfied are you with the product descriptions? | 6 | 790 |
How satisfied are you with the product descriptions? | 7 | 996 |
How satisfied are you with the ease of website navigation? | 1 | 118 |
How satisfied are you with the ease of website navigation? | 2 | 116 |
How satisfied are you with the ease of website navigation? | 3 | 122 |
How satisfied are you with the ease of website navigation? | 4 | 433 |
How satisfied are you with the ease of website navigation? | 5 | 864 |
How satisfied are you with the ease of website navigation? | 6 | 720 |
How satisfied are you with the ease of website navigation? | 7 | 959 |
How satisfied are you with the quality of our product? | 1 | 184 |
How satisfied are you with the quality of our product? | 2 | 144 |
How satisfied are you with the quality of our product? | 3 | 160 |
How satisfied are you with the quality of our product? | 4 | 322 |
How satisfied are you with the quality of our product? | 5 | 620 |
How satisfied are you with the quality of our product? | 6 | 793 |
How satisfied are you with the quality of our product? | 7 | 916 |
How satisfied are you with our delivery service? | 1 | 158 |
How satisfied are you with our delivery service? | 2 | 206 |
How satisfied are you with our delivery service? | 3 | 111 |
How satisfied are you with our delivery service? | 4 | 375 |
How satisfied are you with our delivery service? | 5 | 665 |
How satisfied are you with our delivery service? | 6 | 669 |
How satisfied are you with our delivery service? | 7 | 808 |
You can present the data in a Likert Scale Chart, as shown below.
A Crosstab Chart is a hierarchical data visualization method.
Let’s use the social media traffic data below to illustrate a Crosstab Chart.
Social Networks | Browsers | Conversions |
Chrome | 40.79 | |
Safari | 36.99 | |
Firefox | 25.83 | |
Edge | 15.29 | |
Android Webview | 30 | |
Slideshare | Chrome | 22.59 |
Slideshare | Firefox | 14.4 |
Slideshare | Edge | 6.93 |
Slideshare | Android Webview | 16.87 |
Chrome | 56.84 | |
Firefox | 13.5 | |
Safari | 48.02 | |
Android Webview | 44.5 | |
Chrome | 52.11 | |
Android Webview | 14 | |
Edge | 9.9 | |
Firefox | 13.45 | |
Safari | 30.53 | |
Quora | Safari | 20.42 |
Quora | Edge | 14.05 |
Quora | Android Webview | 19.72 |
Quora | Chrome | 46.25 |
Quora | Firefox | 10.59 |
Safari | 25.63 | |
Android Webview | 25.73 | |
Firefox | 15.72 | |
Chrome | 27.01 | |
Chrome | 21.71 | |
Android Webview | 16.33 | |
Safari | 20.77 | |
Firefox | 16.3 | |
Edge | 3.3 | |
Firefox | 24.59 | |
Safari | 34.5 | |
Chrome | 69.47 | |
Edge | 13.25 | |
Android Webview | 37.61 |
This is the Crosstab visualization of the data.
A Multi Axis Line Chart uses multiple axes to display data more flexibly. It allows you to plot multiple series of data on a single chart. This visualization helps analyze data with multiple dimensions and compare measures across variables.
Let’s say you have data on orders, sales, and profits, as shown below.
Months | Orders | Sales | Profits |
Jan | 756 | 18766 | 18 |
Feb | 485 | 18788 | 29 |
Mar | 412 | 18743 | 24 |
Apr | 607 | 18788 | 22 |
May | 915 | 16406 | 19 |
Jun | 413 | 17765 | 22 |
Jul | 828 | 20532 | 26 |
Aug | 611 | 20016 | 19 |
Sep | 683 | 20122 | 18 |
Oct | 886 | 20125 | 25 |
Nov | 397 | 23783 | 21 |
Dec | 408 | 22942 | 21 |
You can plot it on a Multi Axis Line Chart with each variable having its axis to make analysis easier.
A Radar Chart is a graphical representation of data points plotted on a polar coordinate system. It plots each variable at a point on the circumference of a circle. The points are then connected by lines to form a polygon shape.
Let’s visualize the order data below on a Radar Chart.
Months | Garments | Electronics | Cosmetics |
Jan | 13147 | 32289 | 18388 |
Feb | 9047 | 29305 | 18692 |
Mar | 13493 | 9696 | 10639 |
Apr | 10260 | 24357 | 12218 |
May | 12127 | 28597 | 14936 |
Jun | 11048 | 23525 | 10915 |
Jul | 5435 | 32997 | 19854 |
Aug | 12624 | 10003 | 11609 |
Sep | 7768 | 33472 | 18657 |
Oct | 9459 | 29548 | 15701 |
Nov | 14201 | 28118 | 11120 |
Dec | 13790 | 26850 | 12802 |
Data visualization tools are software applications that render data visually in a graph or chart. This is helpful with identifying trends, conducting analyses, decision-making, and goal-setting.
There are different types of data visualization tools available. Your objectives and data types will guide you in choosing the ideal tool.
Microsoft Excel is the most widely used spreadsheet. Yet, using Excel can be challenging.
Luckily, there is ChartExpo for Excel. ChartExpo is a potent data visualization tool that allows for creating a wide variety of custom visualizations.
Here are some of the benefits of using ChartExpo;
ChartExpo charts are available both in Google Sheets and Microsoft Excel. Please use the following CTA’s to install the tool of your choice and create beautiful visualizations in a few clicks in your favorite tool.
Let’s use the Radar Chart example to learn how to create data visualizations in Excel with ChartExpo.
Data visualization facilitates quick identification of patterns and trends unclear in raw data. This allows for faster and more accurate analysis, which leads to informed decision-making.
Furthermore, data visualization provides a visual representation of data that is easier to understand and remember. This facilitates quick gleaning of insights and effective decision-making.
You can easily spot areas necessitating improvements and take steps to address them. This makes it easier to develop solutions quickly, increasing the entire process’s efficiency.
Data visualization improves communication by making data easier to understand and share across teams and departments. It can also help bridge any gaps between technical and non-technical departments. Its visual nature makes it accessible to everyone.
Users are more likely to pay attention to the information as it is more interesting and engaging. Furthermore, data visualization can create an immersive experience for users. Consequently increasing their engagement and driving them to take action. Increased engagement can help with streamlining processes and improving decision-making.
Data visualization is the representation of data or information in a graphical format. It visually represents data to simplify analysis and communicate insights more effectively.
Data visualization tools are programs for making visual representations of data. They facilitate the transformation of all sorts of data into readable charts and graphs.
The best data visualization technique is creating Excel charts and graphs with ChartExpo.
Data visualization is a powerful tool that helps businesses and organizations with the following;
We have outlined the value of data visualization along with the most effective data visualization techniques.
You can effectively organize your data by using the correct data visualization tool. Choosing the ideal data visualization technique is, therefore, essential.
Microsoft Excel is the most popular data visualization tool, yet gleaning insights can be challenging.
Then comes ChartExpo.
ChartExpo is an excellent Excel add-in to use for your data visualization quest.
It has features that make customizing and data visualization simple. ChartExpo is the best tool for producing meaningful visualizations thanks to its intuitive user interface and affordability.