What's the best chart for your data analysis? (2024)

Key takeaways from this article

• Audience-tailored visuals:

  Consider your audience's expertise and interest when selecting charts for data analysis. This ensures that the insights you present are engaging and understandable, leading to better-informed decisions.

• Perspective-driven design:

  Choose chart types based on the perspective you wish to convey. This influences how data is received and understood, making it a potent tool for highlighting specific trends or relationships in your data.

This summary is powered by AI and these experts

• Mayur Jadhav Senior Advisor | BI Developer. Writer |…

• • Report contribution
  Data charts are valuable tools in data analysis as they visually represent information, making it easier to understand trends, patterns, and relationships within the data. The common types of data charts include bar graphs, line graphs, pie charts, scatter plots, and more. Utilizing these charts can help you communicate insights and findings effectively to others. For better understanding, minimum one example can be added explaining each types.

  Like

  3

• • Report contribution
  I think the article should be focused on chart types instead of data types with situations where they can be used in the analysis.

  Like

  7

• • Report contribution
  Understanding our audiences is the crucial part here. We can use different charts for the same conditions, but your audience's comfort in understanding and interpreting the data should be of top priority.There will be clients who love tabular representations more than anything, and there will also be people who prefer charts like Bar, Line, Donut, etc. So make sure that your reports align with the preferences of your audience.

  Like

  5

• • Report contribution
  Using histogram is the best way to visualize the distribution of categorical data into bins. Treemap shows hierarchical data as nested rectangles, comparing sizes. Stacked Area Chart displays the trend of multiple categorical variables over time. Mosaic Plot represents the association between two categorical variables.

  Like

  3

• Matt M. Revenue Operations Leader | Revenue Engines Optimized by AI

  • Report contribution
  Categorical data is a statistical data type where each value is a category or group, and each group represents qualitative information. There are two types of categorical data:Nominal: Data that can be grouped into categories but have no order or priority. Examples are hair color, blood type, or ethnic group.Ordinal: Data that can be grouped into categories and have an order (i.e., can be sorted). Examples are rankings (like in a race), education level (high school, undergraduate, postgraduate), and Likert scale survey responses.

  Like

  3

• Adam R. Operational Data Analyst | Customer Support Professional | Team Leader

  • Report contribution
  See Also

  How to choose the right chart type for your data5 Most Common Data Visualization Types and When to Use Them – Customer Service Blog from HappyFox4.1 Choosing a Chart Type – Beginning Excel, First EditionWhat is Data Visualization and Why is it Important? | Definition from TechTarget

  Based on my experience, numerical data is very frequently paired with a temporal component and stakeholders are very practically concerned with changes over time.I've had the best luck displaying those changes as either a heatmap on a matrix, line chart or scatter plot. They are pretty instantly digestible for stakeholders and allow people to get into the next level of questions related to the data.

  Like

  10

• • Report contribution
  Box Plot, also called Box-and-Whisker Plot, summarizes the distribution's five-number summary as minimum, first quartile, median, third quartile, maximum. Bubble Chart combines the concepts of a scatter plot and a bubble size to represent three-dimensional data. Heatmap displays numerical data in a matrix with colors to indicate intensity or value. Violin Plot combines a box plot and a kernel density plot to show the distribution of data. Waterfall Chart demonstrates cumulative effects of positive and negative values over a sequence.

  Like

  5

• Jonathan Morrill Lead Reservoir Engineer

  • Report contribution
  One of the best ways to graphically show the progression from start to finish that includes the reality of uncertainty is to combine the box plot and waterfall into the same structure. The individual box’s can track the expected outcome (or perhaps the “risked” expectation). The idea is that it shows the compounding range of outcomes and how quickly the uncertainty of the expected outcome grows.

  Like

  4

• Rahul Setia Data Lover from years | Senior Consultant @ PwC | Business Intelligence and Data Analytics

  • Report contribution
  When dealing with numerical data, selecting the right chart is key. Histograms for distribution, line charts for trends, scatter plots for relationships, and box plots for a summary. Bar charts can work for continuous data, while area charts show accumulations. Heatmaps reveal patterns, and bubble charts add dimensions. Choose wisely for effective insights! 📊📈 #DataVisualization #NumericalData

  Like

  3

• Rahul Setia Data Lover from years | Senior Consultant @ PwC | Business Intelligence and Data Analytics

