What is Data Visualization?
Data visualization refers to creating a graphical representation of information in charts, maps, graphs, etc. Data visualization serves as an essential part of data analytics and helps interpret big data in the current scenario using complex numerical and factual figures.
Why is Data Visualization Important?
With an infinite amount of data available to businesses worldwide, the challenge lies in interpreting the data. If interpreted and represented correctly, data can offer valuable insights into a business’s growth. Data visualization techniques allow companies and individuals to understand even the most complex data sets by converting them into simple charts and graphs.
Recent studies discovered that the appropriate use of visualizations in data analytics carries the potential to shorten business meetings by 24%. A good business intelligence strategy with visualization capabilities can significantly boost the return on investment (ROI) of a business. Therefore, data visualization is critical to the sustained success of a business. If you are a beginner and interested to learn more about data science, check out our data science courses from top universities.
What are the Different Data Visualization Techniques?
Several visual data analysis techniques can be applied to ensure business growth.
Some of them are mentioned below:
1. Know Your Target Audience
This may be considered as one of the most overlooked aspects related to data visualization. While presenting data in the form of charts and graphs, it is wise to know the target audience and understand their needs and expectations.
Some stakeholders and executives may be happy seeing a simple pie chart or bar graph. However, there may be others who would want you to delve deep into the subject matter and explain the insights. Therefore, it is highly recommended that you conduct a thorough research about the audience of your presentation before the meeting. Then, create visuals to meet their expectations.
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Just like any other business pursuit, your efforts are only as effective as the strategy behind them. It is essential to set aims and objectives before creating data reports with graphs, charts, and visuals. You must also decide the most crucial data insights you would like to share with your audience.
Having a clear idea of what you want to explain and the outcome you need makes it easier to create accurate visualizations. One of the most efficient ways to guide your efforts is by using a set of relevant Key Performance Indicators (KPIs) for your ongoing campaigns or projects.
3. Choose Correct Visual Type
You do not want to confuse your audience with a set of sophisticated graphics on the screen. Your aim should be to simplify things for a more accessible and better understanding. Therefore, opt for the right type of chart that projects your purpose.
For instance, a simple line graph can show the growth in a particular trend over the year. On the other hand, a pie chart can represent the percentage of profit and loss in the previous year. Other forms of visualizations include gauge charts, bar graphs, scatter plots, and more.
Choose the most suitable and simplest form of visual to explain the insights to your audience. This will lead to clarity of concept and help them understand its impact on current and future business growth.
Read: Data Visualisation in Detail
4. Make Wise Use of Colors
The simplest of our visual data analysis technique include choosing the correct color-scheme for displaying representational assets. Opting for the right color-scheme helps to improve your efforts significantly. The concepts of the color theory have a significant influence on your visualization model’s overall performance. Also, you must always seek to keep your color-scheme consistent in your data analysis.
You may use sharp contrasts to differentiate between elements (e.g., positive trends in green and negative trends in red). In general, people use red, green, blue, and yellow, since they can be easily identified and decrypted.
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5. Handle big Data With Ease
With a massive amount of data available, it has become very challenging to handle it.
Here are some essential visual data analysis techniques to help you manage your big data.
Use these tips to break it down for the most streamlined, rational, and straightforward visualizations possible:
- Find out the data you and your company have access to and determine which one is the most important. Then, mark each branch of information to make it easy to isolate, interpret, and decode.
- Ensure all of your managers, staff, and team members understand where the data comes from and how to access it to ensure those insights are managed efficiently across departments.
- Keep the data and data management systems accessible and up-to-date to make the visualization process quick.
- Ensure that you use business dashboards that display the essential insights in an easy-to-access, interactive environment. They speed up the visualization process while still extracting the information’s full value.
6. Organize Your Data
After you have grouped your data and split it down into relevant data segments, you can delve deeper and build a clearly defined hierarchy. You may organize it by using a framework (color-coded, binary, etc.). However, the structure, organization, and configuration of the data will remain in continuous development. The simulation attempts are faster, easier, and more efficient with an organized system.
7. Organize Your Unstructured Data
To correctly manage unstructured data sets, you can review the network diagram or word cloud services.
