What is Visual Analytics?

Visual Analytics is defined as a collection of data and analytics that serves as a comprehensive suite of metrics and processes, that helps to guide your decisions, understand your user behaviors, and build and manage your products. As a whole, Visual Analytics is an area of business analysis that makes for useful business intelligence and decision-making. As a whole, Visual Analytics is also one of the most important tools for creating and managing enterprise-wide dashboards.

As a whole, Visual Analytics can provide insight into your customers, analyze, and manage their data, and help you to optimize your business. The visual analytics ecosystem is built on the foundation of the enterprise’s information systems, and is the foundation for a large enterprise-wide collection of analytics.

Visual Analytics enables businesses to better understand how they are performing on the enterprise, from marketing, to marketing decisions, and more. You can use Visual Analytics to help you build, manage, and analyze your product and provide insights. You can also understand how your business performs on the consumer side of your business, from marketing, to marketing decision -making, and more.

Why is Visual Analytics Important?

Think about whenever you’ve looked at rows and rows of data in a report and seen what can be described as a “sea of numbers.” Some numbers are large and some are small… but when they’re just sitting there as numbers on a page next to one another in sequence in black and white, it becomes very hard to see the differences and variances between their relative values.

Take for instance how big the differences might of one number to another… the downside is that reading reports this way, takes a LOT of time.

Visual analytics is essentially a collection of measurement techniques that use a combination of analytical reasoning and visualized information. It is founded on the principle that visualization itself can be used as an instrument to more rapidly analyze information than to simply view that information as just numbers or letters.

What makes visual analytics so powerful is the fact that it organizes, simplifies, and “pronounces” information to make it much easier for us to understand and consume data to more quickly understand it when it would otherwise be extremely difficult to extrapolate insights from.

Types and Use Cases of Visual Analytics

There are many different types and use cases of visual analytics that include Business Decision Mapping, Information Design, Graphic Communication, Data Visualization, Infographics, Morphological Analysis, Mental Models, Knowledge Visualization, and even Personalizing the Experience of the Customer.

Typically, enhancing end-user experiences i.e. UI/UX tops the list of use cases. Efficient, data-driven interactions with end-users has existed since the 1990s. However, developments in areas such as predictive analytics are helping to make a reality out of the vision of one-to-one end-user relationships.

Decision Making in Real-Time

In the digital world that exists today, the capacity to make instant decisions and take immediate action has become the norm. To achieve this, you will have to bring together all your stakeholders with the information in your processes, and systems.

Once you begin to use things like colors and SIZES to represent numbers (i.e. visual analytics), you’ve now entered into the benefits of the world of visual analytics and why it becomes so valuable at organizing the data being visualized for mining, statistical work, and other forms of analytics and reporting.

In essence, visual analytics makes it easier for data to be analyzed, digested and transformed into for visual representation. This, in turn, makes it easier for key players in any organization to both access, interpret, and share data to take necessary action to improve operations quickly. So let’s dive into our 5-step guide to Visual Analytics

1) Identify the Objective of the Visualization

To create effective visualization, the question to be answered must be very clear. In other words… be sure that you’re comparing two things that should actually be compared. Having a clear question or inquiry that you’re looking to address assists in preventing the age-old problem of comparing “apples” vs. “oranges,” which is a common issue in data visualization.

Successful businesses constantly seek innovative methods that assist them in drawing attention to key messages. This enables them to make informed decisions, especially under challenging conditions. Asking the right questions allows us to use visual analytics techniques to provide decision-makers with valuable insights for complex business environments.

2) Become Acquainted With the Data and Use Basic Visualizations to Begin

This step involves building a basic diagram, which could be a flow chart, line chart, bar chart, surface plot, scatterplot, networks, map or other forms of visualizations. What is ultimately used will depend on the available data.

Be sure to select the right visualizations for the information at hand. Using these visualizations makes the insights simpler and easier to understand and assists end-users in communicating vital recommendations and messages. These messages would otherwise be hard to understand, particularly for decision-makers who do not have in-depth technical knowledge and expertise.

3) Pinpoint Visualization Messages and Produce the Most Informative Indicators

Knowing the datasets well and understanding what every variable represents is the most essential step in visualization. Visual presentations of information are not a new phenomenon and over the years, a growing progression of techniques has been developing within the analytics industry.

In the early days, the use of simple hand-drawn tables and charts was the order of the day. This eventually evolved into spreadsheets, which then gave rise to graphs like line graphs, pie charts and bar graphs.

Data visualization techniques such as animated sequence assemblies, interactive bar graphs, network graphs, and 3-D scatter plots then took use in visual analytics. These operated on code that could be accumulated into a variety of programming software platforms, which is clearly the norm today.

We can now utilize visualizations across all industries as it assists in capturing the variances of our data within our environments and eco-systems in real-time. This data allows analysts and data scientists to reach insights and decision-making capabilities that best address the needs of their goals quickly and effectively.

4) Selecting the Correct Types of Multi-Axis Charts

In this step, things like “relative activities,” indexes, or even simply wanting to view multiple data-sets within the same charts and views may often be helpful to end-users and can be strategized in multiple different types of visualizations or plots to compare multiple variables simultaneously.

Using special features in charts like a ‘secondary-axis’ allows us to view two different variables in the same chart. For example, let’s say you want to view something as simple as Retail Sales by Year and Profit Margin % by Year in the same chart. Use a secondary axis! (See the image below).

visual-analytics-min-secondary-axis

This is a perfect example of a challenge that can be addressed with visual analytics.

5) Use Particular Features to Direct Attention to Vital Information

Using the eyes to pick-up key features within reporting can be challenging. As such, it is important to use features like arrows, size, shapes, color, labels, and scale to ensure decision-makers can easily identify important messages.

Notice the use of the red-rectangle to highlight the secondary axis of the profit margin of the chart above. Doesn’t it make it so much easier to see? Also, notice the use of the red-arrow… these simple visual tools direct the user’s attention to the vital information.

The Future of Visual Analytics

Businesses are looking for techniques and technologies that enable users to immediately start working with data to build visual analytics and simply obtain value from the data more readily. As such, some vendors have incorporated tools that allow business owners to interact with the data while it flows through the pipelines.

This means users are no longer required to wait for terabytes of data for the completion of refinement processes before accessing the data. More improvement in this area is expected and will continue to be expected well into the future. This allows for quick insight and action and typically comes in the form of management consoles and dashboards and this will continue to increase our capabilities at a dramatic pace.