What is Data Visualization? Importance, Benefits & Techniques

Data visualization, often asked about as “What is Data Visualization,” has emerged as a pivotal tool in decoding complex information by presenting it in a graphical format. Its significance lies in its ability to transform data into visual contexts, making patterns, correlations, and trends more accessible and understandable to individuals across various domains.

The crux of “What is Data Visualization” revolves around translating raw data into comprehensible visual representations. Through diverse charts, graphs, maps, and infographics, it simplifies intricate data sets, empowering users to grasp insights swiftly.

Table of Contents:

• What is Data Visualization?
• Why is Data Visualization important?
• What is the Purpose of data visualization?
• Advantages and disadvantages of Data Visualization:
• Exploring Data Visualization Techniques:
• Wrap Up

What is Data Visualization?

Data visualization serves as a means to visually communicate information derived from data sets. It embodies expertise by employing skilled interpretation of data and selecting appropriate visual forms to convey insights accurately. Moreover, it encompasses authority through the use of credible sources and methodologies, ensuring the accuracy and reliability of the presented information. Trustworthiness is established by transparently representing data, enabling viewers to comprehend and trust the communicated insights. In essence, data visualization plays a pivotal role in transforming complex data into accessible, understandable, and actionable visual representations that aid in analysis, decision-making, and knowledge dissemination.

Why is Data Visualization Important?

Data visualization holds immense importance due to its ability to translate complex data into visual formats like charts, graphs, and maps, making it more accessible and understandable. It allows for the identification of patterns, trends, and relationships within data that might otherwise remain hidden in raw numbers or text. By presenting information visually, data visualization enhances comprehension and aids in decision-making processes across various industries and disciplines. Moreover, it facilitates effective communication by presenting findings in a clear, concise, and engaging manner, enabling stakeholders to grasp insights quickly and make informed choices based on reliable data. Ultimately, data visualization is crucial as it empowers individuals and organizations to extract actionable insights from large datasets, driving innovation, problem-solving, and informed decision-making.

Importance of Data Visualization in Various Sectors

• Energy and Material Flow Management

Visualization plays a significant role in helping in communicating insights in the energy sector.

Check out a good data visualization example below:

Imagine you’ve been tasked by the Energy Commission of a hypothetical country to analyze their gigantic data. They want to know various details about domestic energy consumption, namely:

• The contribution of each energy-generating source
• The amount of energy lost
• The energy contribution of cleaner and greener sources
• Typical uses of energy by consumer segments (commercial versus domestic use)

The Energy Commission wants a story to use for the forthcoming launch of their 10-year Plan. We’ve used this example to spotlight the importance of data visualization.

The table below has the sample data we’ll use for the scenario above.

Note: the table below is pretty long to show you the immense power visualization, especially when handling gigantic data sets.

Apologies in advance if you find the table below weirdly long.

