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Because there is more data, there are more sorts of graphs and charts than ever before. In fact, the amount of information created, captured, copied, and consumed in 2025 will be nearly quadruple that of today.
As a result, data visualisation is critical for organisations. Various tables and charts might assist you:
- Encourage your team members to take action.
- Goal advancement will impress stakeholders.
- Display to your viewers what you believe as a company.
Data visualisation fosters trust and can unite disparate groups around new initiatives.
Creating charts and graphs can be difficult and time consuming. You can check out these PowerPoint charts templates
How to choose the right data:
Multiple data sources exist in channels such as social media and blogs and managing these complex content assets can be burdensome. What should you be monitoring? What is most important? How do you display and analyse data in order to glean insights and actionable data?
1. Determine your data presentation objectives.
Do you wish to persuade or clarify something? Are you attempting to display data that assisted you in finding a solution, or are you attempting to express a change that is occurring?
A graph assist you in comparing different figures, comprehending how distinct components affect the overall, or analysing trends. Graphs and charts can also help you recognise data that differs from what you’re used to or see connections between groups.
Clarify your objectives, then utilise them to influence chart choices.
2. Determine what data is required to reach your goal.
Different sorts of data are used in various charts and graphs. Graphs are typically used to depict numerical data, whereas graphs are a visualization of information that might or might not include numbers.
So, while every graph is a type of chart, not every chart is a graph. If you don’t have the data you need, you may need to spend some time gathering it before creating your chart.
3. Collect your info.
Most organisations collect numerical data on a regular basis, but you may need to spend some extra time gathering the appropriate data for your chart. You may require qualitative data in addition to quantitative data techniques that track traffic, income, and other user data.
Here are some alternative methods for gathering data for your data visualisation:
- Interviews
- Surveys and quizzes
- Customer feedback
- Examining client records and documents
- Community councils
4. Choose the appropriate graph or chart.
Using the incorrect visual aid or defaulting to the most frequent kind of data visualisation could confuse your audience or lead to incorrect data interpretation.
However, a chart is only beneficial to you and your business if it clearly and successfully expresses your argument.
Here’s an outline of graph and chart kinds to help you better understand each one and how to utilise them.
1. Bar Chart
When one data title is long or there are more than ten things to compare, a bar chart should be used to reduce clutter.
Bar graphs can be used to compare data across groups or to follow changes over time. Bar graphs are especially effective when there are significant changes or when comparing one group to another.
The above example compares the number of clients based on business role. It is clear that individual contributors have more than double the number of clients per role than any other group.
2. Column Diagram
Use a column chart to compare two or more items, or to compare two or more items across time. This format could be used to see revenue per homepage or clients by close date.
While column charts display data vertically, bar graphs display data horizontally. While both can be used to depict data changes, column charts are ideal for negative data.
Warehouses, for example, frequently measure the number of incidents that occur on the shop floor. When the number of events goes below the monthly average, a column chart in a presentation might make that drop more visible.
3. Line graphs
A line graph displays trends or progression over time and can be used to display a wide range of data. When charting a continuous data set, you should use it.
Line graphs allow viewers to track changes over short and extended time periods. As a result, these graphs are useful for detecting minor changes.
Line graphs can be used to compare changes for multiple groups over the same time period. They’re also useful for determining how different groups interact with one another.
This type of graph could be used by a company to compare sales numbers for various services or products over time. These charts also are useful for assessing the performance of service channels.
4. Pie chart
A pie chart depicts a fixed number and how sections represent a portion of a larger whole – the structure of something. A pie chart depicts percentages of numbers, and the total sum of all parts must equal 100%.
Another example of clients by corporate role is seen in the image above.
The bar graph example indicates that individual contributors outnumber all other roles. However, this pie chart shows that they account for more than half of all customer positions.
Pie charts make it simple to see a piece in relationship to the whole, hence they are useful for displaying:
- Customer identities as they relate to all customers
- Revenue from your most popular products or product types in relation to all product sales
- Percent of total profit from different store locations
5. Funnel chart
A funnel chart depicts a succession of steps as well as the completion percentage for each one. This type of chart is useful for tracking the sales process or conversion rate along a succession of pages or processes.
The advertising or sales funnel is the most popular application for a funnel chart. However, there are numerous other applications for this adaptable chart.
This graphic can help you easily identify what inputs or outputs effect the final results if you have at least 4 stages of time series.
A funnel chart, for example, might show you how to optimize your buyer path or shopping trolley workflow. This is because it can assist in identifying large drop-off points.
