Line chart visualization helps businesses obtain valuable insights into evolving patterns in their data, letting them swiftly respond to opportunities and challenges. In this blog, we’ll go into detail about when and how to use line charts and look at some examples.
What is a line chart?
A line chart is a kind of chart that employs lines to link data points. It’s a simple and effective way to visualize how the data changes over a time period using continuous data. Businesses can track stock price performance, study weather trends, or monitor population growth.
Benefits of using line charts in an organization
- Visualizing trends and patterns: Line charts provide a clear and intuitive visual depiction of how values change, easing the identification of trends, seasonal variations, and other patterns.
- Data comparison: Allows users to compare various data series on the same chart, enabling them to analyze relationships among variables and identify correlations.
- Simplicity and clarity: They are simple and easy to understand, making them accessible to many stakeholders and ensuring that complex information is communicated effectively.
- Saving time and resources: Line charts enable faster comprehension and analysis, saving an organization time getting skilled personnel to help interpret the data.
When to use a line chart
- To show trends over time.
- When comparing multiple data sets.
- To show dynamic changes in distribution.
- To show forecasts of future values.
When not to use line charts
- When handling categorical data with no natural order.
- When showing changes in a variable over time for multiple categories.
- When you have a small data set.
- When dealing with data that is not in even intervals.
- When trying to showcase a complex relationship between two factors.
- When dealing with data that is not numeric.
How to configure line charts with Bold BI
Configuring a line chart involves several steps to establish an attractive visualization. Use this documentation to learn how to configure a line chart in Bold BI and to better understand how to use its properties to present your data.
Common mistakes to avoid while using a line chart
- Inappropriate scaling: Improper scaling of the axes can misrepresent the data and create a misleading perception of trends.
- Lack of annotations: Failing to provide relevant annotations can make it challenging for viewers to understand the significance of the data.
- Using the wrong line style: The line style you use should be suitable for the data you are plotting.
- Inconsistent data points: Data points at irregular intervals can lead to distorted perceptions of the data trends.
- Misusing 3D line charts: Avoid using 3D line charts, as they can introduce visual distortions and make it challenging to interpret the data accurately.
Best practices for using a line chart
- Use clear labels for the axes: The x-axis and y-axis should be clearly labeled to enable the reader to read and understand the units of measurement used in the data.
- Use a consistent line style.
- Add a title to the chart: The title should be a clear and concise summary of the information in the chart.
- Avoid using too many lines on the same chart: Using too many lines makes it difficult for the reader to see the trends in the data.
- Start the y-axis at zero: Starting the y-axis at zero makes it easier for the reader to see the actual changes in the data.
- Use gridlines to help the reader read the chart: Use light gridlines to enable the reader to follow the lines from the axes onto the chart, clarifying the values.
- Use a legend to identify the different lines on the chart: If you are using multiple lines on the same chart, it is helpful to use a legend to identify them. The legend should be placed in a visible location on the chart.
Use cases of line charts
Marketing: sales velocity by month
Sales velocity by month is a marketing metric that measures the rate at which a company’s products are being sold over the course of months. By plotting this data on a line chart, marketers can quickly identify patterns, seasonal trends, and potential growth opportunities.
Healthcare: patients by week
This patients by week widget is a healthcare metric used to visualize the variation in patient numbers over time, enabling healthcare professionals to track weekly trends, identify peak periods, and allocate resources efficiently, essentially contributing to better patient care and resource management.
Human resources: full-time vs. part-time employees
This full-time vs. part-time employees widget is an HR metric comparing the number of full-time and part-time employees in a company over a several years. It enables users to identify workforce trends, patterns, and fluctuations.
Support: duration of calls by date
This duration of calls by date widget is a customer service metric that compares call duration patterns over time, allowing teams to address recurring challenges in time management and enhance customer satisfaction by taking steps to reduce wait times during higher-volume times.
Finance: debt ratios
This debt ratios metric provides insight into a company’s financial leverage and overall financial health. This ratio is beneficial to the finance team as it helps them assess the company’s ability to repay its debts, manage its financial risk, and make strategic investment decisions.
Agile: completed story points trend
The completed story points trend metric for agile allows teams to assess their work efficiency and predict future performance. Consequently, it helps in better project planning, resource management, and timely project completion.
IT: project progress by date
The project progress by date metric helps IT teams track and manage project timelines effectively. This metric is beneficial, as it aids in early detection of delays, enabling proactive problem-solving and efficient resource allocation.
Supply chain: inventory days of supply
By plotting inventory days of supply on a line chart, organizations can gain insight into their inventory management performance, enhance stocking strategies, minimize carrying costs, and ensure a suitable balance between supply and demand. Predicting inventory days of supply for upcoming months helps plan future inventory management, optimizing supply chain efficiency and cost-effectiveness.
Education: Student enrolment by year
This line chart enables educational institutions to track and analyze enrollment variations , identify peak admission years, and in conjunction with other widgets, evaluate the impact of different factors like demographic changes or policy adjustments on student intake. Educators can make informed decisions, allocate resources efficiently, and strategize for future growth. Student enrollment data over multiple years can predict future enrollment trends, enabling educational institutions to plan effectively.
In conclusion, a line chart is a powerful visual aid, outlining temporal trends and patterns. Its simplicity makes insight into the metrics it displays easily accessible, encouraging improved decision-making.