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17 Essential Data Visualization Techniques, Concepts & Methods To Improve Your Business – Fast

Data visualization techniques, methods, and concepts blog post by datapine

“By visualizing information, we turn it into a landscape that you can explore with your eyes. A sort of information map. And when you’re lost in information, an information map is kind of useful.” – David McCandless

Did you know? 90% of the information transmitted to the brain is visual.

Concerning professional growth, development, and evolution, using data-driven insights to formulate actionable strategies and implement valuable initiatives is essential. Digital data not only provides astute insights into critical elements of your business, but if presented in an inspiring, digestible, and logical format, it can tell a tale that everyone within the organization can get behind.

Data visualization methods refer to the creation of graphical representations of information. Visualization plays a crucial part in data analytics and helps interpret big data in a real-time structure by utilizing complex sets of numerical or factual figures.

With the seemingly infinite streams of data readily available to today's businesses across industries, the challenge lies in data interpretation, which is the most valuable insight into the individual organization as well as its aims, goals, and long-term objectives.

That's where data visualization comes in.

Due to how the human brain processes information, presenting insights in charts or graphs to visualize significant amounts of complex data is more accessible than relying on spreadsheets or reports.

Visualizations offer a swift, intuitive, and simpler way of conveying critical concepts universally – and it's possible to experiment with different scenarios by making tiny adjustments.

Recent studies discovered that the use of visualizations in data analytics could shorten business meetings by 24%. Moreover, a business intelligence strategy with visualization capabilities boasts a ROI of $13.01 back on every dollar spent.

Therefore, the visualization of data is critical to the sustained success of your business and to help you yield the possible value from this tried and tested means of analyzing and presenting vital information. To keep putting its value into perspective, let’s start by listing a few of the benefits businesses can reap from efficient visuals. 

Benefits Of Data Visualization Skills & Techniques

As we just mentioned in the introduction, using visuals to boost your analytical strategy can significantly improve your company’s return on investment as well as set it apart from competitors by involving every single employee and team member in the analysis process. This is possible thanks to the user-friendly approach of modern online data analysis tools that allow an average user, without the need for any technical knowledge, to use data in the shape of interactive graphs in their decisions making process. Let’s look at some of the benefits data visualization skills can provide to an organization. 

  • Boosts engagement: Generating reports has been a tedious and time-consuming task since businesses and analytics came together. Not only are static reports full of numbers and text quickly outdated, but they are also harder to understand for non-technical users. How can you get your employees to be motivated and work towards company goals when they might not even understand them? Data visualizations put together in intuitive dashboards can make the analysis process more dynamic and understandable while keeping the audience engaged.  
  • Makes data accessible: Following up on the accessibility point, imagine you are an employee that has never worked with data before. Trying to extract relevant conclusions from a bunch of numbers on a spreadsheet can become an unbearable task. Data visualizations relieve them from that burden by providing easy access to relevant performance insights. By looking at well-made graphs, employees can find improvement opportunities in real-time and apply them to their strategies. For instance, your marketing team can monitor the development of their campaigns and easily understand at a glance if something is not going as expected or if they exceeded their initial expectations. 
  • They save time: No matter the business size, it is very likely that you are working with raw data coming from various sources. Working with this raw data as it is can present many challenges, one of them being the amount of time that it takes to analyze and extract conclusions from it. A time that could be spent on other important organizational or operational tasks. With the right data visualization tools and techniques, this is not an issue, as you can quickly visualize critical performance indicators in stunning graphs within seconds.  Like this, you can build a complete story, find relationships, make comparisons, and navigate through the data to find hidden insights that might otherwise remain untapped. 

17 Essential Data Visualization Techniques

Data visualization techniques, methods, and skills by datapine

 Now that you have a better understanding of how visuals can boost your relationship with data, it is time to go through the top techniques, methods, and skills needed to extract the maximum value out of this analytical practice. Here are 17 different types of data visualization techniques you should know.

1. Know Your Audience

This is one of the most overlooked yet vital concepts around.

In the grand scheme of things, the World Wide Web and Information Technology as a concept are in their infancy - and data visualization is an even younger branch of digital evolution.

That said, some of the most accomplished entrepreneurs and executives find it difficult to digest more than a pie chart, bar chart, or a neatly presented visual, nor do they have the time to delve deep into data. Therefore, ensuring that your content is both inspiring and tailored to your audience is one of the most essential data visualization techniques imaginable.

