After all, it provides pivotal insights into customer behaviour, market trends, and the overall performance of your business. Sounds easy enough, right? Review your data, and get better at everything. That would be the answer if data were easy to comprehend or interpret. Sadly, it’s more often than not the complete opposite. So, how do we extrapolate this much-needed information into a form we can easily digest and drive insights from? 

Allow us to introduce the hero to this story, the humble chart. 

Charts help visualise complex data sets, making it easier to understand patterns, trends, and relationships. So, join us as we explore the importance of using the right charts for your business and how they can enhance your data analysis.

Types of Charts to Consider for Your Business

When choosing a chart to represent your data, it's important to consider the type of information you want to convey. There are various types of charts available, each designed to display specific types of data effectively. Let's take a look at some common chart types and their uses:

Bar chart: 

Bar charts are ideal for comparing data across different categories or groups. They can be used to showcase sales figures, market share, or customer preferences. They are the unsung hero of displaying data quickly and effectively. 

Benefits: 

• Easy to understand and interpret: Bar charts are a simple way to show the relative sizes of different categories or the changes in data over time. The height of each bar is proportional to the value of the data it represents. This makes it easy for people to see how the different parts of the data relate to each other.
• Versatile: Bar charts can be used to display a variety of data types, making them a versatile tool that can be used to visualise a wide range of information.
• Effective for comparing data: Want to compare data across different categories or over time? Bar charts are your friend. This also makes them a useful tool for identifying trends and patterns in data.
• Great for large volumes of data: Bar charts can be used to display a large amount of data in a compact and easy-to-read format. This makes them a useful tool for summarising large datasets.

Examples of their use: 

Bar charts can be used to compare sales figures for different products, track the number of website visitors over time, or show the distribution of student test scores.

Cons: 

• Can be difficult to read when there are many categories, or data points: As the bars start to overlap, it can be difficult to distinguish between them making it difficult to read.
• Not good for displaying data with small differences between categories: The small differences can be lost in the overall size of the bars, meaning that spotting patterns and trends becomes more difficult. 

How to use bar charts more effectively:

Here are some top tips for getting the most out of your bar charts: 

• Use bar charts to display data with a small number of categories (no more than 10).
• Use different colours for the bars to make them easier to distinguish (brand colours look great and keep the marketing team happy!)
• Use a consistent legend to label the different categories.
• Use bar charts to complement other data visualisations, such as pie charts or line charts.

Line chart: 

Line charts are perfect for displaying trends over time. They are commonly used to track sales performance, website traffic, or stock market fluctuations.

Benefits: 

• Easy to understand and interpret: Line charts are a simple way to show the changes in data over time. The slope of the line indicates the direction of the change, and the steepness of the line indicates the magnitude of the change. This makes it easy for people to see how the data is changing over time.
• Effective for showing trends: Trends are patterns that repeat over time, and line charts can help to identify these patterns. This can be useful for making predictions about the future or for identifying areas where improvement is needed.
• Good for large amounts of data: Line charts can be used to display a large amount of data in a compact and easy-to-read format. This makes them a useful tool for summarising large datasets.

Examples of their use: 

Line charts can be used to track the stock market, show the growth of a company's revenue, or plot the temperature over time.

Cons:

• More data points = more difficulty reading: Line charts can become difficult to read when there are many data points. This is because the lines can start to overlap and it can be difficult to distinguish between them.
• Not good for displaying data with sudden changes: Line charts are not good for displaying data with sudden changes. This is because the lines can be misleading and make it difficult to see the overall trend.

How to use line charts more effectively:

Sold on the line chart for displaying your data? Wondering how to use them effectively? Here are some top tips to get you started:

• Use line charts to display data with a small number of data points (no more than 30).
• Differentiate the lines using different colours to make them stand out from one another. 
• Keep the line chart as simple as possible and use a legend that is consistent with your other charts.
• Use line charts to complement other data visualisations, such as bar charts or scatter plots.
  

Scatter plot: 

Scatter plots are effective for visualising relationships between two variables. They can help identify correlations, outliers, or clusters in your data. 

Benefits: 

• Easy to visualise relationships between variables: Scatter plots are a simple way to show the relationship between two variables. The direction of the points indicates the direction of the relationship, and the strength of the relationship is indicated by how close the points are together. This makes it easy for people to see how the two variables are related to each other.
• Effective for showing correlations: Scatter plots are effective for showing correlations between two variables. Correlation is a measure of how closely two variables are related. A positive correlation means that the variables tend to move in the same direction, while a negative correlation means that the variables tend to move in opposite directions.
• Good for large volumes of data: Scatter plots can be used to display a large amount of data in a compact and easy-to-read format. This makes them a useful tool for summarising large datasets.

Examples of their use: 

Scatter plots can be used to show the relationship between height and weight, sales and marketing spending, or test scores and hours of study.

