What is data visualisation? 8 essential types for businesses

With the growing availability of affordable software tools, small businesses find themselves with access to huge amounts of business data. But having access to this information isn’t the same thing as really understanding it. For your business data to have value, you need to analyse it effectively.

One of the best ways to achieve that is with the help of data visualisation. In this article, we’ll explore data visualisation in more detail by diving into the following topics:

• What is data visualisation?
• The importance of data visualisation in business
• Different types of data visualisations
• Analytics and insights from the Uber Eats platform

Data visualisation is the practice of translating data into visual formats that make the information easier to understand. For many people, turning numerical data into visual charts and graphs helps you process what you’re seeing – you can spot trends and changes in data over time and see how different data sets relate to one another.

As access to data has grown in recent years, so has the availability of tools with data visualisation features. Small businesses will find that many of the products you have for other purposes will also include some forms of data visualisation. These products may include:

• POS (point-of-sale) systems
• Third-party delivery platforms
• Marketing software
• Accounting software
• Inventory management software

For anything your tech stack doesn’t already cover, creating new data visualisations is now easier than ever with user-friendly data visualisation tools.

Data visualisations come in many formats, each suited to different types of datasets or goals. Using the right data visualisation for the job will help you maximize your business data. For most small businesses, these are some of the main types of data visualisations you’ll encounter:

A bar chart, sometimes called a bar graph or column chart, is a good way to see comparisons between different categories of data. Bar charts include a horizontal axis and a vertical axis (often referred to as the x axis and y axis, respectively) that each represent a different kind of value – for example, one might be sales volume and the other dates, as in the first graphic below. Viewing your data in this format can help you identify trends and make comparisons.

You can pack more insights into a bar chart with a stacked bar chart, which plots multiple related data points onto one bar, to better visualise how they compare. The first chart below, for instance, not only shows total sales by date but also makes it easy to see how many sales came from organic search versus from ads. And you don’t have to stop at charting 2 values in each bar. The customer retention data visualisation below uses 4 colours to help business owners track how often customers return, separated by month.

Pie charts use a circular shape to illustrate quantitative data. Each slice of the pie represents a proportionate part of the whole. The slices visualise percentage data that adds up to 100%.

The pie chart below provides a breakdown of the most common issues customers report with delivery orders. Seeing the visual difference between 85% (items missing, in this case) and 11% (wrong order received) can clarify the relative scale of each kind of error and help the business prioritise which strategies to emphasise to reduce overall issues.

Line graphs show the progression of data over a period of time. Like bar graphs, they include a horizontal (x) axis and vertical (y) axis. The horizontal axis is used for units of time, such as months, days or years. The vertical axis can be for any quantitative type of data you track, such as the amount made in sales, number of orders or average ticket size. Line graphs help you see how results have changed in a set time period and identify notable trends in any peaks or valleys in the data.

As shown in the line graph below, you can plot more than one line on your graph to see the relationship between multiple datasets. In this case, the graph shows how sales numbers compare for 2 different years. You could also use this kind of data visualisation to compare your numbers versus industry averages or compare the results from 2 store locations for the same time period.

A funnel chart is a useful tool for visualising data that shows a progression over sequential steps or stages. For businesses, funnel charts are a common form of data visualisation for displaying the buyer’s journey, or the likely steps a consumer will take from first learning of your brand to becoming a long-term customer.

Funnel charts can help you track how well your marketing and sales process is moving your audience through to different stages. With a conversion funnel visualisation, like the one shown below, you can identify if there’s a particular step in the process where you tend to lose customers. If the funnel shows a notable drop-off between adding items to a cart and placing an order, that suggests there’s some issue stopping customers from taking that final step, like a high delivery fee or long wait time. By revealing a potential problem, a funnel chart can help you diagnose the issue and fix it.

Tables allow you to line up a list of data points in a series of rows and columns and sort them as needed. Sorting is a powerful feature for gaining a new perspective on the data in a table. You can easily reorder your data based on different questions you have. For example, you can quickly sort by the highest-grossing item to determine your most profitable products or see which items show the biggest change in sales to measure the results of a promotion.

As evidenced in the table below, you can use colors to help illustrate an increase (green) versus a decrease (red) in the data, and you can use images to remind you what the numbers in each line refer to. Seeing the pictures of your most popular items in a list next to the relevant data can make it easier for you to process.

Heat maps use colour gradients to illustrate quantity or intensity. Their formatting can vary. A heat map can be in a table-like format as shown in the example below, which uses the 2 axes to break down the number of order issues by day of the week and time of day. You may also see website heat maps that measure how different parts of a webpage attract visitors’ attention.

Another common version is a heat map that uses a geographic map to show how the distribution of something varies across locations. Geographic heat maps are often called dot maps or distribution maps. Small businesses could use a dot map to understand where the bulk of your customers are located, to help make decisions about your delivery range or where to open new locations.

While the appearance of heat maps can vary considerably based on the format used, a common feature is using different shades of colour to illustrate a point. In the heat map below, the darkest shades of red emphasise the times when the business sees the highest occurrences of order errors – the red is an intentional choice, as it points to an issue to be aware of.

A gauge is a data visualisation that takes the form of a half-circle to illustrate a percentage. Gauges are often used to show progress toward a goal. You’ve probably encountered them in a fundraising context before.

A gauge can help provide a simple snapshot of how well you’re performing in a particular area. In the example below, a business can quickly see what percentage of customers are happy with the items they’ve received. They can also dig into that data to see at a glance how customers feel about specific items.

A percentage bar is another option for illustrating percentages visually. Instead of a pie or a gauge, you can use colours applied to a bar format to better reveal proportions or comparative data that adds up to 100%.

Percentage bars are typically used to either:

• Illustrate how close a data point comes to reaching 100%, similar to how you would with a gauge, or
• Show the comparison between 2 data points that add up to one whole measure, as in the example below that shows the relative share of new and returning customers

Percentage bars are a fairly simple type of data visualisation that can help you make more sense out of small datasets.