If you’ve ever looked at a spreadsheet full of data, you’re probably familiar with that overwhelming feeling of information overload. And if you’re a merchant, business owner or analyst with multiple data sources at your fingertips every day, you’re definitely familiar with it.  How do you organize and present all of the data your business (or your team) generates in a way that others can easily understand - and that helps you pull out the most relevant insights for strategy and decision-making?  Of course, you can always write out a summary of what the data is indicating, but if you want your reports to make an impact with stakeholders, potential investors, or other business partners - not to mention better scale as data and insights change over time - you’ll want to use data visualization. Visualization is the most efficient and effective way to make sense of your multichannel data - and use it to drive your business strategy forward.  Here, we’ll cover what data visualization is, how you can begin executing it, different types of data visualizations and when to use them, as well as your options for delivering data visualizations and reports to stakeholders.

What is data visualization?

In a nutshell, data visualizations are graphics that reveal data, or mapping between graphic marks and corresponding data values. They help simplify overwhelming spreadsheets or data tables into imagery that is easier to digest and derive insights from.  The beauty of this kind of presentation? It can take an enormous amount of data and compress it into its simplest form, so that complex ideas and insights can be communicated clearly.  Humans are a visual species (65% of us are visual learners), and we can process a lot of information from seeing series of colors, shapes and patterns presented with the proper context. This makes it easy for us to recognize repeating trends, insights and outliers that then help inform the choices we make about how to drive our business.  As our world becomes increasingly data-driven, keeping up with the ability to consolidate, sort and present an ever-increasing volume of data is a must for any business - and data visualization can help.

How does data visualization work?

If you want to start using data visualization to improve your reporting and enhance your business operations, there are a number of different ways to get started. Many different business intelligence platforms exist to help you organize and visualize your data - at the highest level, you’ll need a system that connects the data sources you want to analyze (whether that’s spreadsheets you’ve created or the data sources themselves via API) and allows you to access prebuilt visualizations or create your own reports. Which one will work best for you depends on the data sources you want to analyze, your level of data analysis expertise, and how much customization you need.  But to get the most from the data you’re collecting, it’s important to have a basic understanding of what visualizations to use and when. Without this foundation, your data visualizations and reports won’t be as impactful for your business.

 
• Ask yourself and your team these questions before starting:
 
• Who is the visualization or report designed for (internal vs. external stakeholders, executives vs. marketing analysts)?
 
• What metrics are being analyzed, and how many data sets are being reported on?
 
• How does it make sense to group your data (daily, weekly or monthly)
 
• What is the core question you are trying to answer or insight you are trying to achieve?
 
• What kind of visualization fits best?

Keep these questions in mind as you read through the list of visualization options below. There are several visualization methods to choose from, but this is by no means an exhaustive list - see the suggested resources at the end for more ways to make your data shine.

Powerful examples of data visualization

1. Line chart

Line charts are one of the most common types of data visualization and have been around for a long time - for good reason. They are most effective at showing the progress of data over time. If tracking a particular metric over a specific timespan is your main goal, a line chart is probably your best option. Line charts are excellent for tracking sales, advertising campaigns and performance KPIs over time.  Advantages of line charts include:

 
• They are easy to create and understand
 
• They can represent multiple metrics at once
 
• They highlight relevant trends and insights over time
 
• They are helpful for forecasting and decision-making

When do line graphs not make sense? They will not be as helpful if you’re trying to display quantities, categorical data, part-to-whole comparisons or very sparse data sets.

Example: A line graph in Glew showing revenue by week for a two-month period, compared to the same period in the previous year

2. Vertical and horizontal bar charts

Bar charts are best used when you have comparative data that you need to visualize. If you have several different metrics or categories of data that it would be helpful to see side by side, a bar chart will be the best chart option.  Whether you want to present data vertically or horizontally will depend on your data set. Vertically is usually ideal; however, if you have categories with longer names, or a much larger data set, horizontal may be the way to go.  Bar charts can be used to compare many types of data, including marketing KPIs and channel performance, customer groups (like new versus repeat customers) and product insights (like total orders for different product categories or SKUs).