Cons: 

• Can be difficult to read when there are many data points: This is because the points can start to overlap and it can be difficult to distinguish between them.
• Not good for displaying data with outliers: Outliers are data points that are far away from the rest of the data. They can make it difficult to see the overall trend in the data.

How to use scatter plots more effectively:

Here are our top tips for using scatter plots more effectively:

• Use scatter plots to display data with a small number of data points (no more than 30).
• Make the points easier to distinguish by using contrasting colours.
• Keep the scatter plot as simple as possible, keep focus on the scatter plots and not on other decorative elements.
• Scatter plots are great for complementing other data visualisations, such as line charts or bar charts.

Pie Chart: 

Pie charts are used to illustrate the proportionate distribution of data in relation to a whole. They are often used to represent market share, demographic breakdowns, or budget allocations.

Benefits: 

• Easy to understand and interpret: Pie charts are a simple way to show the relative sizes of different parts of a whole. The size of each slice of the pie is proportional to the value of the data it represents. This makes it easy for people to see how the different parts of the data relate to each other.
• Easy to create: Pie charts are relatively easy to create, even with basic data visualisation tools. This makes them a good choice for people who are not familiar with data visualisation software.
• Can be used to display a variety of data types: From categorical data, numerical data, and time series data, pie charts are used to display them all and more. This makes them a versatile tool that can be used to visualise a wide range of information.

Examples of their use: 

Pie charts can be used to show the breakdown of sales by product, the composition of a population by age group, or the distribution of votes in an election.

Cons: 

• Can be difficult to compare different pie charts: This is because the size of the slices is relative to the size of the whole pie, and the size of the whole pie can vary from chart to chart.
• Not good for displaying data with many categories: If the slices of the pie become too small it becomes difficult to distinguish between them. 
• Not good for displaying data with small differences between categories: Small differences can be lost in the overall size of the slices making it difficult to visualise for an audience. 

How to use pie charts more effectively:

So, your mind’s made up and you're set on using a pie chart to display your data. Here are some top tips to get the most out of your delicious pie chart:

• Use pie charts to display data with a small number of categories (no more than six).
• Highlight important data by making the slice of the pie larger or using a different colour. This can be helpful for drawing attention to specific data points.
• Use a consistent legend to label the different categories.
• Pie charts are a good choice for showing the composition of a whole, such as the different types of expenses in a budget or the different sources of income for a company.
• Use pie charts to complement other data visualisations, such as bar charts or line charts.

Box plot: 

A box plot is a chart that uses a box and whiskers to represent data values. It’s often used to show the distribution of data and identify outliers.

Benefits:

• Consistent: Box plots are a standardised way of visualising data, which means that they can be easily interpreted by people who are familiar with them, making them a reliable way to communicate data.
• Effective for comparing distributions: This can be useful for identifying differences between groups or for understanding how a particular group changes over time.
• Flexible: Box plots can be customised to fit the specific needs of the data being visualised. This makes them a versatile tool that can be used in a variety of contexts.
• Can be used to identify outliers: Outliers are data points that are far away from the rest of the data. They can be useful for identifying errors in data or for identifying unusual values that may be worth investigating further.

Examples of their use: 

Box plots can be used to show the distribution of test scores, the range of salaries for a particular job, or the height of a population.

Cons: 

• Can be confusing: Box plots can become difficult to read when there are many data points. As the boxes overlap it can become confusing and difficult to distinguish the data points. 
• Not good for displaying data with continuous distributions: Continuous distributions are distributions where the data can take on any value within a certain range. This is because box plots only show the five-number summary of the data, which may not be enough to accurately represent the distribution.

How to use box plots more effectively:

Box plots are a useful tool for displaying data that shows the distribution of data. However, they do have limitations. Here are our top tips for using them in the right way: 

• Use box plots to display data with a small number of data points (no more than 30).
• Use box plots to understand the spread of data. This can help you to understand the variability of it.
• Use box plots to complement other data visualisations, such as line charts or bar charts.
• Use box plots to compare different types of data like categorical, numeric, or time series.

Area Charts:

Area charts are similar to line charts but filled with colour, which can help emphasise the magnitude of data over time. Just be mindful of the colours you choose otherwise you’ll give yourself a headache trying to decipher which line is which. 

Benefits: 

• Effective for comparing values: Area charts are effective for comparing the values of different groups. This can be useful for identifying differences between groups or for understanding how a particular group changes over time.
• Can be used to show trends: Trends are patterns that repeat over time, and area charts can help to identify these patterns. This can be useful for making predictions about the future or for identifying areas where improvement is needed.

Examples of their use: 

Area charts are great for tracking marketing campaigns, the financial performance of investments, the incidence of different diseases over time, or changes to environmental conditions. 

