HomeBlogBusiness intelligence and VisualizationDashboard Data Visualization: Benefits, Types and Examples
The ability to track multiple key performance indicators (KPIs) and metrics is critical to business success today. It is not feasible to analyze and interpret large amounts of data manually. Data visualization has become vital to understanding the various trends present in the data – both visible, as well as hidden trends. Dashboards showcase visual trends and information such as KPIs (Key performance metrics), trends, filters, and forecasts. Data visualization and dashboard design are both an art and a science and creating them is not as easy as it might seem. How do top UX designers and visual designers illustrate complex information without confusing users? Want to learn more about data visualization, check out Top Business Intelligence and Visualization Courses
Dashboards are data visualization tools that monitor, analyze, and display key performance indicators (KPIs), metrics, and key data points. Dashboards enable technical and non-technical users to understand and apply business intelligence to make better decisions. Users actively participate in the analysis process by compiling data and visualizing trends and occurrences and viewers. It is also used to convey messages and understand patterns easily and often. It also helps you better understand the relationships between your data. Displaying data is important because viewing data in a one-million-row Excel spreadsheet, it’s hard to read or even understand. Whereas, when viewed via a graphical representation, it will help you make faster decisions and reach your goals. The ability to see an overview of all data points for a quick understanding of distributions and relationships. These can provide additional context to help confirm the conclusions you want to draw from the statistical summary.
In the early stages of the data analysis process, exploratory visualizations don't need to be pretty. More importantly, analysts can quickly gain a personal understanding of the data. Exploratory data analysis requires looking at the data from multiple angles and experimenting with different relationships and representation types to get the complete picture possible.
So, to summarize a data visualization dashboard is a tool that tracks, analyses, and displays KPIs, metrics, and critical data points which can be further used to take data-driven decision
1. Make data easier to understand
You don't have to be a data scientist to use and understand the dashboard. The average user can quickly scan data visualization dashboards to get a high-level overview of key data points without having to painstakingly sort through spreadsheets, emails, or documents to find answers to critical business questions.
2. Visualize multiple KPIs at once
Most organizations use a variety of services to track KPIs and metrics, including marketing automation platforms, email marketing platforms, CRM tools, and more. Tracking and analysing data from each of these tools individually wastes valuable time and resources.
The purpose of using dashboards for data visualization is for users to get a bird's-eye view of the data from each of these platforms in one centralized location, with the ability to quickly understand what it means for the business. The user can then drill down into any aspect of the data and compare it to established KPIs, helping us understand what is working and where there is room for improvement.
3. Create reports on the fly
In today's fast-paced global business environment, it is important to eliminate the old habit of generating reports at the end of the month, quarter or year. By leveraging dashboards that update in real-time, your organization can make quick changes before they have a chance to cause significant damage to the business.
Management does not have to wait for management to give them messages; everyone in the organization can easily access the dashboard and use its insights to make intelligent decisions.
Dashboards make it easy for teams to collaborate, whether they're all working in the office, virtually, or in the field. The cloud-based dashboard tools update in real-time and are accessible from any browser. They help keep everyone on the same page and working toward the same goals.
Users can also create a link to their dashboards that can be shared with stakeholders inside and outside the organization.
I had a chance to develop many dashboards for my business partners. And in almost all of my roles as a data analyst. There are many tools to accomplish dashboard goals, for example, Tableau and HTML/JavaScript, Power BI, Looker, Excel, and so on. Based on my experience, dashboard development is not technologically challenging. The most challenging part is the content creation.
What story can you tell with the dashboard to make users revisit it frequently?
You need to partner with your colleagues and leadership to find out exactly how the dashboard can be useful. To some extent, I am saying that dashboard development is like product development. Great collaboration among roles such as product managers, product developers, and leadership can make a dashboard truly successful. There are many types of visualizations we can use in one dashboard. It depends on the analyst how he wants to tell the story about the data.
We will look at the most common visualizations one by one:
The bar chart or bar graph is one of the most common data visualizations on this list. They’re sometimes also referred to as column charts. Bar charts are used to compare data along two axes. One of the axes is numerical, while the other shows the measured categories or themes.