Some stakeholders within your organization or clients and partners will be happy with a simple pie chart, but others will be looking to you to delve deeper into the insights you’ve gathered. For maximum impact and success, you should always conduct research about those you’re presenting to prior to a meeting and collate your report to ensure your visuals and level of detail meet their needs exactly.

2. Set Your Goals

Like any business-based pursuit, from brand storytelling right through to digital selling and beyond - with the visualization of your data, your efforts are only as effective as the strategy behind them.

To structure your visualization efforts, create a logical narrative and drill down into the insights that matter the most. It’s crucial to set a clear-cut set of aims, objectives, and goals prior to building your management reports, graphs, charts, and additional visuals.

By establishing your aims for a specific campaign or pursuit, you should sit down in a collaborative environment with others invested in the project and establish your ultimate aims in addition to the kind of data that will help you achieve them.

One of the most effective ways to guide your efforts is by using a predetermined set of relevant KPIs for your project, campaigns, or ongoing commercial efforts and using these insights to craft your visualizations.

3. Choose The Right Chart Type

One of the most effective methods of data visualization on our list; is to succeed in presenting your data effectively, you must select the right graphics for your specific project, audience, and purpose.

For instance, if you are demonstrating a change over set periods with more than a small handful of insights, a line graph is an effective means of visualization. Moreover, lines make it simple to plot multiple series together.

Visual representation of a line chart for sales methods

**click to enlarge**

An example of a line chart used to present monthly sales trends for a one-year period in a clear and glanceable format.

Here are six other effective chart types for different data visualization concepts:

a) Number charts

Number chart is one of the data visualization techniques that can showcase the immediate amount of sales generated in a year

Real-time number charts are particularly effective when you’re looking to showcase an immediate and interactive overview of a particular key performance indicator, whether it’s a sales KPI, site visitations, engagement levels, or a percentage of evolution.

b) Maps

In this example, data visualization methods are represented with a map chart, where you can easily see differences in sessions by continent

First of all, maps look great, which means they will inspire engagement in a board meeting or presentation. Secondly, a map is a quick, easy, and digestible way to present large or complex sets of geographical information for a number of purposes.

c) Pie charts

Data visualization concepts can be presented with a simple pie chart

While pie charts have received a bad rep in recent years, we feel that they form a useful visualization tool that serves up important metrics in an easy-to-follow format. Pie charts prove particularly useful when demonstrating the proportional composition of a certain variable over a static timeframe. And as such, pie charts will be a valuable item in your visualization arsenal.

d) Gauge charts

Operating expenses ratio financial graph

This example shows the operating expense ratio, strongly related to the profit and loss area of your finance department’s key activities, and this color-coded health gauge helps you gain access to the information you need, even at a quick glance.

Gauge charts can be effectively used with a single value or data point. Whether they're used in financial or executive dashboard reports to display progress against key performance indicators, gauge charts are an excellent example of showcasing an immediate trend indication.

e) Bar or column chart

One of the most common types of visuals, the bar chart, is often used to compare two or more values in the same category, such as which product is sold the most in the women's department. Retail analytics tools allow you to visualize relevant metrics in interactive bar charts such as the one displayed below. There you can see a detailed breakdown of sales by country. This way, you can easily understand at a glance where to focus your promotional efforts, for example. 

A bar graph is one of the most common data visualization methods used to compare values in the same category

d) Area chart 

Area charts are perfect when you want to show how different values developed over time. It combines a line and a bar chart to show how numeric values change based on a second variable. For example, we can see an area chart in action below tracking the P/E ratio. This financial analytics metric measures the value of a company’s shares compared to an industry benchmark (second variable). It gives investors an idea of how much they would pay for stock shares for each dollar of earnings. 

A financial KPI displayed in an area chart as an example of how data visualization skills allow businesses to extract relevant conclusions from their data

e) Spider chart 

Spider charts are complex visuals used to compare multivariate data with three or more quantitative variables. They are not so commonly used as bar or column graphs, but they prove extremely useful when analyzing rankings, reviews, or performance. For instance, our example below shows an employee skill analysis where three employees are being evaluated based on 6 attributes and a score. Through this, users can understand which employee is over or underperforming in each area and provide help where needed. 

Spider chart as a data visualization technique example

f) Treemap chart

This chart type is used to display hierarchical data through rectangles that increase or decrease their size proportional to the changes in the value it represents. It is often used to display large volumes of data in a visually appealing way to help the audience extract conclusions from it. It can be divided into multiple categories, but each category needs to have a different color, as seen in our example below, where the patient drug cost per stay is divided by department.   

