You’ve spent countless hours gathering a large amount of raw data. Your desk is covered with an overwhelming number of documents, notes, and materials. Perhaps you’re asking yourself: what can I do to transform my data into something that is easy to understand? Or maybe, you’re wondering which approach is going to add the most visual appeal.
Our brain processes image 60,000 times faster than text, and 90% of information transmitted to our brain is visual. For this reason, it’s important to utilize data visualization techniques to drive successful results.
What is data visualization?
Data visualization is the process of converting data or other information to a graphical representation. Some of the most common data visualization examples include charts, tables, graphs, timelines, and maps. Data visualization tools are ideal for identifying trends, deviations, and patterns in data sets easily and in a way that these patterns can also be understood by non-technical people.
Data visualization techniques are especially important for organizing very large amounts of data. Big Data is only getting bigger and harder to manage. However, with the right tools, managing it does not have to seem impossible.
Why is data visualization important?
Even the most seasoned people in business struggle to pull out insights from raw data. Many times, taking your data and plugging it into a spreadsheet is simply not enough. Spreadsheets are a great source for analysis, collaboration, and integration, but they should not be seen as an end-all-be-all.
It’s important to take your data one step further. As previously mentioned, our brains process images much faster than text. Data visualization is a fast, simple, and effective way to pull your own insights from raw data while also improving customer relationships.
In addition, every business can find benefits in data visualization techniques. Among others, it lets you identify shortfalls within your company, analyze customer behavior, and make predictions on product placements.
It is the process of taking raw data, converting it into a visual representation, and delivering the data in a simplified form. From there, the data is used to draw conclusions. Scientists are now taking this process one step further by creating Machine Learning algorithms that convert essential data into visual representations.
Data visualization techniques – Which ones to choose?
Your data visualization technique(s) will differ depending on the type of data you’re analyzing. It’s equally important to consider what kind of impact you want to have on your audience.
Here are some of the most used data visualization techniques.
Charts
- Pie chart – One of the most used data visualization techniques. They’re ideal for showing parts to a whole or key takeaways.
- Bar chart – Ideal for showing data distributions or making comparisons that show us common vs. uncommon groups.
- Gantt chart – Commonly used for project management. Gantt charts are essentially timelines that show a project’s progress.
- Waterfall chart – A great way to show how values are affected by certain positive and negative factors. These are commonly used to analyze earnings and spending.
- Area chart – Ideal for analyzing trends over time.
- Pictogram chart - A great way to convert data into simple, easy-to-read images. These tend to be very engaging, especially for a younger audience.
Tables
- Highlight table – An engaging way to guide viewers into quickly interpreting trends and patterns. Table cells are highlighted for simple, easy data comparison.
Graphs
While there are certainly several types of graphs, the type you should use varies by the context of your data.
- Wedge Stack graph – A data visualization technique that depicts hierarchical data in a radial system.
- Streamgraph – This is a form of a stacked area graph that represents the progression (or degression) of a certain group of numeric values on its y-axis along with a set of values on its x-axis.
- Correlation Matrices – There are best used for showing a correlation between variables.
- Bullet Graph – These are useful for comparing the performance of one primary measure to other (one or more) measures. For example, comparing actual sales to target sales for a visual representation of quarterly performance.
Maps
- Heat map – Makes differences in data easier to interpret by categorizing data variations by color.
- Choropleth map – Depicts different numerical values in a common geographical region. Values are commonly organized by varying colors or shades of one color.
- Treemap – Ideal for organizing large amounts of hierarchical data.
Infographics
- Word cloud – This is a type of data visualization technique that analyzes the number of times words are used in the text. It helps viewers to form a conclusion on trends and patterns.
Dashboards
In the most simple terms, dashboards are visual progress reports. It gives you the ability to see progress and the delivery of work, which makes them particularly popular as a tool for Scrum project boards or Key Performance Indicator tracking.