Imagine you've just polled your friends to find out what kind of movie they liked the most:
| Table: Favourite Type of Movie | ||||
|---|---|---|---|---|
| Comedy | Action | Romance | Drama | SciFi |
| 4 | 4 | 6 | 1 | 4 |
We can show that on a bar graph like this:
An A-line basket or line chart is a type of data visualization that shows changing data over time. Like a bar graph, a line graph has an x-axis and a y-axis. The difference is that both axes contain numerical values representing data. To create a line chart, enter the relevant time frame on the x-axis and quantitative measurements on the y-axis. Plot the data by combining the time value and the numerical value. After drawing all the dots, connect them with a line. A line graph can have one or several lines. In the case of a graph with several rows, each row represents a category. Each category has a color, and a description is detailed in the legend.
A scatter plot is a data visualization type used to analyze the correlation between variables. The data is plotted on the chart as dots at the intersection of its two values. To understand it better let us see an example: The local ice cream shop keeps track of how much ice cream they sell versus the noon temperature on that day. Here are their figures for the last 12 days:
One of the most common data visualizations is the pie chart. The data in a pie chart represent parts of a whole. The entirety of the circle is the whole, and each section is relevant. The best type of data for a pie chart has no more than five or six parts. Any more than this makes the wedges too thin at the centre. If more than three values are like each other, it will be difficult to tell the difference. The best pie charts use contrasting colors that fit well together, making each section visually different from the one next to it.
A histogram is like a bar graph but has a different plotting system. Histograms are the best data visualization type to analyze ranges of data according to a specific frequency. They’re like a simple bar graph but specifically to visualize frequency data over a specific time period. For example, Mr. A want to make an investment in the stock market. He has shortlisted the below stocks and wants to know the frequency of the prices.
Solution:
We have created a histogram using 5 bins with 5 different frequencies, as seen in the chart below. In Y-axis, it’s the number of stocks falling in that category. In X-axis, we have a range of stock prices. For example, the 1st bin range is 100 to 300. And we can note that the count is 7 for that category from the table and as seen in the below graph.
There are many more types of visualisations which we can create based on our requirements. Want to learn more about them check out: Knowledgehut Top Business Intelligence and Visualization Courses
We create the dashboard to answer certain questions using data or We can a dashboard to track growth over a period. To complete these goals, we use KPIs. it stands for Key Performance Indicators. They are important because they allow you to tell your progress toward specific goals.
KPIs may vary from role to role and organization to organization. Deciding KPIs is art. We need to carefully decide based on what outcome we need at the end of it. Let’s say you are a financial organization your KPIs will differ from Product based organization.
Below are the most common dashboard data visualization KPIs.
KPIs represent how you are doing in relation to strategic goals. And by goals, we mean specific business results, such as targeted quarterly revenue or targeted new customers per month. Metrics support KPIs by representing the tactical processes or actions necessary to achieve the KPIs. Metrics track and measure success against goals for specific actions such as monthly brochure downloads or store visits, revenue generated, number of visitors, etc.
As we understand what dashboard data visualization is and why we create it so let’s look at them.
Lets look at some of the data vizualisation tools which are currently being used in market to create the data vizualisation.
It is an interactive data visualisation tool created by Microsoft. It is a cloud-based, business analytics service for analyzing and visualizing data. Power BI gives you a platform to be productive and artistic with reports and analytics. Churning out useful information from the information and creating a visible report.
Key features of Power BI
It is one of the most widely used data visualization tools. And it is mainly recognized for its data-blending features. Combining data sources to create a report is a tedious task but tableau makes it super easy. Many organizations use this tool to analyze data and create complex reports.
Key Features of Tableau :
It is modern and enterprise ready data visualization tool. It is very fast and intuitive with wide variety of options already loaded. And you can use it on web interface without installing any app.
Key Features of Superset:
it's a package that allows you to build interactive web applications using both the statistical power of R and the interactivity of the modern web. An excellent and efficient alternative to spreadsheets and printed visualizations, R Shiny saves space and time in the construction, automation and distribution of data visualizations and statistical analyses.
Key Features of R-Shiny :
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To conclude, a data visualization dashboard helps people see, work with, and better understand data. You can create data visualization with excel dashboards and reports, paid tools like Adobe Analytics, Google looker, and Tableau or you can use open-source dashboard tools for visualizing data. Whether simple or complex, the right visualization can get everyone on the same page, regardless of their level of expertise. It's hard to imagine a professional industry that doesn't benefit from more understandable data.
Data visualization is a pictorial representation of data. There are many types of visualization. Like Bar chart. Histogram, pie chart, scatterplot etc. If we represent more than one visualization as per the context it becomes dashboard.
There are ample amount of data visualization tools available in the market. Some of them are paid and some of them are free. Defining best tool depends on usage and purpose of users
Bar graphs, histograms, scatterplots, and Pie charts are the most popular form of data visualization.
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