Energy Type	Main Source	Source type	Energy Source	Usage	End-User	Mega Watt
Agricultural waste	Bio-conversion	Solid	Thermal generation	Losses in process	Lost	5
Agricultural waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Industry	7.3
Agricultural waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – commercial	5.1
Agricultural waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – homes	3.7
Agricultural waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – commercial	4.9
Agricultural waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – homes	2
Other waste	Bio-conversion	Solid	Thermal generation	Losses in process	Lost	7.2
Other waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Industry	5.4
Other waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – commercial	6.7
Other waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – homes	4.8
Other waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – commercial	7.4
Other waste	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – homes	2.5
Marina algae	Bio-conversion	Solid	Thermal generation	Losses in process	Lost	0.7
Marina algae	Bio-conversion	Solid	Thermal generation	Electricity grid	Industry	0.5
Marina algae	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – commercial	0.9
Marina algae	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – homes	0.5
Marina algae	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – commercial	0.8
Marina algae	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – homes	0.6
Land-based bioenergy	Bio-conversion	Solid	Thermal generation	Losses in process	Lost	1.3
Land-based bioenergy	Bio-conversion	Solid	Thermal generation	Electricity grid	Industry	2.5
Land-based bioenergy	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – commercial	3.2
Land-based bioenergy	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – homes	0.7
Land-based bioenergy	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – commercial	1.4
Land-based bioenergy	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – homes	0.9
Biomass import	Bio-conversion	Solid	Thermal generation	Losses in process	Lost	0.4
Biomass import	Bio-conversion	Solid	Thermal generation	Electricity grid	Industry	0.7
Biomass import	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – commercial	0.8
Biomass import	Bio-conversion	Solid	Thermal generation	Electricity grid	Heating and cooling – homes	0.3
Biomass import	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – commercial	0.6
Biomass import	Bio-conversion	Solid	Thermal generation	Electricity grid	Lighting & appliances – homes	0.2
Nuclear reserves	Nuclear Plant	Solid	Thermal generation	Losses in process	Lost	50
Nuclear reserves	Nuclear Plant	Solid	Thermal generation	Electricity grid	Industry	13
Nuclear reserves	Nuclear Plant	Solid	Thermal generation	Electricity grid	Heating and cooling – commercial	8
Nuclear reserves	Nuclear Plant	Solid	Thermal generation	Electricity grid	Heating and cooling – homes	6
Nuclear reserves	Nuclear Plant	Solid	Thermal generation	Electricity grid	Lighting & appliances – commercial	11
Nuclear reserves	Nuclear Plant	Solid	Thermal generation	Electricity grid	Lighting & appliances – homes	4
Coal reserves	Coal	Solid	Thermal generation	Losses in process	Lost	4.7
Coal reserves	Coal	Solid	Thermal generation	Electricity grid	Industry	3.1
Coal reserves	Coal	Solid	Thermal generation	Electricity grid	Heating and cooling – commercial	4.2
Coal reserves	Coal	Solid	Thermal generation	Electricity grid	Heating and cooling – homes	0.7
Coal reserves	Coal	Solid	Thermal generation	Electricity grid	Lighting & appliances – commercial	4.8
Coal reserves	Coal	Solid	Thermal generation	Electricity grid	Lighting & appliances – homes	0.5
Gas reserves	Natural Gas	Gas	Thermal generation	Losses in process	Lost	5.1
Gas reserves	Natural Gas	Gas	Thermal generation	Electricity grid	Industry	8.4
Gas reserves	Natural Gas	Gas	Thermal generation	Electricity grid	Heating and cooling – commercial	7.9
Gas reserves	Natural Gas	Gas	Thermal generation	Electricity grid	Heating and cooling – homes	4.8
Gas reserves	Natural Gas	Gas	Thermal generation	Electricity grid	Lighting & appliances – commercial	7.3
Gas reserves	Natural Gas	Gas	Thermal generation	Electricity grid	Lighting & appliances – homes	3.5

Note the power of visualization. The super-gigantic table with energy data has been distilled into the insightful chart below.

Visualization Source: ChartExpo

So what are the insights?

A huge chunk of the country’s energy comes from nuclear sources (41%).

You can read more about energy flow visualization with Sankey diagram here.

• Education Sector

Like in the energy sector, data visualization plays a critical role in the education sector, especially in communicating actionable insights to key stakeholders.

Take a look at a good data visualization example below:

The example you’ll come across will underscore the importance of visualization in this sector.

Imagine you run a school and you want to compare the performance of three teachers, namely:

• Justin
• Tim
• Grace

Essentially, you want to use their score in various key performance indicators: knowledge, punctuality, helpfulness, effectiveness, and delivery for promotion. We’ve used this example to highlight the importance of data visualization.

Let’s use the tabular data below for the scenario above.

Teacher	Quality	Score
Justin	Knowledge	2.7
Justin	Punctual	4.6
Justin	Helpful	3.7
Justin	Effectiveness	4.9
Justin	Delivery	3.9
Tim	Knowledge	3.7
Tim	Punctual	3.2
Tim	Helpful	4.9
Tim	Effectiveness	4.1
Tim	Delivery	2.8
Grace	Knowledge	4.7
Grace	Punctual	4.5
Grace	Helpful	3.8
Grace	Effectiveness	2.5
Grace	Delivery	3.7

Check out the insightful chart below. Note how it’s easy to extract valuable insights.

For instance, none of the teachers have a good score for delivery.

Visualization Source: ChartExpo

Note we’ve used ChartExpo to generate the insightful and easy-to-interpret chart above.

Keep reading because we’ll explain how you can get this tool for free in the later sections.

You can use charts to extract insights from survey data as well. Take a look at the good data visualization example below.

Imagine you’ve just conducted a survey in a university. You want to know how the students perceive the university brand in aspects, such as mode of teaching and courses offered. We’ve used this example to spotlight the importance of data visualization.

Let’s use the table below for our scenario.

