Today, organizations utilize a substantial amount of data that is often difficult for their teams to read and manage efficiently, so it's important that they have powerful resources at their disposal in order to generate the precise patterns required for informed decision making. This is where big data visualization helps firms and corporations shift from the outdated trend of traditional spreadsheets and reports to graphical representation for data analysis.
Data visualizations play an important role in the following aspects:
It's important to present your data in a meaningful way, and there are a handful of factors that you should keep in mind when determining how to present your data.
First and foremost, it's important to consider your audience and the depth of data that's important to them. If your audience is made up of general fitness app users interested in the percentage of fellow users taking yoga classes in comparison to HIIT, cardio, and kickboxing classes, a simple pie chart may suffice. If you're putting together a lot of really complex data to present to a team of scientific researchers, however, a higher level of visualization may better suit your needs.
The content of your data is especially important because it determines the kind of tactics you should employ when presenting your data. For example, if you're looking to indicate the relationship between two different things, a scatter plot may help you do that. Similarly, line charts help you display dynamics in relation to a timeline, and bar charts are helpful in exhibiting a direct comparison of two or more datasets.
The whole point of creating visualizations for your data is to make it easy to read and understand what you're presenting in a single glance. Adding some context to your visualizations can make that process even more efficient because it helps viewers of your visualization better understand what they’re looking at. It can be as simple as putting "Record High" in red next to the highest point on a line for a line graph or adding a key beneath your chart.
When you’re representing various types of data, it’s important to recognize that each type is going to have a different rate of change. Financial results, for example, are typically measured on a weekly, monthly, or yearly basis, while time series and tracking data are changing constantly. Depending on the rate of change of your data, it may be best to consider more dynamic representation or static visualization techniques in data mining.
The goal you’re looking to achieve with your data visualization also affects the way it should be implemented. In order to make a more complex analysis, visualizations should be compiled into controlled, dynamic dashboards that serve as visual data analysis techniques and tools. With that being said, dashboards are not absolutely necessary if you need to highlight a single or occasional data insight.
Every business, no matter how large or how small, can boost its decision-making efficiency by implementing an appropriate data processing approach, and the following strengths of big data visualizations can help improve decision making.
If you're looking to manage and present data that provides a clear understanding of all of the processes and outcomes of your business, it may help to have a few techniques up your sleeve. Here are some strategies to help you ensure that all parties can make the right decisions at the right time for the sake of your business' growth.
Humans are visual by nature, so it's important to find opportunities to break up or even replace some of the text in your reports with charts and maps in order to keep your audience engaged and present your data in a more visually friendly way. However, you also want to make sure that you're choosing the best method for displaying your data. For example, line charts are ideal for mapping continuous datasets over a set period of time, but they wouldn't really be suitable for representing a percentage breakdown of your monthly marketing spend. For that, you'd want a pie chart.
The colors that you use in your charts can truly make or break your data visualization. A carefully selected color palette can help you tap into any pre-attentive processing powers of the human brain in order to make your data more clear and easier to understand. A poorly chosen color palette, on the other hand, can obscure important details you'd like to share and can potentially make your data visualization less effective and harder to use.
Data at different levels should be placed in a hierarchy and labeled by implementing a suitable visualization system in order to illustrate it effectively. When building your data visualizations, consider the way that you'll be presenting the data and make sure that it follows the flow of your presentation. Consider what information you want to prioritize and place it in a prominent spot that will draw the eye. And finally, ask yourself whether anything can be removed—whenever possible, it's best to be concise.
Scientists and data analysts can use network diagrams to graphically show an unstructured network point under consideration. Network diagrams visualize how things are interconnected through the use of nodes, or vertices, and link lines to represent their connections. Ultimately, they are used to help illustrate the type of relationships that exist between a group of entities. A word cloud, on the other hand, is a visualization that focuses on keywords and displays the most used words in any given text ranging from small to large, according to how frequently each appears. They're useful in providing you with a glance into the most important keywords used in news articles, social media posts, and customer reviews, among other text.
Automated data visualization tools are helpful because they provide you with an easier method for creating visual representations of large data sets. If you're working with data sets that hundreds, or even thousands or millions, of data points, automating this process can make your job significantly easier.
These tools can be used for a variety of different purposes—everything from managing dashboards and creating annual reports, to producing sales and marketing materials or developing investor slide decks. We'll discuss some popular automated data visualization tools later in this article!
There is a myriad of different ways you could visualize your data, but the types most commonly used are charts, plots, maps, diagrams, and matrices.
