What is Visual Analytics?
Falling under the category of visual business intelligence (BI) and business analytics (BA), Visual Analytics is basically just a visualization of the data analysis. It involves data displayed in a visual chart or dashboard that makes it easier to understand that if it were presented in endless rows and columns of a table. This makes it easy to decipher trends at a glance, without taking up too much of your time. Additionally, visual data analysis tools make data analysis easier to understand for those who aren’t data scientists.
The the approach uses data visualization technologies to help data scientists and other professionals identify trends, patterns, and relationships in the data they are working with. Packaged visual analytics software tools make it easier for non-technical users to use by including drag-and-drop options for setting and modifying analytical parameters.
Visual analytics is especially helpful in applications involving large, complex data sets and analytical processes that require a high degree of monitoring and interaction -- for example, big data analytics and data mining uses.
Common Features of Visual Analytics Tools
Data Visualization
This should go without saying, as it’s the essence of what makes visual analytics tools… well… visual analytics tools. Data visualization simply refers to the ability to take data sets and display that information in graphics, charts, figures, and bars. It is used as a way to deliver visual reporting to users for the performance, operations or general statistics of an application, network, hardware or virtually any IT asset. This data is generally in the form of numbers, statistics, and overall activity. This could be in a bar chart, pie chart, histogram, scatter plot, cluster graph or any other type of chart or graph.
Displaying complex data in easy-to-understand graphics help our brains process the information in a much more direct way than analyzing numbers. Some methods for achieving this include turning the data into a visual narrative via storyboarding or the animation of static data. Most software offer assisted guidance through the data discovery process (auto charting) and prefabricated templates for visualizations (plugins). Some even incorporate data presented in three dimensions (geospatial interaction). Your data visualization needs will vary based on the type of data you’re analyzing.
Data visualizations can be used to present any type of data, from monthly revenue to lead generation to warehouse inventory levels. This means they’re useful for gaining insights into any process you want to improve. No matter if you’re trying to discover why your revenue dipped or you just need to compare marketing strategies, data visualization helps simplify your analysis.
Dashboards
Dashboards are where the majority of data visualization takes place. They create charts and graphs for specific data, constantly updating with the latest real-time information to keep you informed. Dashboards are great for at-a-glance or ad-hoc analysis when you just need to quickly check your data. For example, you can compare the number of leads generated from email sign-ups by email type to gain insights into marketing effort effectiveness.
The most useful dashboards are real-time dashboards, which update their visualizations minute-by-minute. By implementing real-time dashboards, you can monitor the status of whatever data you’re tracking as it happens. This allows you to notice issues as they arise. The ability to discover issues in real time allows you to address them quickly.
Especially in a fast-paced supply chain, fixing issues quickly is a must. It reduces the number of setbacks you could face, such as warehouses with too much inventory or shipments that arrive late. There are many other benefits to utilizing real-time dashboards, like monitoring KPIs, driving accountability, as well as saving employees time and money.
Integration with Multiple Data Sources
In order to visualize data about various processes, you have to gather them from wherever you collect and store them. This means your visual analysis tool needs to be able to communicate with all of the different data sources you have. This allows data visualization tools to pull that data, analyze it and visualize it.
There’s no shortage of data sources that businesses commonly use. The most common source is some kind of database — such as NoSQL, Hadoop or a cloud service — that can hold millions of data points. Not every small business uses databases, however, so integrating visual data analytics tools with other software systems is also common. This could include pulling supplier, distribution and inventory data from SCM software; customer and lead information from a CRM; and employee data from an HR system.
System integration can also refer to the IT or engineering process concerned with joining different subsystems or components as one large system. It ensures each integrated subsystem functions as required. It is the ability of software to cooperate and communicate with other platforms, applications, and software. This can be anything from CMMS systems to CRM software to a business’ website to an email platform. Integrating systems consolidates data and makes it easier for sales and marketing teams to coordinate efforts. Not to mention it makes everyone’s life easier to have a single dashboard for information.
Collaboration
As we all learned in kindergarten, sharing is caring. Why silo your data visualizations and other analysis processes when you can provide easy access to other users within your organization?
Collaboration features allow multiple users to view and work with the data you’re analyzing. This functionality allows you to decide what type of analysis to perform as a group. Additional benefits include the ability to share your analysis with other business users, add comments on different visualizations and hold discussions in the visual analytics platform.