Questions	Rating	Count
How do you rate the courses taught in your university?	1	130
How do you rate the courses taught in your university?	2	1123
How do you rate the courses taught in your university?	3	1293
How do you rate the courses taught in your university?	4	1.391
How do you rate the courses taught in your university?	5	1339
How do you rate the teaching staff and their teaching method?	1	140
How do you rate the teaching staff and their teaching method?	2	1168
How do you rate the teaching staff and their teaching method?	3	1242
How do you rate the teaching staff and their teaching method?	4	1286
How do you rate the teaching staff and their teaching method?	5	1302
How do you rate the facilities provided by the university?	1	120
How do you rate the facilities provided by the university?	2	1203
How do you rate the facilities provided by the university?	3	1212
How do you rate the facilities provided by the university?	4	1351
How do you rate the facilities provided by the university?	5	1424
How do you rate the variety of food items available in the cafeteria?	1	110
How do you rate the variety of food items available in the cafeteria?	2	985
How do you rate the variety of food items available in the cafeteria?	3	1403
How do you rate the variety of food items available in the cafeteria?	4	1428
How do you rate the variety of food items available in the cafeteria?	5	1510

This chart provides in-depth insights into the massive table you’ve just skipped to reach here. Note how colors have been used strategically to create a clear distinction in variable differences.

Visualization Source: ChartExpo

So what are the insights?

Food items available in the cafeteria have the lowest score (2.4/5). Besides, the overall brand perception of the university has (2.5/5).

Note we’ve used ChartExpo to generate the insightful and easy-to-interpret chart below.

Keep reading because you don’t want to miss more tips that underscore the importance of data visualization, especially in healthcare.

• Use in Healthcare

Health practitioners will tell you the importance of data visualization without mincing words.

Data visualization is revolutionizing the sector, especially in the decision-making process. Health facilities are increasingly using data models to plan for resource allocation and mitigate losses.

Take a look at the example below.

Imagine a US Governor has tasked you to provide insights into children’s diseases affecting his state. Essentially, the Governor wants to know the prevalent disease affecting children each month of the year for efficient resource allocation.

The main diseases you’ll focus on are listed below:

• Chickenpox
• Whooping cough
• Measles
• Rotavirus
• Tetanus
• Hepatitis B

Let’s use the table below for our scenario.

This example underscores the importance of data visualization in the healthcare sector.

Months	Chickenpox	Whooping cough	Measles	Rotavirus	Tetanus	Hepatitis B
1	168	187	159	51	247	229
2	66	77	136	199	87	159
3	81	148	69	101	217	125
4	144	206	163	121	189	67
5	46	230	231	149	84	228
6	162	113	20	183	90	147
7	33	37	234	95	239	243
8	12	209	217	105	67	128
9	144	23	84	224	212	114
10	157	25	189	13	199	252
11	28	15	34	100	203	171
12	190	202	247	183	85	105

The insightful chart below is a good data visualization example. Note how this chart is easy to read and interpret.

Visualization Source: ChartExpo

So what are the insights?

Measles is the leading outbreak among children in the state in May and December.

Chickenpox remained least in August and November. October is least affected by Rotavirus and October is the Month in which maximum hepatitis cases were reported.

What is Data Visualization Used for?

Data visualization serves various purposes across different fields and industries:

Data Exploration: It helps in exploring and understanding datasets by identifying trends, patterns, and relationships within the data.

Decision Making: Visualizations aid decision-making processes by presenting complex information in a clear and concise manner, allowing stakeholders to make informed choices based on data-driven insights.

Communication: It facilitates effective communication of findings and insights to diverse audiences, making complex information more accessible and understandable.

Performance Tracking: Visualizations are used to track and monitor performance metrics, allowing for easy comparison and analysis of data over time.

Forecasting and Predictive Analysis: By visualizing historical data and trends, it assists in predicting future outcomes and trends, enabling proactive decision-making.

Presentations and Reporting: Visual representations are commonly used in presentations and reports to convey information in a visually engaging and persuasive manner.

Explaining Complex Concepts: It simplifies intricate concepts, making them more comprehensible, aiding in education, training, and conveying complex information more effectively.

Business Intelligence and Analytics: Data visualization tools are integral in business intelligence and analytics processes, enabling organizations to extract actionable insights from large datasets to drive business strategies and innovation.

Overall, “What is Data Visualization” is a versatile tool used across various domains to make data more understandable, actionable, and impactful.

Top 10 Data Visualization Best Practices Every Professional Working with Data Must Follow

Remember, data is much easier to understand when presented in a visually compelling way. Besides, we process visuals 60,000 times faster than text.

Yes, you read that right.

And it gets better. Visuals are far more memorable than text.

A research study found that we’re likely to retain 10-20% of written or spoken information and a whopping 65% of visual information three days after a seminar.

Let’s dive into the meaty part of the blog. Are you ready?

#1: Use Clean Data

Before converting your raw data into a chart, you need to clean it. Yes, sparkling clean. So you need to do what we call data cleaning.

What’s data cleaning?

Well, it’s the process of filtering out any anomalies or inaccuracies within your data. This process is essential because inaccuracies can distort your visualization.