Patiend drug cost per stay displayed on a treemap chart

To find out more and expand your data visualization techniques knowledge base, you can explore our selected types of graphs and charts simple guide on how and when to use them.

4. Be Careful Not To Mislead  

As mentioned a couple of times already, well-made visuals open the analytical world to a wider audience by offering easy-to-understand access to critical information. In fact, during the COVID-19 pandemic, millions of people across the globe used graphs and charts to stay informed about the number of cases and deaths. That said, purposely or not, visuals are not always used with the best intentions. The data in them can be manipulated to show a different or more exaggerated version of the truth. This is a common tactic used in the media, politics, and advertising, and you should be aware of it not only to identify it but also to prevent it from happening to you when generating a graphic. Some of the bad practices to avoid include: 

  • Truncating axes: It happens when the y-axis starts at a defined value instead of 0. This makes small differences between data points seem hyperbolic. It is widely used in politics to exaggerate particular scenarios. 
  • Omitting data: As its name suggests, this involves omitting specific data sets from the visual. This could either be intentional to hide a specific trend or unintentional to ensure the chart is not crowded. To prevent it, double-check that something is not critical to the context and overall understanding of the chart before omitting it. 
  • Correlating causation: It is the assumption that because two variables changed simultaneously, one caused the other. This should not be taken as an assumption, and causation should always be confirmed. 

Learn more about this data visualization methodology by exploring our guide on misleading data visualizations

5. Take Advantage Of Color Theory

The most straightforward of our selected data visualization techniques - selecting the right color scheme for your presentational assets will help enhance your efforts significantly. 

Colors not only help in highlighting or emphasizing areas of focus, but they are also proven to be a key factor in the user’s decision-making process, as specific colors are known to cause certain emotions in people. Therefore, putting some thought into the process is very important. For instance, you should consider preconceived color associations that users might have, such as associating lighter colors with lower or median values or red and green showing negative and positive results. Taking advantage of these natural associations can help you build visuals that will be automatically engaging and understandable for the audience. 

On that same note, using a color palette that matches the business’s branding will also make the visuals more engaging and familiar. If you choose to go this route, ensure you respect the text and the use of white space. The principles of color theory will have a notable impact on the overall success of your visualization model. That said, you should always try to keep your color scheme consistent throughout your data visualizations, using clear contrasts to distinguish between elements (e.g., positive trends in green and negative trends in red). As a guide, people, on the whole, use red, green, blue, and yellow as they can be recognized and deciphered with ease.

6. Prioritize Simplicity  

Another technique that should not be ignored is always to keep your design simple and understandable, as that is the key to a successful visual. To do so, you should avoid cluttering the graph with unnecessary elements such as too many labels, distracting patterns or images, and colors that are too bright, among other things. Another important thing to consider to ensure simplicity is to use fonts that are classic and easy to understand. Avoid italics or other “artistic” fonts to prevent your text from taking the attention away from the main message of your graph. 

Most importantly, when designing your visuals, stay away from 3D effects and any other element that can make the graphic overwhelming to the eye, such as borders, color gradients, and others. As mentioned in the previous point, stick to a light color palette that is not tiring to the eye. In a business context, it is also a good idea to use the colors, font, and overall brand identity of the business to boost the audience’s engagement towards your visuals. 

In the context of generating a dashboard or report where you need to include multiple visuals, it is recommended to avoid cluttering them with too many graphs. Stick only to the ones that will help you tell a compelling story. More on this point later in the post. 

7. Handle Your Big Data

With an overwhelming level of data and insights available in today’s digital world - with roughly 1.7 megabytes of data to be generated per second for every human being on the planet by the year 2020 - handling, interpreting, and presenting this rich wealth of insight does prove to be a real challenge.

To help you handle your big data and break it down for the most focused, logical, and digestible visualizations possible, here are some essential tips:

  • Discover which data is available to you and your organization, decide which is the most valuable, and label each branch of information clearly to make it easy to separate, analyze, and decipher.
  • Ensure that all of your colleagues, staff, and team members understand where your data comes from and how to access it to ensure the smooth handling of insights across departments.
  • Keep your data protected and your data handling systems simple, digestible, and updated to make the visualization process as straightforward and intuitive as humanly possible.
  • Ensure that you use business dashboards that present your most valuable insights in one easy-to-access, interactive space - accelerating the visualization process while also squeezing the maximum value from your information.

