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.
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.
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.
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 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.
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:
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.
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:
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.
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.
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?
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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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:
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:
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.
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.
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.
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:
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?
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.
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.
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.
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:
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:
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 various techniques in “What is Data Visualization” unveils a multitude of ways to present data effectively.
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 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 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 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.
Following points you should have in mind:
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:
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.
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:
Composition charts show how individual parts make up the whole of something, such as:
To show composition, use these charts:
Distribution charts visualize with clarity outliers, the normal tendency, and the range of information in your values.
Use these charts to show distribution:
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:
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:
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:
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.
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.
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.
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.
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.
Businesses leverage “What is Data Visualization” to streamline operations, identify market trends, understand consumer behavior, and drive strategic decision-making.
Overcrowding visuals and misrepresenting data are common mistakes. Maintaining simplicity and accuracy is crucial.
Data Visualization benefits professionals and non-professionals alike, enabling anyone to grasp insights from data more easily.
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.
“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.
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