Visualizing classification data – Four popular scenarios explained
So, now that you know all the different options out there, it’s worth asking – what is data visualization used for? There are a wide variety of use cases, but let’s touch on some of the most common scenarios and examples.
Use case #1: Marketing email outbound response categorization
Scenario: You’re in charge of handling an outbound email marketing campaign for the launch of your company’s newest product. To begin, you put together an email that offers existing customers a 15% discount by simply purchasing the product through a link in the message.
Your sales can obviously be viewed by looking at the revenue, but how do you analyze your effectiveness?
You may consider adding classifications such as:
- interested
- not interested
- maybe later
- unsubscribe
Since you're looking at simple variables, you might consider using data visualization tools such as a pie chart, bar chart, or waffle chart to effectively represent these classifications.
The pie chart is going to give you the simplest representation. It would be an ideal data visualization tool for identifying how each classification relates to the effectiveness of your campaign.
A stacked bar chart would be a good data visualization tool to dive a little deeper into each classification. For example, you might want to divide your audience into different categories (e.g. men vs. women). Each of the four classifications would represent a specific color while each audience category would be represented by a bar.
Using data visualization software with waffle chart integrations is ideal for analyzing a set goal or target. For example, you’re looking for at least 75% of your audience to fall into the “interested” or “maybe later” classification. It’s a good tool for viewers to clearly see the majority.
Data visualization software such as Google Sheets or Google Data Studio would allow you to go one step further and update your waffle chart in real time. Down the road, you could even use your chosen data visualization software to compare email campaigns.
Use case #2: Customer support ticket classification
Scenario: Your company wants to compare customer support tickets by their urgency. You’ve decided the best approach is to classify tickets by “urgent” or “not urgent.” The last step of this project is finding a good data visualization software that provides a simple, yet the effective depiction of your findings.
The bar chart is perhaps the best approach for categorizing the two topics. It would also be ideal if you wanted to add customer groups (e.g. new customers vs. existing customers) and classify their tickets by urgency.
Two of the best data visualization software for simple bar charts are Google Sheets and Tableau.
Use case #3: Social listening
Scenario: Your company wants to better understand what customers expect from their brand. Part of this process involves using social media channels to see what customers are saying.
You find the most effective data visualization examples in programs such as Tableau, Google Sheets, and Airtable.
Word clouds might be a technique you find useful. You decide to use them to identify words that appear most frequently across each channel. Furthermore, you can use word clouds to identify how your competitors are being referenced.
You might also consider a bullet graph for analyzing company performance across social media platforms.
Use case #4: Document type categorization
Scenario: You have recently been onboarded with a law firm that oversees filing numerous documents for clients every month. Your goal is to find a visually appealing way to track the status of these documents and establish deadlines for their execution.
You get to work on researching the best data visualization examples for your needs. You find that a Gantt timeline will be the most appropriate for establishing target dates and making sure each document is being executed on time.
Overall, you conclude that Google Sheets and Airtable seem to offer the most promising data visualization design.
Advantages and disadvantages of data visualization
So far, it may seem like approaching data in a way as simple as displaying it in easily digestible graphics can only bring benefits. However, it is important to approach data with the right type of visualization to avoid misrepresentation.
Let’s take a look at the potential advantages and disadvantages of data visualization.
Summary
If your company is looking to simplify its processes through automation, it’s important to consider having a plan for data visualization. Tagging/categorizing data is going to be beneficial for both your company and its customers.
Data is best interpreted when it is organized in a usable, easy-to-understand way. Visual representations are statistically more likely to be remembered than text alone.
There are numerous data visualization designs that exist today. It’s important to identify which designs work best for your company’s needs to drive the most effective results.
When it comes to deriving data and visualizing it, Levity can certainly help out.
Levity is a no-code ML automation platform that automates processes and can help you classify data into different categories, as part of your wider automated workflow process. This, in turn, can help you decide on the best visualization technique – one that will communicate the data and resonate the strongest with your audience.
