Mastering data visualization is the first step toward using data analytics and data science to your advantage to add value to your organization. Here, you will learn how to use different data visualization techniques and reap the rewards of data-driven decision-making.
Apr 04 2020 ● 4 min read
Data visualization is the process of creating visual representations of information such as simple charts, maps, graphs, plots, infographics, and dashboards. These data visualization techniques help present data in a way that is easier for the viewer to understand and make the right decisions.
You should use different visualization methods to present complex data and its interactions. But the visualization technique you choose is determined by factors like audience, context, type of data, etc. Choose the wrong tactics, and you risk leaving half the information on the ground.
So, what determines what data visualization you’ll use?
Your visual data representation needs to be suited to the specific target audience. For example, a personal finance app should use uncomplicated visualization like bar charts or pie charts that are easy to read.
On the other hand, data scientists or C-level decision-makers who regularly work with data will benefit from more complex visualizations like box-and-whiskers plots, stacked area charts, etc.
The type of data you need to present will often determine the visualization method. For example, you might use line charts to show the dynamics of time-series metrics. You can use a pie chart to show the percentage or share within a whole. Bar charts work well for comparative analysis; you can use a scatter plot to establish correlations between multiple data points.
How you will use certain types of data visualization often depends on the context. For example, you can use different shades of one color to showcase growth. To differentiate elements, you can use contrast colors, and to show positive and negative variance, you can use a diverging color scale, e.g., going from bright red to deep blue.
Another factor determining the choice of visual elements is the rate of change or data dynamics. Financial results can be measured monthly or yearly, while time series and tracking data change constantly. Depending on the rate of change, you may consider dynamic visualization, like an interactive dashboard or static visualization.
The purpose of data visualization can also determine the way of use. A complex analysis requires grouping visualizations into dynamic dashboards with multiple visual data analytics features, such as filtering, comparison, and data transformation. On the other hand, if you need to show occasional data insights, there’s no need for a dashboard.
Pie charts are simple and easy to read, making them ideal for audiences interested only in key takeaways. Understanding of data is not required. A workhorse of your data visualization arsenal, you can use a pie chart to illustrate proportions or part-to-whole comparisons.
This chart type is most effective when used with text and percentages to describe the content. Without the percentage values, pie charts can be challenging to interpret as the human eye has difficulty estimating areas, for example when two or more categories have similar arc lengths.
In such cases, a bar chart would be a better choice.
Pie charts work best when there are just two or three categories, so the viewer needs to make fewer comparisons. For example, here’s the proportion of paid ads traffic in Whatagraph’s PPC dashboard:
The sample data shows that Facebook Ads bring in the bulk of the traffic, while LinkedIn Ads barely outrun Google Ads.
While you’re here, maybe you want to check out the rest of this fantastic PPC Overview Dashboard Template we created.
A bar chart is another data visualization staple that is easy to read and interpret. Here, one chart axis shows the categories compared, and the other a measured value.
Each bar's length is proportional to each category's value, making these charts an excellent alternative to tables for showing the values of different categories.
This bar chart from Whatagraph’s Web Traffic Report Template clearly shows where the most traffic comes from.
Data analysts typically use bar charts to make comparisons. However, as with pie charts, having too many categories involved can make it difficult to label and compare them.
In that case, you may want to use:
A histogram is a graphical representation of information that uses bars of various heights to illustrate data distribution over a defined period or continuous interval. This makes histograms ideal for identifying increased activity or concentration of values and substandard values and gaps.
For example, you can use a histogram to show how many clicks your website received each day over the past week, which helps you determine on which days your visitors are the most active.
Histograms allow you to inspect data for its underlying distribution, outliers (data points that differ significantly from other observations), skewness (distortion of a symmetrical distribution), etc.
Keep in mind, however, that in histograms, the height of the bar doesn’t necessarily indicate how many occurrences there were within each bar (bin). It’s actually the product of height multiplied by the width that shows the frequency of occurrences in that bin.
This confusion often comes from the fact that many histograms have equally spaced bars, in which case, the bar's height reflects the frequency.
Histogram vs. bar chart
The most significant difference between these two chart types is that a histogram is used to show the frequency of score occurrences in a continuous data set divided into bins. Bar charts, on the other hand, are used for many different types of variables, including ordinal and nominal data sets.
Lina graphs are perfect for tracking data change over long periods or continuously changing data. As such, line charts are most commonly used to indicate trends.
Sometimes, you can have more series (lines) in a single chart, like in the graph below.
As with pie and bar charts, having too many series can make your visualization look messy. Make it easier for the viewer by formatting the chart so that the most critical series has the most visible color, like in the example from Ahrefs below:
A heat map is a type of data visualization that uses color coding the way a bar graph uses height to indicate numerical values. The color difference makes it easier for viewers to understand the situation quickly.
Heat maps have a wide use. Mapped against a web page or geospatial image, colors can tell you which areas get the most attention.
Although heat maps are an effective data visualization technique for examining a large number of values, they lack the precision of bar charts and other more accurate presentations — simply because color differences are difficult to measure.
An area chart is a variation of a line graph in which the area below the line is shaded to represent the total value of each data point. You can use stacked area charts if you need to compare several data series on the same graph.
This data visualization technique is useful for displaying changes in one or more quantities over time and showing how each quantity combines to make up the whole.