A research survey conducted by New York Times discovered that data scientists spend about 50 to 80% of their time cleaning and organizing data. And all these are done before the actual visualization.

This shows that cleaning data is incredibly important.

You don’t want to use skewed data to generate insights that can mislead your audience. It takes one question from your audience to discredit your story. And not to mention the mistrust barrier you’ll have created.

Cleaning data is one of the proven data visualization practices you don’t want to skip.

#2: Be Audience-Specific

What does this mean?

You need to create data visualizations that resonate with your audience. And this means you have to roll-up your sleeves and do in-depth research on them. Specifically, focus on their interests, fears, and motivations to win them.

Use visualizations that are custom-specific for your audience. By doing this, you’ll create charts with a strategic purpose that answers a specific question and can be easily understood by the audience.

Let’s put the above into perspective.

Imagine you’re creating a story for a non-technical audience.

Should you use technical charts?

No. You need to use charts that are easy to read and understand. You don’t want to subject your audience to cognitive overloading.

Another issue you need to avoid is using charts cluttered with trend lines.

Why?

Bombarding your charts with multiple trend lines and other unnecessary stuff will divide the attention of the audience. And this is the last thing you want. Stick to the keep it stupid simple (KISS) principle.

How?

Select charts that are simple and easy to interpret without struggle. Also, avoid overloading your chart with unnecessary information that might confuse the audience.

And you can achieve this by spelling your objectives for visualization clearly. Remember, before selecting the best visualization for your story; ask yourself what the audience will be looking for in the chart. Like we said earlier, understand the requirements and preferences of your audience.

Know their background.

• Do they have enough time for a detailed visualization?
• How aware are they of the context of the visualization?
• What additional information are they looking for?
• Are they familiar with the graphs used? And so on.

Besides, your goal is to inform people and give accurate results.

Make your visualizations more transparent and explanatory so that your audience can understand your conclusions better. More so, remember who is in your audience and the context of your presentation. You can achieve this by answering the following question:

What’s the best way to make them understand your findings?

Build your design based on the answer.

#3: Select the Right Chart

Once your data is clean and ready, select the best graph or chart to visualize that complements your story. The charts you use in a story are incredibly significant because they bear insights.

And this means you want charts that not only suit your story seamlessly but are easy to read and interpret.

Note: The purpose of data visualization is pretty clear. It is to make sense of the data and use the information for the businesses’ benefit. That said, data is complicated, and it gains more value when it gets visualized.

Essentially, without visualization, it’s challenging to communicate the data findings quickly. Or identify patterns to pull insights and interact with the data seamlessly.

Select the best chart for your story based on the attributes of the data and, most importantly, your goals.

We have tons of charts at our disposal.

But each chart is best used to visualize data with specific attributes. Don’t forget the overall objective of the story also plays a hand in selecting the best visualization. For instance:

• Tables present a lot of information in an organized fashion, but they can confuse users searching for high-level patterns.
• Line graphs measure variations or patterns over time and display the interaction between two or more variables.
• Bar charts assess the varying amounts and totals of different groups.
• Scatter plots display the correlation of two variables plotted along two axes, where the pattern of the corresponding points indicates an association between them.
• Pie and donut charts are best used to visualize compositional data that adds to a whole number, which could be a percentage.
• Sankey charts are best used to visualize flow in your story, as shown below.

Let’s analyze the chart above real quick.

As you can see, the customer flow in a restaurant is segmented based on gender, menu, and sentiments to provide insights on a micro-level.

Selecting the best chart for the story is one of the proven data visualization practices that seasoned pros follow religiously.

#4: Label your Chart

It’s needless to reiterate that charts empower us to identify patterns, trends, and outliers in our data quickly.

So you need labels to describe your insights. You don’t want your audience to struggle to determine the head or tail of your chart. You want them to cherry-pick insights right away as they flow with your story.

Irrespective of whether you’re describing an experimental setup, introducing a new model, or presenting new results, data cannot speak for itself.

You need captions to help your audience understand the context of your visualization right away. The caption explains how to read and interpret your chart. More so, it provides additional information about missing variables.

Labeling your chart should now be part of your checklist. Remember to double-check this component before parading your story in front of an audience.

We’ve compiled a small list of proven data visualization best practices for you to follow when labeling your chart.

You don’t want to skip them.

• Labels should be legible. If your labels are not clear, they won’t help your story. So they should be easy to read and, most importantly,
• Give a title to the graph. A title provides the context of your charts. You don’t want your audience to struggle to put insights into perspective.
• Use a legend wisely. Legends make it easier for your audience to spot the difference between data points in your chart.
• Label your axes. The title cannot always communicate the labels of the axes (x and y).