Charts can be read more easily and quickly than raw data, so they're often used to speed up the absorption of large quantities of data and the relationships that exist between those parts of the data.
1) Line Chart
Line charts are used to reveal trends, progress, or changes that may occur between different variables as time progresses. With this in mind, it generally works best when your data set is continuous rather than full of starts and stops.
When to Use: Line charts are best suited for trend-based visualizations of data over a period of time.
2) Bar Chart
A bar chart is made up of rectangular bars with lengths proportional to the values that they represent and can be plotted either vertically or horizontally depending on how you'd like to lay out your data.
When to Use: Bar charts are useful if you're looking to compare and contrast multiple variables, display a set with negative numbers, or when two metrics from varying categories need to be compared (as shown in the figure below).
3) Pie or Donut Chart
Pie charts are typically used to represent percentages or to visualize the individual parts that make up a composition. They're not meant to compare individual sections to each other or to represent exact values (you should use a bar chart for that).
When to Use: Use a pie chart when you'd like to represent numerical amounts in percentages.
Plot charts are excellent at highlighting links among multiple datasets and their parameters using plot points, and they can be either 2D or 3D.
1) Bubble Plot
Different colored bubbles are used as markers to show relationships, and the bubbles can be animated to portray change over a span of time.
When to Use: For tracking datasets that contain hundreds of varying values.
2) Scatter Plot
A scatter plot chart is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data, and they are primarily used for correlation and distribution analysis.
When to Use: They're ideal when you have a lot of data points and would like to exhibit a correlation between your X and Y variables. Scatter charts can also show the data distribution or clustering trends and help you spot anomalies or outliers.
3) Histogram Plot
A histogram plot chart is a common variation of a column chart used to present the distribution and relationship of a single variable over a set of categories.
When to Use: You should use a histogram plot chart when you'd like to see the shape of your data’s distribution, especially when determining whether the output of a process is distributed normally.
Maps are popular in data visualization and are used by many organizations to show data points on large layouts and geographical places.
1) Dot Distribution Map
Dot distribution or dot-density maps are a simple, yet highly effective method of showing density differences in geographic distributions across a landscape.
When to Use: Use dot distribution maps to show business growth over various geographical locations.
2) Heat Map
A heat map uses color the way a bar graph uses height and width as a data visualization tool. If you're building an app or a web page and would like to know which areas get the most user attention, a heat map can show you and help you make more informed UX/UI decisions.
When to Use: Heat maps are particularly useful to conduct website analysis such as page usability evaluation and performing A/B testing.
Diagrams and matrices include more than one data type to show complex links and structures in hierarchical or dimensional form.
1) Correlation Matrix
A correlation matrix is a table showing correlation coefficients between variables. Each cell in the table shows the correlation between two variables, and darker and lighter colors represent strong and weak correlations among points, respectively.
When to Use: Use a correlation matrix when you want to observe correlation among variables and track data coming from different stages.
2) Decision Tree
A decision tree is a decision-making support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
When to Use: Use a decision tree when anticipated outcomes need to be determined about a function of a large data set influenced by various factors.
Tableau helps people to better see and understand data with their visual analytics platform. Their interface is easy to use, they have libraries full of interactive options for numerous types of visualizations, and they offer plenty of useful third-party integrations.
Power BI is a business analytics service offered by Microsoft with the aim to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their own reports and dashboards.
QuickSight is a cloud-based business intelligence service offered by Amazon. Using it, users are able to create eye-catching dashboards with machine learning insights. It is also the first BI service to offer pay-per-session pricing, where you only pay when your users access their dashboards or reports, making it cost-effective for large-scale deployments.
Google Charts is a free tool that helps users create charts for dynamic data, and it allows users to work in browser mode which eliminates the need for extra plugins. It includes google spreadsheets, fusion tables, and an SQL database with a vast variety of charts that can be fully customized.
Fusion Charts is a data visualization tool that works on a JavaScript framework and offers many server side programming languages. It is powerful and contains 150 different types of charts and thousands of maps that you can create a visualization from.
Although conducting data analysis for complex datasets might sound challenging at first, it's relatively easy to jump into once you have a handle on the best methods to use when presenting your data. It can also save your team valuable time—recent research has suggested that business meetings could be shortened by up to 24% using data visualization analytics.
If you're looking to build a data visualization application for internal use or a consumer-facing app with automatically generated charts, Crowdbotics offers managed app development to deliver your build on time and within your budget. Get in touch with one of our experts today for a detailed quote and timeline.
Nimra Ejaz
Originally published:
March 5, 2021