A network diagram is also used for drawing a network graphic map. This layout style is useful when preparing detailed network documents for designers, network engineers, and data analysts.
Word clouds provide an easily understood means to present diverse sets of complex data, in line with network diagrams. In comparison to graphical objects, however, a word cloud is an image created using words for a particular context or topic. The size of each word represents its frequency or value in the context of the data.
8. Do not Forget to Compare
It may be the simplest of our data visualization techniques, but it is relevant. You should have as many detailed comparisons as possible when sharing your knowledge and observations. Display two maps, tables, diagrams simultaneously, each providing different representations of the same statistics over a given timeline.
For example, show monthly sales reports for 2016 and 2017 next to each other. This way, you can have a concise guide on the effect of the results, outlining the strengths, drawbacks, patterns, highs, and troughs anyone should talk about and act upon.
9. Narrate Your Story
Sharing the story will help reach the viewers and make it easier for them to understand the concept with minimal effort. This is akin to content marketing in which you graphically show information to deliver a meaningful idea or target. Several scientific studies have confirmed that humans respond positively to a well-known story. By taking this approach to your visualization efforts, not only will you impress your audience with your presentations and reports, but you will also increase your odds of communicating your most critical messages leading to long-term evolution and success.
To create a perfect story, you should gather your material and think like a writer. Create a simple beginning, middle, and end. Include conflict and resolution. You must also develop suspense throughout your plot so that your visualizations have a large impact on listeners.
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10. Use Modern Tools
We live in a fast-paced, modern age, far away from pen and paper. To make a booming visualization success, you can use current resources to help you make the best possible choices when collecting your data most securely and successfully.
A task-specific, collaborative web interface or platform provides an understandable, concise, detailed, and accessible way of quickly capturing, organizing, and displaying data. This ensures that the data visualization techniques have as much effect as possible while taking up as little time as possible.
What Factors can Influence Data Visualization Choices?
Factors that may influence data visualization choices include:
- Type of Audience: The type of audience impacts the choice of data visualization. There may be people interested in simply knowing the current status, while others may delve deeper into every related parameter and concept.
- Context: You should use various approaches to make the graphs appear according to the background. To highlight a particular statistic, e.g., extreme income development relative to specific years, you might choose to use a vibrant shade for the essential item on the table. Otherwise, you should use contrasting colors to distinguish opposing ideas.
- Content: The type of content and relation you wish to share is another factor influencing your visualizations choice. For instance, if the metrics change over time, you may use line graphs to show the dynamics, while scatter plots can show a correlation between the factors.
Check out: Benefits of Data Visualization
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The data mentioned above visualization techniques will serve to propel your efforts to new successful heights. If you are looking for courses that can help you master data visualization techniques and concepts, upGrad is your best bet. Our comprehensive and informative classes, using illustrations and easy to understand vocabulary, can help you get hold of the concepts. With upGrad’s courses, you can master all the techniques related to data visualization.
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What is the significance of data visualization?
Data scientists and data analysts can understand huge data sets. But the other important professionals and the customers who are a part of the decision-making process will find it difficult to understand this complex data.
This is where data visualization comes into the picture. This technique helps you to convert the results and reports into interactive and easy to understand visuals, such as charts, graphs and diagrams.
State the different ways to visualize data.
Different visualization techniques are used to visualize different kinds of data. The Temporal category includes the single-dimensional data. The plus point of this kind of visualization is that all the charts are already familiar to us. For example, Scatter plots, Line charts, Timelines, etc. The hierarchical category includes data that is divided into subcategories.
This type of data can be visualized like a tree structure. For example, Tree diagrams, Ring charts, Sunburst diagrams. Datasets that have networking between them or that are connected deeply with each other fall into this category.
In what ways is data visualization advantageous?
Data visualization provides enhanced ways to analyze and research the data set. After successful analysis of the data, it allows you to display your research in the form of interactive charts and plots like scatterplot charts and line charts. One of the most essential applications of data visualization is to analyze patterns and trends to deduce predictions and potential areas of growth.
One of the best features of data visualization is that it lets you divide the data according to the customer preferences which makes it easier to focus more on those parts which require more attention.