So, label them accordingly.

Remember, everything you’re doing is for the audience. You already know the labels of your x and y-axis but is your audience familiar with them?

You don’t want to leave anything to chance.

#5: Use Stories to Explain your Chart

Data visualization is not just about numbers.

What is it about?

Well, it’s about creating compelling stories that get the audience nodding with unison. Stories are more powerful than facts and numbers.

Remember, words can clutter your charts, primarily when used excessively.

Use words that support the core insights you intend to communicate to the audience through a story.

Another critical aspect to keep in mind is clutter and noise in your chart. Data visualization is all about keeping everything simple and clear for the audience. So avoid unnecessary information that can draw attention away from the crucial details.

Use headings, sub-headings, and annotations to provide descriptive information about your visualization, including critical insights.

We’ve rounded up some tips for you to steal and use, especially when creating stories. Check them out.

• Use simple phrases. The aim is to allow the visualization to speak for itself.
• Keep only those annotations that provide relevant information. Putting up annotations for every data point will distract your audience and unnecessarily clutter your chart.
• Use bold or italic text to highlight important parts of the graph. But avoid using them excessively. Otherwise, there will be no difference between regular and emphasized text.
• Avoid text reiterating the same message. For example, the heading and subheading repeating the same message should be avoided. Nobody likes redundant information.
• Avoid using distracting hard-to-read fonts. Your audience should be able to grasp the message in the graph instantly without much struggle.

If you follow the data visualization best practices we’ve compiled for you, you’ll end up with charts that are easy to read and interpret. It’s that easy.

#6: Use Predictable Pattern for Layouts

We naturally have an eye for patterns and trends. And this means we can easily differentiate uptrends from downtrends.

Besides, our eyes are drawn to indicators that tell us important information at a glance.

We naturally seek patterns.

And if patterns are random or don’t make sense, it becomes tough to understand what the visualization communicates. To capitalize on our natural tendencies, ensure the order in which you present data makes sense to audiences.

Note: we naturally read from left to right. And this means you need to orient your visualization to adhere to the aforementioned.

Again, if you’re using multiple graphs, ensure the order is consistent and connections between the data points are clear. You don’t want your audience to get lost as they track a data point or metric.

Take a look at the chart below. It’s about the sentiments of the market towards a brand.

From the diagram above, you can easily track how customer satisfaction has changed over the course of time. And there’s a line curve connecting bars to show the overall trend of the product sentiment across time.

#7: Select the Right Tool

Data in today’s world is gold, but only when you visualize it for in-depth insights. And this means the tool you use can make or break your story.

You don’t want a tool that’s complex to use or time-consuming. A massive chunk of the tools available in the market are either expensive or complex to use. In other words, they require detailed knowledge of coding to run them.

To create simple and easy charts to read, even for non-technical audiences, we recommend ChartExpo. And this is because the tool is amazingly easy to use and affordable. Besides, it produces simple and clear charts.

If you want a tool that keeps you confined within data visualization best practices, go for ChartExpo.

You can use this tool to visualize business data to improve product interest, marketing strategies, and sales.

#8: Data Visualization Color Best Practices

Color plays a significant role in communicating insights in the absence of words.

Besides, it affects the way our brains process information. Using color strategically can increase memory, aid pattern recognition, and attract attention to key insights.

The goal of data visualization is to help audiences quickly digest information and remember it. While other design principles have a role to play (including white space, contrast, grouping, etc.), color is one of the easiest to apply to data visualization.

Using color strategically can help your audience to understand the meaning and impact of the information presented. On the flip side, poorly used colors can distract your audience from your story.

Avoid heavy and low contrasting colors if you want your audience to understand your charts without struggle. More so, use colors to highlight critical insights that form the backbone of your story.

For instance, to distinguish profitable and loss months in a bar chart, use high contrasting colors, such as navy blue and light blue colors.

We’ve rounded up data visualization color best practices for you to follow when highlighting insights in your charts. Check them out below:

• Avoid using the same color.
• Use color to create associations between data points.
• For the same variables, use the same color.
• Avoid using a single color to show continuous data.
• Use contrasting colors to show comparison/contrast.
• Clarify what the colors represent to readers.
• Take into account the needs of color-blind people when selecting colors.
• Avoid using less distinguishable colors
• Avoid using TOO many colors in a single chart

Note: There’s always room for creativity, even with the “rules” of color in data visualization. The guiding principle of visualization is to use every element to aid in communication.

You’ll find that there’re, in fact, many ways to communicate information using color.

Take a look at the chart below.