In finance, for example, one area can represent the value of company shares, while the second variable shows the industry benchmark against which you’re comparing it. By looking at it, investors can see how much they would pay for stock shares for each dollar they earn.
A scatter plot is a type of graph that displays data for two variables represented by points plotted against the horizontal and vertical axis. Scatter plots are useful for presenting relations between variables and identifying trends and correlations in data.
Identifying trends is easier when more data points are present, making scatter plots ideal for visualizing very large data sets. The closer the data points are grouped, the stronger the correlation or trends.
These properties make scatter plots an integral part of machine learning.
Machine learning scatter plot in Python’s Matplotlib feature
Still, when interpreting scatter plots, you need to be careful. Even if two variables might be strongly correlated, it needn’t mean there is a causal relationship behind them.
For example, a scatter plot of draft beer sales along the coastline can strongly correlate with the number of shark attacks. This doesn’t mean that buying draft beer causes shark attacks but rather that whatever is causing one trend is also causing the other — in this case, hot and sunny weather that makes people go for refreshments and a swim.
Also known as a box plot, this chart provides a visual summary of data through its quartiles — values that divide sorted data into four parts, each with an equal number of observations.
A box is drawn from the first quartile to the third of the data set, with the vertical line within the box representing the median. Whiskers extend from the box to the minimum and maximum value. Outliers are represented by individual points that are in line with the whiskers.
A box and whiskers plot is ideal for quickly identifying whether or not the data is symmetrical or skewed.
Treemaps are a visually engaging way of showing part-to-whole relationships in data. Here, hierarchical data is represented as a set of squares. Each square (called leaf node) is a category within a given variable, while the area of each square is proportional to the size of that category.
This makes treemaps more intuitive than other part-to-whole visualizations, like pie charts.
A huge advantage of treemaps is that they can display many categories on the screen simultaneously, making efficient use of available space.
A treemap can also include different categories, but in that case, each category needs to have a different color.
You can use word clouds or network diagrams to visually represent semi-structured or intentionally unstructured data.
A word cloud is a visual representation of textual data in which the word size is proportional to its frequency. The more often a specific word appears in a dataset, the larger its visualization. Apart from the size, words can have different boldness or color schemes depending on their frequency.
Word clouds are a good choice for identifying essential keywords and comparing differences in textual data between two sources.
Network diagrams are often used to graphically represent a network. This type of visualization is helpful for network engineers, designers, and data analysts while compiling extensive network documentation.
With data visualization techniques explained, let’s make a selection of the best visualization tools you can use to speed up the process.
Whatagraph is an all-in-one platform to connect, visualize, and share marketing data. This means you can complete the whole reporting process within one app without needing third-party data connectors or visualization tools.
Pull data directly from over 45 marketing platforms and visualize it on detailed dashboards and reports using a variety of visualization types.
Still, you can connect any data source using a Custom API, Google Sheets, or BigQuery data warehouse.
This makes Whatagraph an ideal choice for both marketing agencies that handle multiple clients, as well as large enterprises that need fresh and accurate multi-source updates to make data-driven decisions.
Use our intuitive drag-and-drop report builder to create a wide range of marketing data visualization, or pick a ready-made report or dashboard template from our library.
Once you visualize your data in Whatagraph, you can share it in a few clicks.
Create an email template, set the recipients, frequency, and delivery time, and automate the sendouts. Alternatively, you can share a live link to a report or dashboard so the recipients can check data as it updates.
Microsoft Power BI is a data visualization and business intelligence tool that allows you to create interactive dashboards and reports. Power BI is ideal for users who need to perform deep analytics, combine data from multiple sources, and predict outcomes by identifying real-time trends.
Apart from having exceptional Excel integration, Power BI enables data mining from various databases such as CSV, XML, JSON, SQL Servers, and cloud-based sources like Microsoft Azure data warehouse and Salesforce CRM.
With Power BI you can easily share your insights with others, making collaborating on data analysis projects easier.
Still, when it comes to visualization customization options, Power BI still needs work. It’s more of an all-around data visualization tool for in-house teams that need to visualize complex data for stakeholders. Marketing agencies, on the other hand, could benefit more from a tool like Whatagraph that allows you to change the color scheme and branding to make each report unique for individual clients.
Tableau is a cloud-based data visualization platform that allows you to connect to any data source and create interactive, shareable dashboards.
It helps simplify big data into easily digestible visualizations so that technical and non-technical users can understand it.
Tableau is more of a universal data visualization tool than Whatagraph and has powerful business intelligence capabilities. This is why professionals and researchers in various industries use it to answer important and complex data questions.
Data visualization techniques allow users to make large volumes of data more accessible and understandable to audiences that may not understand how data works. With competitors scrabbling for data insights themselves, a quick and reliable way to analyze collected information can give you a huge competitive advantage.
Whether you stick to simple visualization methods or combine them into infographics or interactive dashboards, you can’t ignore the power of visualization.
But you shouldn’t spend a lot of time visualizing your data.
You should be able to add your sources, choose the visualization widgets, and start reading the trends.
That’s just the workflow that Whatagraph provides.
Why don’t you try it and see what your marketing data looks like in our dashboards?
Request a free trial today and visualize your data more efficiently than ever before.
Published on Apr 04 2020
WRITTEN BY
Gintaras BaltusevičiusGintaras is an experienced marketing professional who is always eager to explore the most up-to-date issues in data marketing. Having worked as an SEO manager at several companies, he's a valuable addition to the Whatagraph writers' pool.
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