Best Data Visualization Techniques to Analyze Data

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?

Table of Content:

1. What is Data Visualization?
2. Data Visualization Techniques with Examples
3. Create Data Visualizations with Data Visualization Tools
4. Why is Data Visualization Important?
5. Wrap Up

Let’s get started.

What is Data Visualization?

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:

• Scientific research.
• Financial analysis.
• Business intelligence.
• Data analytics.

Data Visualization Techniques with Examples

1. Sankey Chart

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.

Example

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.

2. Treemap

A Treemap displays hierarchical data in a rectangular layout. It uses nested rectangles to represent different parts of the data.

• The larger rectangles represent larger parts of the data.
• The smaller rectangles represent smaller parts of the data.

Each rectangle also has a color associated with it. You can use this color to indicate the rectangle’s category.

Example

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.

3. Word Cloud

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.

Example

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.

4. Pareto Chart

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.

Example

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.

5. Comparison Bar Chart

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.

Example

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.

6. Scatter Plot

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.

Example

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.

7. Likert Scale Chart

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.”

Example

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.

8. Crosstab Chart

A Crosstab Chart is a hierarchical data visualization method.

Example

Let’s use the social media traffic data below to illustrate a Crosstab Chart.

Social Networks	Browsers	Conversions
Pinterest	Chrome	40.79
Pinterest	Safari	36.99
Pinterest	Firefox	25.83
Pinterest	Edge	15.29
Pinterest	Android Webview	30
Slideshare	Chrome	22.59
Slideshare	Firefox	14.4
Slideshare	Edge	6.93
Slideshare	Android Webview	16.87
Twitter	Chrome	56.84
Twitter	Firefox	13.5
Twitter	Safari	48.02
Twitter	Android Webview	44.5
LinkedIn	Chrome	52.11
LinkedIn	Android Webview	14
LinkedIn	Edge	9.9
LinkedIn	Firefox	13.45
LinkedIn	Safari	30.53
Quora	Safari	20.42
Quora	Edge	14.05
Quora	Android Webview	19.72
Quora	Chrome	46.25
Quora	Firefox	10.59
Instagram	Safari	25.63
Instagram	Android Webview	25.73
Instagram	Firefox	15.72
Instagram	Chrome	27.01
Reddit	Chrome	21.71
Reddit	Android Webview	16.33
Reddit	Safari	20.77
Reddit	Firefox	16.3
Reddit	Edge	3.3
Facebook	Firefox	24.59
Facebook	Safari	34.5
Facebook	Chrome	69.47
Facebook	Edge	13.25
Facebook	Android Webview	37.61

This is the Crosstab visualization of the data.

9. Multi Axis Line Chart

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.

Example

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.

10. Radar Chart

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.

Example

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

Create Data Visualizations with Data Visualization Tools

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 provides many customization options. You can modify the appearance of the visualization and add annotations to explain the data further. This level of customization enables the creation of charts personalized to your unique needs and preferences.
• ChartExpo has an intuitive user interface. You only need a few clicks to complete the task at hand. In addition, it contains guides and tooltips that provide further information about the features and functionalities.
• ChartExpo also offers a 7-day free trial to try the platform before committing to a subscription. In addition, it has an affordable monthly plan of $10. Thus, it is the perfect choice for creating custom and insightful data visualizations without breaking the bank.

How to Install ChartExpo in Excel?

1. Open your Excel application.
2. Open the worksheet and click on the “Insert” menu.
3. You’ll see the “My Apps“.
4. In the office Add-ins window, click “Store” and search for ChartExpo on my Apps Store.
5. Click on the “Add” button to install ChartExpo in your Excel.

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.

How to Create Data Visualization in Excel?

Let’s use the Radar Chart example to learn how to create data visualizations in Excel with ChartExpo.

• To get started with ChartExpo, install ChartExpo in Excel.
• Now Click on My Apps from the INSERT menu.

• Choose ChartExpo from My Apps, then click Insert.

• Once ChartExpo is loaded. Click on “Radar Chart” from the list of charts.

• Click “Create Chart From Selection” button after selecting the data from the sheet, as shown.

• The Radar Chart will look like as follows.

• If you want to have the title of chart, click on Edit Chart, as shown in the above image.
• To change the title of the chart, click on the pencil icon that is available very next to Chart Header.
• It will open the properties dialog. Under the Text section, you can add a heading in Line 1 and enable the Show Give the appropriate title of your chart and click on Apply button.
• For saving changes click on Save Changes. This will persist the changes.

• To enlarge the size of the dots in the Radar Chart, click the “Settings” button. Expand the “Curve Point” property and toggle the “Show” option.
• Click the “Apply” button to keep the modifications.

• The final chart will look like as below.

Insights

• Garment orders have been most significant in November.
• Electronics have been the top-selling product throughout the year, except for March and August.
• Cosmetics have a better performance than Garments. Cosmetics outperformed in February and had the fewest orders placed in March.

Why Is Data Visualization Important?

• Improved Decision-Making

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.

• Increased Efficiency

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.

• Improved Communication

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.

• Increased Engagement

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.

FAQs:

What is data visualization?

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.

What are data visualization tools?

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.

What is the best data visualization technique?

The best data visualization technique is creating Excel charts and graphs with ChartExpo.

1. Take into account the purpose and context of the project.
2. Choose the appropriate chart or graph type.
3. Design it to convey the data to the target audience appropriately.

Wrap Up

Data visualization is a powerful tool that helps businesses and organizations with the following;

• Improved decision-making.
• Improved communication.
• Efficient engagement with the audiences.

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.