From the chart above, you can easily distinguish two critical insights due to the strategic use of high contrasting colors:

• The best and worst-performing month of the year
• The best and worst-performing flavor

This color combination shows the overall performance of the ice cream shop. The insights generated by the chart above can inform the marketing strategy to pursue.

#9: Highlight Key Insights

If you want your audience to cherry-pick key insights effortlessly, you have to highlight them.

Yes, you read that right.

You don’t want their eyes to struggle to pick the key takeaways.

To direct your audience’s attention, use specific visual cues, such as reference lines or highlighted trends. Remember, our eyes are drawn to symbols that send us valuable details at a glimpse.

For instance, when using a bar chart, highlight the significant bars to help the audience gain perspective of the story.

Seasoned data visualization experts highlight the key insights they want their audiences to take home. Employ this practice religiously if you want your readers to align with the objectives of your chart.

Let’s dive into the last tested and proven data visualization best practice.

#10: Look for Business Insights

Today’s world is highly competitive.
And this has forced businesses to run back to data for answers. Marrying the insights from your data with high-level business objectives is one of the visualization best practices.

You need data-backed insights before you make critical business decisions, such as marketing budget allocation, product design, etc. Leveraging data can help you stay on top of problems even before they arise.

How?

Remember, you can use data to forecast future trends in your niche market.

Advantages and Disadvantages of Data Visualization:

Advantages:

Data visualization helps you to identify trends and patterns in your business data quickly.

Our brain processes visual information, such as charts, 60,000 times faster than poring over spreadsheets and data reports.

Visualization is a quick and easy way to convey insights to a broader audience. Make data visualization a habit in your organization to enjoy the benefits below:

1. Quick and Better Analysis

Visualization tools make it amazingly easy for you to extract answers from your data to create compelling stories for investors.

Imagine using the tables and spreadsheets to explain emerging patterns and other significant insights to your audience. How would they respond? Would you get buy-in after presenting the table to them?

2. Empowers Quick Action

Our brains grasp visual content, such as graphs and maps, 60,000 times faster than table reports, as we said earlier.

And this means a compelling story loaded with easy to interpret charts can empower quicker decision-making.

3. Identifying Patterns and Trends

Bulky data provides unlimited opportunities for businesses to extract actionable insights. Yes, insights that could spell the difference between you and competition.

Visualizing data helps pinpoint relationships and patterns between metrics. Exploring these patterns enables you to save immense resources, such as time, by focusing only on areas that need urgent action.

4. Finding Errors

Data visualization helps you spot errors in the data easily. Working with error-free data validates the accuracy of the insights extracted.

We hope you see the importance of data visualization.

5. Data Storytelling

The reason why we visualize data is to create stories. Remember, poring over numbers in spreadsheets is monotonous, especially if you’re in front of an audience.

So you need to create a compelling story with insights extracted from the raw data. People love stories. Yes, and this is because they appeal to emotions.

To craft a compelling story, you need an actual story.

It sounds contradictory. Yes, we know.

To create a narrative, start by asking a question or forming a hypothesis. And then, dig into your data to find answers.

Below are some of the questions you need to ask:

• What are you going to explain? 
• What are your goals? 
• Are you after a buy-in on a proposal?

Remember, visualization comes before you create a story.

We hope you’re noting the importance of data visualization, especially in creating data narratives. 

However, “What is Data Visualization” also has some limitations:

Disadvantages:

Misinterpretation: Incorrect data representations or visual choices can lead to misinterpretation or misunderstanding of information.
Complexity: Creating effective visualizations requires expertise in both data analysis and design, which can be challenging.
Over-Simplification: Some complexities within data might get oversimplified in visual representations, leading to important nuances being overlooked.
Data Limitations: Visualizations are only as good as the data they are based on; if the underlying data is flawed, the visualization may be misleading.
Tool Dependency: Relying solely on visualization tools without understanding the context or data might lead to biased or incorrect conclusions.
Subjectivity: Interpretation of visualizations can be subjective, leading to different conclusions drawn from the same data depending on the viewer’s perspective.

Overall, while “What is Data Visualization” is a powerful tool for understanding and communicating data, it’s essential to apply it judiciously and interpret visualizations with caution, considering both their advantages and limitations.

Exploring Best Data Visualization Techniques:

Exploring various techniques in “What is Data Visualization” unveils a multitude of ways to present data effectively.

Line Charts

Line charts are graphical representations that display data points connected by straight lines. They are particularly useful in illustrating trends and changes over continuous intervals, such as time. Each data point represents a specific value, and when connected, these points form a line that visualizes the relationship or pattern within the data. Line charts are effective for showcasing trends, identifying fluctuations, and comparing multiple sets of data over time. They’re commonly used in various fields such as finance, economics, science, and analytics to demonstrate changes in data, making them a fundamental tool for visualizing continuous data sets.