Cons: 

• Not good for displaying data with sudden changes: This is because the filled regions can be misleading and make it difficult to see the overall trend.
• Hard to compare values: When there are multiple series in an area chart, it can be difficult to compare the values of the different series. This is because the filled regions can overlap and it can be difficult to see the individual values.
• Difficult to spot outliers: Area charts are not good for displaying data with outliers. Outliers are data points that are far away from the rest of the data. They can make it difficult to see the overall trend in the data.

How to use area charts more effectively: 

Okay, so you want to see how data has changed over time and you’ve decided an area chart is the way to go. Here are our top tips for getting the best out of them: 

• Use a consistent scale so that the changes in the data can be easily seen
• Use a clear and concise legend, so that the different filled regions can be easily identified. The legend should also be placed in a location where it’s easy to see.
• The colours used in the area chart should be appropriate for the data being displayed. For example, if the data represents positive and negative values, it is helpful to use contrasting colours so that the differences can be easily seen.
• Gridlines can be helpful for making the area chart easier to read. They also help identify the values of the data.
• Annotations are your friend, use them! For example, you could use annotations to point out sudden changes in the data or to identify outliers.

Analysing Your Data with the Right Charts

Once you’ve selected the right chart type, it's time to dive into data analysis. Remember, charts aren’t just pretty pictures; they provide meaningful insights into every corner of your business. Now, if you really want to get the most out of your visualisations, we suggest the following: 

Simplify and Focus:

Avoid cluttering your charts with excessive data points or unnecessary design elements. Keep them simple, and focus on conveying the key message.

Highlight Key Insights:

Use labels, annotations, or contrasting colours to draw attention to important data points or trends that require further analysis or action.

Compare and Contrast:

Utilise multiple charts to compare different variables or time periods, enabling you to uncover meaningful patterns or discrepancies.

Provide Context:

Always include titles, axis labels, and legends to provide context and help your audience interpret the data accurately.

Update and Iterate:

As your data evolves, so should your charts. Regularly update your visuals to reflect the latest information and iterate on your analysis techniques.

Avoid Common Chart Mistakes for Your Business

While we champion the value in charts, we also understand that people are still making some fairly common mistakes, stopping them from really getting the full benefit and value. Here are some helpful tips you can take to avoid falling into the charting pitfalls: 

Choosing the Wrong Chart Type:

Selecting the wrong chart type can make your data confusing or misleading. Ensure you understand the purpose of each chart type and match it to your data appropriately.

Using Excessive Chart Junk:

Chart junk refers to unnecessary visual elements such as 3D effects, extreme colours, or excessive gridlines. Keep your charts clean and uncluttered to avoid distractions.

Not Providing Adequate Context:

Failing to include clear labels, legends, or units can make your charts difficult to interpret. Always provide context to ensure your audience understands the data presented.

Overcomplicating Charts:

Avoid overwhelming your charts with too much information. Emphasise simplicity and highlight the most critical insights to avoid confusion.

Ignoring Data Integrity:

Ensure your data is accurate, up-to-date, and representative of the information you want to convey. Incorrect or incomplete data can lead to incorrect conclusions.

Exploring the Benefits of Visualising Your Data with Charts

Now that we understand the different types of charts, their proper usage, and how to avoid common mistakes, let's now look at the benefits visualising your data can offer:

Clarity and Understanding:

Charts provide a clear representation of complex data, making it easier to understand and interpret, even for non-technical individuals.

Better Decision-Making:

Visualising data allows decision-makers to identify trends, outliers, and patterns quickly. This leads to informed and data-driven decision-making.

Improved Communication:

Charts are a universal language that transcends barriers. They enable effective communication of information to stakeholders, clients, or colleagues.

Enhanced Data Exploration:

Charts help uncover hidden insights and relationships within the data. They encourage exploration and promote a deeper understanding of your business's performance.

Persuasive Presentations:

Charts are attention-grabbing and compelling. They can help you communicate your message persuasively during presentations or pitch meetings.

Finally, let's take a closer look at how different chart types can help specific areas of your business:

Sales and Revenue:

Use bar charts to compare sales figures across different regions or time periods to identify growth opportunities or areas for improvement.

Marketing and Advertising:

Utilise pie charts to illustrate market share or demographic breakdowns, enabling you to target specific customer segments effectively.

Operations and Efficiency:

Apply line charts to track key performance indicators (KPIs) to monitor operational efficiency over time and identify areas for optimisation.

Customer Behaviour:

Leverage scatter plots to uncover relationships between customer satisfaction and purchase frequency, enabling you to tailor your marketing strategies accordingly.

Financial Analysis:

Utilise area charts to visualise budget allocations or revenue streams over time, enabling you to identify trends and optimise financial performance.

Each chart type brings unique benefits to different areas of your business. By understanding their applications, you can harness the power of charts to drive growth and success.

Remember, data is only as valuable as the insights it provides. By choosing the right charts, analysing your data effectively, and avoiding common charting mistakes, you can transform raw data into actionable information. If you want more information about how to visualise your data to get the most value out of it, visit our Data Visualisation Solution page, or contact us to get started.