8. Use Ordering, Layout, And Hierarchy To Prioritize

Following on our previous point, once you’ve categorized your data and broken it down into the branches of information that you deem to be most valuable to your organization, you should dig deeper, creating a clearly labeled hierarchy of your data, prioritizing it by using a system that suits you (color-coded, numeric, etc.) while assigning each data set a visualization model or chart type that will showcase it to the best of its ability.

Of course, your hierarchy, ordering, and layout will be in a state of constant evolution, but by putting a system in place, you will turn your visualization efforts speedier, simpler, and more successful.

9. Utilize Word Clouds And Network Diagrams

An example of a word cloud technique

**click to enlarge**

To handle semi-structured or decidedly unstructured sets of data efficiently, you should consult the services of network diagrams or cloud words.

A network diagram is often utilized to draw a graphical chart of a network. This style of layout is useful for network engineers, designers, and data analysts while compiling comprehensive network documentation.

Akin to network diagrams, word clouds offer a digestible means of presenting complex sets of unstructured information. But, as opposed to graphical assets, a word cloud is an image developed with words used for particular text or subject, in which the size of each word indicates its frequency or importance within the context of the information.

10. Use Text Carefully 

So far, we’ve made it abundantly clear that the human brain processes visuals better than text. However, that doesn’t mean you should exclude text altogether. When building efficient graphics with your data, the use of text plays a fundamental role in making the graphs understandable for the audience. That said, it should be used carefully and with a clear purpose. 

The most common text elements you can find in data visualizations are often captions, labels, legends, or tooltips, to name a few. Let’s look at each of them in a bit more detail. 

  • Captions: The caption occupies the top place in a graph or chart, telling the user what he or she should look for in that visual. When it comes to captions, you should always avoid verbosity. Keep them short and concise, and always add the units of measurement. 
  • Labels: Labels describe a value associated with a specific data point in the chart. Here it is important to keep them short, as too long labels can crowd the visual and make it hard to understand. 
  • Legends: A legend is a side section of a chart that gives a brief description to help users understand the data being displayed. For example, what each color means. A good practice when it comes to legends is to arrange them per order of appearance. 
  • Tooltip: A tooltip is a visualization technique that allows you to add extra information to your graphs to make them more clear. Now, adding them under each data point would totally overcrowed them. Instead, you should rely on interactive tooltips that show the extra text once the user hovers over the data point. 

By following these best practices, you will ensure your text brings added value to your visuals instead of making them crowded and harder to read. 

11. Include Comparisons

This may be the briefest of our data visualization methods, but it’s important nonetheless: when you’re presenting your information and insights, you should include as many tangible comparisons as possible. By presenting two graphs, charts, and diagrams together, each showing contrasting versions of the same information over a particular timeframe, such as monthly sales records for 2016 and 2017 presented next to one another, you will provide a clear-cut guide on the impact of your data, highlighting strengths, weaknesses, trends, peaks, and troughs that everyone can ponder and act upon.

12. Tell Your Tale

Similar to content marketing, when you're presenting your data in a visual format with the aim of communicating an important message or goal, telling your story will engage your audience and make it easy for people to understand with minimal effort.

Scientific studies confirm that humans, at large, respond better to a well-told story, and by taking this approach to your visualization pursuits, you will not only dazzle your colleagues, partners, and clients with your reports and presentations, but you will increase your chances of conveying your most critical messages, getting the buy-in and response you need to make the kind of changes that will result in long-term growth, evolution, and success.

To do so, you should collate your information, thinking in terms of a writer, establishing a clear-cut beginning, middle, and end, as well as a conflict and resolution, building tension during your narrative to add maximum impact to your various visualizations.

13. Merge It All Together

Expanding on the point above, in order to achieve an efficient data storytelling process with the help of visuals, it is also necessary to merge it all together into one single location. In the past, this was done with the help of endless PowerPoint presentations or Excel sheets. However, this is no longer the case, thanks to modern dashboard technology. 

Dashboards are analytical tools that allow users to visualize their most important performance indicators all on one screen. This way, you avoid losing time by looking at static graphs that make the process tedious. Instead, you get the possibility to interact and navigate them to extract relevant conclusions in real time. Now, dashboard design has its own set of best practices that you can explore. However, they are still similar to the ones mentioned throughout this post. Let’s look at an example of a sales dashboard to put all of this into perspective. 

Sales dashboard as an example of how data visualization techniques can allow businesses to build efficient data stories for their strategic decisions

**click to enlarge**

As seen in the image above, this sales dashboard provides a complete picture of the performance of the sales department. With a mix of metrics that show current and historical data, users can take a look into the past to understand certain trends and patterns and build an efficient story to support their strategic decisions. 