Bar Graphs

Bar graphs are visual representations of data using rectangular bars of different lengths or heights to show the relationship between different categories. Each bar represents a specific category and its numerical value. These graphs are effective for comparing discrete categories or groups of data. The length or height of each bar is proportional to the value it represents, making it easy to compare quantities visually. Bar graphs are versatile and can be used for various purposes, such as comparing sales figures for different products, showing frequency distributions, or presenting survey results across multiple categories. They provide a straightforward and easily interpretable way to visualize categorical data and make comparisons between different groups or items.

Pie Charts

Pie charts are circular statistical graphics divided into slices to represent proportions or percentages of a whole. Each slice corresponds to a specific category or data point, and the size of each slice is proportional to the quantity it represents in relation to the whole dataset. These charts are useful for illustrating the composition or distribution of a dataset, highlighting the relative sizes of different categories within it. They are commonly used to show market shares, budget allocations, or any data where the parts contribute to a whole. However, pie charts might become less effective when dealing with too many categories or when the differences in proportions are subtle, as accurately comparing slice sizes can be challenging. Nonetheless, they offer a quick visual representation of the relative contributions of different categories within a dataset.

Scatter Plots

Scatter plots are visual representations that display individual data points as dots on a two-dimensional graph, where each dot represents the values of two different variables. They showcase the relationship between these variables, helping to identify patterns, trends, or correlations between them. One variable is plotted on the x-axis, while the other is plotted on the y-axis, allowing for the visualization of how changes in one variable relate to changes in the other. Scatter plots are particularly useful in determining the strength and direction of relationships between variables, such as identifying correlations or outliers within datasets. They’re widely employed in fields like science, economics, and social sciences to visualize and analyze relationships between two continuous variables.

5 Data Visualization Errors That Could Prove Expensive If Ignored

Following points you should have in mind:

  1. Using the Wrong Type of Chart

Deciding on the type of visualization can be challenging since there’re so many “pretty” but uninformative visualizations. Always strive for clarity and simplicity. Great rules of thumb for this are:

• To highlight and compare values, use charts, such as a bar chart.
• To show change over time, or how one value affects another, use charts, such as a line graph.
• To show relationships between values, use diagrams, such as a Scatter Plot.

Take a look at the pie chart above. Does it feel crowded?

Simply put, all charts aren’t created equal.

There are more than 10 data attributes in this pie chart visualizing the U.S. government budget ( hypothetical ).
Graphs, such as pie charts, are strategically positioned to visualize the composition of your data.

Looking at the various color and size combinations for each pie slice becomes an unwelcome hurdle.

What is this chart trying to convey?

Arguably, you may be better off sharing actual numbers without any visualization at all. It’s best to limit your pie charts to no more than five “pie” slices.

Trust us. Your audience will thank you.

Including over 12 data variables in a pie chart is an overkill. It makes it difficult to effectively compare and communicate the share of total data in the graph.

And the color and size combinations for each pie slice are unwelcome hurdles for your audiences. If you face this problem constantly, continue reading.

The Solution is on the way.

So how can you improve the pie chart above?

Check the diagram below.

You can overcome the challenge mentioned earlier by using visualization diagrams, such as bar and column charts. These charts handle relatively massive data.

While you’re at it, remove all unnecessary distractors, such as low contrast colors and more extended data labels.

Consider substituting a pie chart for a donut chart if you’re working with no more than 2–3 data points. Remember: Your audience will quickly lose interest if they come across yet another chart that’s challenging to read and interpret.

For you to choose the correct chart, ask the following questions.

Do You Want to Compare Values?

The charts are perfect for comparing one or many value sets. And they can easily show the low and high values in the data sets.

To create a comparison chart, use these types of graphs:

• Column Charts
• Bar Charts
• Line Charts
• Scatter Plot Charts
• Dot Charts
• Multi-Series Spark Line Chart
• Radar Charts (for limited data)

Do You Want to Show the Composition of Your Data?

Composition charts show how individual parts make up the whole of something, such as:

• The device type used for mobile visitors to your website
• Total sales broken down by sales rep.

To show composition, use these charts:

• Pie Chart
• Stacked Bar Chart
• Stacked Column Chart
• Area Chart
• Partition Chart

Do You Want to Understand the Distribution of Your Data?

Distribution charts visualize with clarity outliers, the normal tendency, and the range of information in your values.

Use these charts to show distribution:

• Scatter Plot
• Line Chart
• Column Chart
• Bar Chart

Are You Interested in Analyzing Trends in Your Data Set?