  • Report contribution
  Choose the right visual:- 📊 Histograms: Show distribution.- 📊 Box Plots: Compare summaries.- 📊 Scatter Plots: Identify relationships.- 📈 Line Charts: Depict trends over time.- 📊 Bar Charts: Compare across categories.- 📊 Pie Charts: Display proportions.- 📈 Area Charts: Compare within a whole.- 🎨 Heatmaps: Use colors for intensity.- 📊 Bubble Charts: Show multi-variable relationships.- 📅 Gantt Charts: Timeline visualization.- 📊 Treemaps: Hierarchical data representation.- 📊 Dual-Axis Charts: Combine different data types.- 📊 Waterfall Charts: Display incremental changes.- 📊 Pareto Charts: Show ranked cumulative effects.Match visualization to data type, goals, and audience. Clarity and accuracy are key! 🚀📊🔢

  Like

  3

• Mohammad Namakshenas Analytics Researcher/Engineer

  • Report contribution
  Gantt Heatmap: This plot combines the Gantt chart and heatmap, allowing for a compact visualization of tasks or events over time, with color intensity indicating the magnitude of the values.

  Like

  4

• • Report contribution
  When plotting temporal data, take a moment to think about the timeframe (daily, weekly, monthly…).If the data has repetitive patterns over time (for example drops downs in the weekend), it might be better to plot it as weekly for example. Otherwise the regular variations will reduce ability to spot evolution.

  Like

  3

• Olawale Olaosebikan Data Analyst | Data Science 2023 Scholarship Student at ALX_Africa | Power BI | SQL | Tableau | Python | Technical Writing | Programmer | Psychologist | CIPM in view

  • Report contribution
  In my experience, temporal data consists of date and time information, requiring graphics that emphasize trends and patterns over time. Here are some chart types to consider:A. Line Chart: A common chart for visualizing temporal data, showing trends and changes over time by connecting data points with lines.B. Area Chart: Similar to a line chart but fills the area beneath the line, making it ideal for visualizing cumulative numbers or proportions over time.C. Gantt Chart: A project management tool displaying activities and their durations across a specified timeframe. It offers a concise view of the project's timeline, presenting task start and end points, durations, and interconnections.

  Like

  2

• Rahul Setia Data Lover from years | Senior Consultant @ PwC | Business Intelligence and Data Analytics

  • Report contribution
  When it comes to representing temporal data, nothing beats the power of line charts or time series plots. 📈🕒 These charts are like a time-traveling lens, showing how data evolves over continuous time periods. Whether it's tracking stock prices, weather patterns, or website traffic, line charts effortlessly capture trends and fluctuations. The x-axis takes you through time, while the y-axis unveils the changing values. An ideal choice to unravel the chronicles of data evolution! ⏳📊 #TemporalData #DataVisualization

  Like

  2

• Matt M. Revenue Operations Leader | Revenue Engines Optimized by AI

  • Report contribution
  Temporal data is a type of data that represents instances in time. It is a crucial component in many data analytics applications and can provide unique insights when visualized properly.Absolute temporal data is anchored to exact points in time, like a specific date or hour. These are fixed timestamps that do not change. An example would be "July 20, 2023, 10:00 AM".Relative temporal data is time duration or a time difference between two events. For example, "it took 2 hours to finish the project" or "she is 30 years old". The exact points in time are not specified, and the value can change depending on the current time or the reference point.

  Like

  1

• Olawale Olaosebikan Data Analyst | Data Science 2023 Scholarship Student at ALX_Africa | Power BI | SQL | Tableau | Python | Technical Writing | Programmer | Psychologist | CIPM in view

  • Report contribution
  Geospatial data, associated with geographic locations, demands specialized charts for effective visualization. Consider the following examples:A. Choropleth maps: Use color coding to represent data values for specific regions like countries or states. It employs color mapping symbology to depict statistical information.B. Bubble Map: Plot data points on a map using bubbles of different sizes and colors. It delineates attributes tied to objects, individuals, concepts, or occurrences.C. Heatmap: A graphical representation of data where values are shown as colors in a two-dimensional matrix. Visualize data density within a geographic area.

  Like

  3

• Matt M. Revenue Operations Leader | Revenue Engines Optimized by AI

  • Report contribution
  Spatial data, also known as geospatial data, refers to information about a physical object that can be represented by numerical values in a geographic coordinate system. This data usually includes location information like coordinates (longitude and latitude), but can also include other aspects like distance, elevation, or even the description of the features located at the site.