14. Make It Interactive 

Even though graphs, charts, infographics, and other types of visuals have been a part of our world for decades now, the use of data has always been exclusively reserved for people with knowledge of the subject, leaving non-technical users behind. Luckily, this thought has changed over the years, as experts have realized the great affinity that humans have with visuals. This has created a shift in the DataViz industry, where designers have begun to prioritize aesthetics and design as a way to convey information in an understandable way. Part of this change has been to introduce interactivity as a key element in their graphics. Making it one of the biggest advantages of data visualization today. 

In short, interactive elements help businesses and users bring their visuals to life by giving them the power to explore and navigate the data and extract powerful insights from it. Tools such as datapine provide multiple interactivity features that are easy to implement and use. For instance, a drill-down filter enables users to dig into lower levels of hierarchical data without having to jump to another chart. This is valuable in a number of opportunities; for example, when looking at sales by country, you can use a drill down filter to click on a specific country, and the whole chart will change to show sales by city of that country. 

Another valuable interactivity option is the time interval widget. This feature allows you to add specific buttons to your charts that enable you to change the period of the data being displayed. For example, if you are looking at a bar chart showing sales by month and realize that a particular month is lower than expected, the time interval widget will allow you to dig deeper into that particular month by looking at the weekly or even daily performance. 

To learn more about the topic of interactivity, check out our guide on the top interactive dashboard features. 

15. Consider The End Device

As we almost reached the end of our list of insightful data visualization methods, we couldn’t leave a fundamental point behind. We live in a fast-paced world where decisions need to be made on the go. In fact, according to Statista, 56,89% of the global online traffic corresponds to mobile internet traffic. With that in mind, it is fundamental to consider device versatility when it comes to building your visuals and ensuring an excellent user experience.   

We already mentioned the importance of merging all your visuals together into one intuitive business dashboard to tell a complete story. When it comes to generating visuals for mobile, the same principles apply. Considering that these screens are smaller than desktops, you should make sure only to include the graphs and charts that will help you convey the message you want to portray. You should also consider the size of labels and buttons, as they can be harder to see on a smaller device. Once you have managed all these points, you need to test on different devices to ensure that everything runs smoothly.  

16. Apply Visualization Tools For The Digital Age

We live in a fast-paced, hyper-connected digital age that is far removed from the pen and paper or even copy and paste mentality of the yesteryears - and as such, to make a roaring visualization success, you should use the digital tools that will help you make the best possible decisions while gathering your data in the most efficient, effective way.

A task-specific, interactive online dashboard or tool offers a digestible, intuitive, comprehensive, and interactive means of collecting, collating, arranging, and presenting data with ease - ensuring that your techniques have the most possible impact while taking up a minimal amount of your time.

17. Never Stop Learning

As you’ve learned throughout this list of 17 techniques of data visualization, building graphics is a process that requires a lot of skills and thoughtful consideration. While following these best practices should help you build successful visuals for multiple purposes, this is a process that requires practice and consistency. For that reason, our last piece of advice is never to stop learning. After your visuals are generated, gather feedback from your audience and rethink your process to make it a bit better on every ocasion. As the old saying goes, practice makes perfect. So don’t be afraid to look at your work with a critical eye. 

Summary & Next Steps 

As seen throughout this guide, data visualizations allow users and businesses to make large volumes of relevant data more accessible and understandable. With markets becoming more competitive by the day, the need to leverage the power of data analytics becomes an obligation instead of a choice, and companies that understand that will have a huge competitive advantage. 

We hope these data visualization concepts served to help propel your efforts to new successful heights. To enhance your ongoing activities, explore our cutting-edge business intelligence and online data visualization tool.

To summarize our detailed article, here is an overview of the best data visualization techniques:

  1. Know your audience
  2. Set your goals
  3. Choose the right chart type
  4. Be careful not to mislead
  5. Take advantage of color theory
  6. Prioritize simplicity
  7. Handle your big data
  8. Use ordering, layout, and hierarchy to prioritize
  9. Utilize word clouds and network diagrams
  10. Use text carefully
  11. Include comparisons
  12. Tell your tale
  13. Merge it all together
  14. Make it interactive
  15. Consider the end device
  16. Apply visualization tools for the digital age
  17. Never stop learning

To get a more in-depth insight into what visualization techniques can do for you, try our 14-day trial completely free!