Example: A bar graph in Glew showing revenue by channel, grouped by week, for a two-month period

3. Pie chart

When the data you’ve collected requires a part-to-whole relationship to be represented, a pie chart is the best way to sum that up. A part-to-whole relationship is important when you want to know what percentage of your business a certain category represents, such new customer acquisition or ad spend from different marketing channels, or representation of a certain category of products or certain segment of customers as compared to the whole. This can help you see where to shift focus when making decisions for your company.  Follow these do’s and don’ts when using pie charts to ensure simplicity and clarity.  Do:

 
• Ideally limit sections to 5 or fewer for readability and ease of understanding
 
• Only use as many colors as you need to distinguish sections - too many can be visually confusing
 
• Make sure that the pie sums to 100% when using percentages, and make sure it’s a meaningful whole

Don’t:

 
• Use 3D effects or explode your chart
 
• Use more than one pie chart and require an audience to compare across them
 
• Omit the percentages of each of part to the whole, if using percentages

Example: A pie chart in Glew showing zero, one, two, three, four and multiple purchase customers over the past year (paired with a corresponding line chart)

4. Area chart

Area charts are similar to line charts, but differ slightly in that they are filled with color below the lines to indicate the magnitude of changes over time. While this type of data can be represented with a line chart, it can be more striking with an area chart, especially when the metrics you’re measuring have had a major jump or drop over time that you want to highlight.

Example: An area chart in Glew showing revenue from all customers compared to new customers over one-month period

5. Data tables

Tables are exceptionally useful when you need to be able to quickly summarize, sort, group, filter, sum or average large amounts of data. While a table isn’t a very impactful visualization on its own, it’s useful when it comes to organizing data into useful subsets - which can then be used to create more compelling visualizations.  While a pie chart or line graph might be able to perfectly represent a static set of data, a table is dynamic and can help you organize your data set in a number of different ways.  In the table below, you could sort your customers by relationship length, total number of orders, or customer status, or filter by a number of additional data points, including first or last order date, revenue, VIP or loyalty status or average order value.

Example: A sample customer table in Glew, showing metrics for individual customers and filterable/segmentable by different metrics and behavior

You can then use data from that table to create more specific visualizations, like the below segment on high AOV shoppers that includes line graphs for number of orders and average order value over time.

Example: A customer segment in Glew showing high AOV customers, including total number of customers, revenue from the segment, total number of orders over time and average order value over tie

Data visualization delivery

Once you’ve determined what types of visualizations work best for the data you have collected, it’s time to choose what delivery method makes sense.  If you need to get information out to customers, stakeholders, investors or employees, an emailed report can be a good method of distribution, especially if this data is being tracked over a period of time where regular updates would be beneficial for your audience.  If you are using a data analysis or business intelligence platform, there are often in-app visualization options, such as templated or custom dashboards, email or PDF report delivery and even scheduled ongoing reports.  To decide on the best method for your data visualization delivery, consider:

 
• Who needs to see your data visualizations? Are they inside or outside of your organization?
 
• Do your visualizations need to be interactive or static?
 
• Should the data update automatically over time?
 
• Do you need recurring daily, weekly or monthly reports?

Data visualizations made easier with Glew  Glew’s business intelligence tools provide the option for out-of-the-box and custom visualizations and dashboards (like the examples you’ve seen here), as well as scheduled and automated reports for both internal and external stakeholders. Learn more about data visualization and reporting with Glew:  

Wrapping up

Data visualization is a powerful way to make sense of all the data you have at your fingertips - and use it to gain meaningful insights for your business. However, effective data visualization requires understanding your data set, having the right tools to help visualize it, and knowing what type of data visualization makes sense for the questions you’re trying to answer.  Before you go, remember these key takeaways:

 
• Data visualizations are graphics that map visual marks to corresponding data values
 
• Data visualizations make it easier for us to recognize repeating trends, insights and outliers that can help us understand complex data sets and make smarter decisions
 
• To execute data visualizations, you’ll need a platform that can connect your data sources and allows you to access or create visualizations - whether that’s prebuilt dashboards, custom reports or something in-between
 
• Types of data visualizations include line charts, bar charts, pie charts, area charts, and tables. What visualization work best depends on your data set and what insights you are trying to glean from it
 
• When creating your data visualizations, consider who the audience is, what metrics and data sources you need to include, how you should group your data, what core question you’re trying to answer, and what type of visualization best meets your needs
 
• Data visualizations can be delivered via one-time email report, scheduled or automated report, PDF, interactive dashboard or a number of other methods, depending on your reporting and analytics software. Consider who needs to view your report and what functionality they need to determine the best delivery method.