In these cases, the most effective visualizations include: 1 Pie charts 2 Waterfall charts 3 Stacked charts 4 Map-based graphs (if your information is geographical)
ChartExpo is a Google Sheets and Excel add-on that lightens your data visualization task. It comes with in-built charts enabling you to create a line chart with only a few clicks. As mentioned earlier, line charts are best suited for showing trends since changes against time take a linear approach.
We have identified a line chart as the best chart to show trends over time from our discussion. Nonetheless, you must narrow down on a specific one since there are numerous options at your disposal. Likewise, dozens of tools exist to assist with deriving line graphs from data sets.
Data visualization guru Edward Tufte famously declared that “pie charts are bad, and the only thing worse than one pie chart is lots of them.” We already talked about the pros of pie charts and why we don’t adhere to this strict no-pie-chart philosophy. We should also state that there are plenty of instances where you should not use a pie chart.
Line charts are the best visual presentation for emphasizing change over time. Consider two variables, one on the vertical axis and the second on the horizontal axis.
An area chart is essentially a line chart — good for trends and some comparisons. Area charts will fill up the area below the line, so the best use for this type of chart is for presenting accumulative value changes over time, like item stock, number of employees, or a savings account.
If you want to compare values, use a pie chart — for relative comparison — or bar charts — for precise comparison. If you want to compare volumes, use an area chart or a bubble chart. If you want to show trends and patterns in your data, use a line chart, bar chart, or scatter plot.
Pie charts are best for showing trends over time.
Line GraphLine Graph A line graph reveals trends or progress over time and you can use it to show many different categories of data. You should use it when you chart a continuous data set.
Area chart can represent any quantitative (measure) data over different period of time. It is basically a line graph where the area between line and axis is generally filled with color. The procedure to create area chart is given below.
Bar Chart. Bar charts are frequently used and we're taught how to read them starting at a young age. The most simple bar charts, those that illustrate one string and one numeric variable are easy for us to visually read because they use alignment and length. Additionally, bar charts are good for showing exact values.
Which is the best way to display this data for analysis? Place the data in a chart that includes individual measurements before averages were calculated so the information is more complete.
Radar Charts are used to compare two or more items or groups on various features or characteristics. Example: Compare two anti-depressant drugs on features such as: efficacy for severe depression, prevalence of specific side effects, interaction with alcohol, continuation of relief over time, cost to the consumer etc.
In short, a pie chart can only be used if the sum of the individual parts add up to a meaningful whole, and is built for visualizing how each part contributes to that whole. Meanwhile, a bar chart can be used for a broader range of data types, not just for breaking down a whole into components.
A pie chart is best used when trying to work out the composition of something. If you have categorical data then using a pie chart would work really well as each slice can represent a different category. A good example of a pie chart can be seen below.
The bar/column chart excels at showing discrete data while comparing one data-point vs. another, while the pie chart is the classic way to show how various parts makes up a whole. Both make it easy to for readers compare values relative to each other.
Visual elements can not only make your content more engaging, but also less likely to get lost in the shuffle. Search engines use complex algorithms to identify signals that indicate web page quality and relevance to specific keywords. Content that satisfies user search intent is one of the most significant ranking factors, ...
Interactive content is engaging because it puts the reader’s mind to work and gets them to think more actively about your message. By giving your site visitors something to do other than read or watch a video, they’ll stay around longer and absorb more of your branded information. In fact, interactive content generates conversions 70% of the time, compared with 36% for passive content.
Intangible digital products can be a challenge to market because it’s often difficult for readers to visualize what they look like from a text description alone. Business software is often complex, containing multiple modules that not every customer will use, which adds a layer of opaqueness.
Infographics. Business decision-makers and consumers make purchases based on data, but not everyone has the time to read through lengthy reports and research. Infographics conveniently package important information in a manner that is easy to digest and share.
6. Data visualizations. Storytelling and data are a match made in marketer heaven, because they appeal to two distinct parts of the brain. Stories engage the part that wants to get lost in a compelling narrative while data satisfies the needs of the logical part of the brain.
Video content is quickly becoming a preference for busy professionals and retail consumers alike. Cisco predicts that video will account for 82% of all internet traffic by 2022. Videos can convey useful information in a matter of minutes, making them perfect for customers who don’t have the time to read long articles.
Nevertheless, slideshows can still be effective at conveying information to an interested audience. You probably won’t use a SlideShare presentation at the top of your marketing funnel, but it may have a place further down. Remember to keep slide text minimal – only include essential information.
If your main goal is to show a direct comparison between two or more sets of information, the best choice would be: Bubble charts. Spider charts. Bar charts.
If your primary aim is to showcase the composition of your data – in other words, show how individual segments of data make up the whole of something – choosing the right types of data visualizations is crucial in preventing your message from becoming lost or diluted.
Modern dashboard software makes it simpler than ever to merge and visualize data in a way that’s as inspiring as it is accessible. But while doing so is easy, a great dashboard still requires a certain amount of strategic planning and design thinking.
Graphs, on the other hand, are perceived by our visual system. They give numbers shape and form and tell a data story. They can present an immense amount of data quickly and in an easy-to-consume fashion. If data visualization is needed to identify patterns and relationships, a table is not the best choice.
Scatter plot is not only fun to say – it’s what you need when looking for the correlation in a large data set. The data sets need to be in pairs with a dependent variable and an independent variable. The dependent (the one the other relies on) becomes the y-axis, and the independent – the x-axis.
Using a double y-axis, one for the bar graph and one for the line, allows you to show two elements of your story in one graph.
At its core, online data visualization is about taking data and transforming it into actionable insight by using it to tell a story. Data-driven storytelling is a powerful force as it takes stats and metrics and puts them into context through a narrative that everyone inside or outside of the organization can understand.
A bar graph, basically a horizontal column chart, should be used to avoid clutter when one data label is long or if you have more than 10 items to compare. This type of visualization can also be used to display negative numbers.
Use the y-axis on the left side for the primary variable because brains are naturally inclined to look left first. Use different graphing styles to illustrate the two data sets, as illustrated above. Choose contrasting colors for the two data sets. 5.
It's used with three data sets, one of which is based on a continuous set of data and another which is better suited to being grouped by category. This should be used to visualize a correlation or the lack thereof between these three data set s.
A heat map shows the relationship between two items and provides rating information, such as high to low or poor to excellent. The rating information is displayed using varying colors or saturation.
An area chart is basically a line chart, but the space between the x-axis and the line is filled with a color or pattern. It is useful for showing part-to-whole relations, such as showing individual sales reps' contribution to total sales for a year. It helps you analyze both overall and individual trend information.
A column chart is used to show a comparison among different items, or it can show a comparison of items over time . You could use this format to see the revenue per landing page or customers by close date.
Relationship charts are suited to showing how one variable relates to one or numerous different variables. You could use this to show how something positively effects, has no effect, or negatively effects another variable.
The visualization you use to explore and display that data changes depending on what you’re after and data types. Maybe you’re looking for increases and decreases, or maybe seasonal patterns. This is a guide to help you figure out what type of visualization to use to see that stuff. Let’s start with the basics: the line graph.
Use it when you have a lot of a points or just a few. Place multiple time series on one graph or place one. Mark the data points with squares, circles, or none at all . Basically, if you’re not sure what to use, the line graph will usually do the trick. Scatterplots work well if you have a lot of data points.
For example, if your measurements aren’t equally spaced, a line graph probably wouldn’t work. Bar charts work best for time series when you’re dealing with distinct points in time (as opposed to more continuous data). They tend to work better when you have data points that are evenly spaced in time.