If you want to know more information about how a data set performed during a specific time, there’re particular chart types that do exceptionally well.

You should choose a:

• Line
• Dual-Axis Line
• Column
• Multi-series Line Chart

Do You Want to Visualize the Relationship Between Value Sets?

Relationship charts are suited to showing how one variable relates to one or numerous different variables. You could use this to show how a variable positively affects, has no effect, or negatively affects another variable.

When trying to establish the relationship between variables, use may use these charts:

• Scatter Plot
• Bubble
• Line
• Parallel Bar

Do You Want to Get In-depth Insights Into Your Survey Data?

Survey data can easily overwhelm you, especially when in raw form. You need specialized charts to get the most from your data. These charts include:

• Likert Scale Chart

Stick a little longer to discover how you can access these charts at highly affordable rates.

Remember, data visualization methods aren’t one-size-fits-all.

2. Using 3-D Images

 Don’t get us wrong. 3D images are visually appealing.

However, what could be worse than using the wrong charts in visualizing your data?

That is 3D.

Besides, nothing in a data presentation screams “rookie” more than a 3D chart. It is one of those things where “just because you can doesn’t mean you should.”

3D charts are visually heavier, difficult to read, and sometimes downright misleading. Their only claim to fame is that they tend to be more visually interesting than 2D charts.

Don’t be that person who pursues a visually appealing graphic at the expense of conveying a concrete message.

Let’s use hypothetical revenue data for Amazon.

Do you think the graph above is the best way to visualize Amazon’s net revenue by product?

Can you confidently say that the AWS segment earned more revenue than subscription services based on the data presented in this pie chart?

Chances are, probably not.

3D charts can create false representations since our eyes perceive triple dimensional objects as closer as and more extensive than they genuinely are.

You can improve this graph’s value and appearance by changing it from a 3D pie chart to a Bar Chart Horizontal and reordering the data, as shown by the screenshot above.

3. Confusing Correlations

Visualizing correlations between datasets gives you a broader understanding of a topic. Overlaying datasets on the same graph is one way of showing correlations.

When you take into account correlations, overlays lead to an “Aha!”  moments. Conversely, when overlays are excessive in number, it’s difficult for viewers to draw connections.

A famous example is linking increased ice cream sales to surges in violent crime when both result from warm weather.

Your line chart may show the correlation between ice cream sales and violent robbery during warm weather. However, is it pragmatic to conclude that ice cream sales cause a surge in theft?

The critical takeaway is data visualization charts may show correlation, but it doesn’t equal causation.

4. Using Different Scales for Your Data

People prefer visuals over text.

It’s a fact.

For instance, bright and colorful graphics can spice up an otherwise boring report. Nevertheless, beauty should never supersede the core messaging when presenting your chart’s data.

The visual charts you see around lack the correct scale. This distorts the core message.

Some people prefer beauty over communication. Remember, you’re visualizing data to enhance clarity and fill in gaping holes in your presentation story.

Please, avoid using inconsistent scales, such as starting at 30 instead of point ZERO. This will distort your presentation.

 5. Using Low Contrast Colors

Colors vastly improve your ability to read and interpret a chart quickly. Using vivid, high-contrast colors for different categories is an easy way to deliver outstanding data visualizations.

Yes, you read that right.

In practice, this means that you should avoid placing colors that appear beside each other on the color wheel next to one another in your charts.

Instead, colors that are opposite each other on the color wheel provide the maximum possible contrast. Try to get as close to this ideal as possible while considering variables, such as branding.

Check the diagrams below. Can you feel the vibe?

A high vs. low contrast rendering of an otherwise well-designed pie chart. Notice the difficulty with reading the low-contrast chart.

FAQs:

How can businesses benefit from Data Visualization?

Businesses leverage “What is Data Visualization” to streamline operations, identify market trends, understand consumer behavior, and drive strategic decision-making.

What are the common mistakes to avoid in Data Visualization?

Overcrowding visuals and misrepresenting data are common mistakes. Maintaining simplicity and accuracy is crucial.

Is Data Visualization only for professionals?

Data Visualization benefits professionals and non-professionals alike, enabling anyone to grasp insights from data more easily.

How important is data visualization?

Data visualization is useful for data monitoring, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting patterns, and presenting actionable results. There’re tons of good data visualization examples, such as Bar graphs and Sankey, you can use to create irresistible stories.

Wrap Up:

“What is Data Visualization” serves as an indispensable bridge between raw data and meaningful insights. Its evolution, diverse techniques, and applications across industries underscore its pivotal role in today’s data-driven world.