  Like

  3

• Rahul Setia Data Lover from years | Senior Consultant @ PwC | Business Intelligence and Data Analytics

  • Report contribution
  When it comes to showcasing spatial data, maps are undoubtedly the crown jewel. 🗺️📍 Whether it's geographical data, population distribution, or any location-based insights, maps provide an intuitive visualization. They paint a vivid picture of data across regions, revealing patterns that might otherwise remain hidden. From heatmaps to choropleth maps, they're the compass guiding us through the spatial intricacies of our world. 🌍📊 #SpatialData #DataVisualization

  Like

  2

• • Report contribution
  Thanks to spatial data visualization, you can communicate insights with utmost clarity, and your stakeholders grasp the bigger picture effortlessly. 🤝🎯 So, if you're dealing with location-centric data, don't miss out on this powerful toolset – maps, diagrams, and bubble charts might just be your secret to unlocking a world of actionable insights! 🌍🗝️

  Like

  1

• Rahul Setia Data Lover from years | Senior Consultant @ PwC | Business Intelligence and Data Analytics

  • Report contribution
  Ways to showcase spatial data:- 🗺️ Maps: Classic geographic representation.- 🎨 Choropleth Maps: Colors for value variations.- 🔥 Heatmaps: Visualize density patterns.- 💬 Bubble Maps: Varying circle sizes for data.- 📊 Proportional Symbols: Size represents values.- 🌍 Cartograms: Resized areas based on attributes.- 🌐 Flow Maps: Depict movement between locations.- 🌐 Network Diagrams: Show interconnected nodes.- 📅 Time-Series Maps: Display changes over time.- 📐 3D Maps: Add depth for terrain or planning.- 📏 Raster Maps: Cells with assigned values.- 🌐 Web-based Libraries: Interactive maps on websites.Select based on data type, insights, and audience. Blend methods for a holistic view. 🌟📊🗺️

  Like

  1

• • Report contribution
  I think the impact of data visualization is determined by three interconnected factors: perspective, data set, and audience. The visual aspects are determined by the perspective, which influences how data is received and understood. A well-prepared and relevant data set assures accuracy, allowing for clear insights to emerge. However, the demands and tastes of the audience play an important part in defining the final visualization. Visualizations can be customized to present information effectively by taking into account the audience's knowledge level and interests. When all of these criteria align, data visualization becomes a powerful tool for presenting complex information, improving comprehension, and enabling better-informed decisions.

  Like

  1

• Rohit Thakur Senior Business Analyst |💡 LinkedIn Top Voice | Agile | MBA- Business Analytics | Helping aspiring Business Analyst to break comfort zones🎯

  • Report contribution
  In My opinion there is not a single chart which is best fit for all.The choice of charts may vary upon what data needs to be presented in effective way.Lets take an example of what not to do- You cannot simple use pie chart for showing more than 10 categories of car sales. In similar manner you cannot show YES and NO percentage on line chart or Bar graphs.

  Like

  7

• Ellen Cattle Data Analyst | Data Analytics, SQL, SAS, Excel, Power BI

  • Report contribution
  Frankly, there isn't an answer to "what's the best chart." Each unique situation will call for a different type of visualization. While I might especially love doing geospatial visualization with a nice heatmap, you aren't always going to need to see population density for your data. Often you'll need to see a comparison of bar graphs over time, or a pie chart to show how much out of the total population each segment occupies.A crucial part of data analysis is figuring out exactly what kind of visualization will communicate your analysis cleanly and clearly to anyone you're presenting it to. The act of choosing your visualization is figuring out "what language" your data needs to speak in order to be effective.

  Like

  4

• • Report contribution
  I would add two more things overall:- do not forget to think about colors smartly. Highlight the data of interest thanks to colors, and don’t forget color blindness when choosing colors.- a good chart isn’t enough. If you prepare a deck for example, ensure the conclusion is very clearly written (ideally in the title), and you ONLY use the supportive charts. No clutter!

  Like

  3

• • Report contribution
  In my experience, my honest opinion and friendly advice is that you should not use Pie Charts. Do not be in a haste to use a pie chart; they are prone to being misinterpreted in many situations, it is particularly a bad option when there are six or more parameters/categories for the variable being visualized. It is advisable that you make sure all the categories to be displayed in the pie chart are comparable and have a relationship with the whole. Because they are very easy to screw up and don't often accomplish the purpose for which people intend to use them. Let me relieve you a bit, they can be kind of helpful when you are only dealing with 2 or 3 different data points, which is not mostly the case.

  Like

  3