Leveraging Advanced Data Visualization Techniques to Drive Actionable Insights

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Keywords: Advanced Data Visualization, Data, ADV, data visualization, Actionable Insights

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Ever wondered why data visualization is becoming increasingly popular? The short answer is that advanced data visualization facilitates decision-making processes. How? The answer is explained in this post!

In this article, we will explore the following headings:

· What is Data Visualization?

· What is Advanced Data Visualization

· Features of Advanced Data Visualization

· The techniques of Advanced Data Visualization

· Advanced Data Visualization Tools and Techniques

· How Advanced Data Visualization is used to drive actionable insights

What is Advanced Data Visualization (ADV)?

How do we explain Advanced Data Visualization without first exploring what data visualization is itself?

Data visualization is beyond aesthetics. It is the graphical representation of information and data. It involves using charts and maps such as dots, line charts, funnel charts, heat maps, column charts, etc. to represent data visually to make complex data more understandable and less technical and accessible to the public with no technical knowledge. See pictorial examples below.

Line Charts

This picture shows a line chart where raw data (number of vehicles sold by a company per month) is represented using a line or a set of data points joined by a line.

Bar Chart

A bar chart is a type of chart in which data is represented in either vertical or horizontal bars. The length of the bars is proportional to the value they represent. They are often used to compare trends.

Now, what is Advanced Data Visualization (ADV)?

Just like Data Visualization, ADV is also used to create a visual representation of complex data in a simple form. The difference, however, is that advanced data visualization uses sophisticated tools to represent data. It goes beyond using charts, graphs, or maps to represent data. Advanced Data Visualization uses automated technologies and machine learning to help users understand the nuances of large and complex datasets.

Features of Advanced Data Visualization

In this section, you will learn the features of Advanced Data Visualization and how they all work together to allow for actionable insights. The features are discussed as follows:

Multi-dimensional Views

Advanced Data Visualization displays data on multiple dynamic planes — multidimensional views. This multidimensional view offers the advantage of simultaneously visualizing and analyzing data across multiple dimensions. With ADV, you can swiftly switch between one plane of data and another. This is very useful when complex data is being represented and needs to be explored from different dimensions, unlike charts or graphs that represent data in just a single dimension.

Advanced Data visualization allows users to visualize and explore relationships, patterns, trends, correlations, and clusters that might not be easily identified or noticeable from raw data or basic data visualization tools such as bar charts or graphs. In essence, it allows looking at data in several dimensions; for example, viewers by region, viewers by demographics, viewers by devices, viewers in a month, through social networks, new versus returning viewers, and so on.

User-Interactiveness

Advanced Data visualization is also interactive, which allows for modification and control over the data. In other words, it allows users to be able to actively interact with, modify, and examine charts rather than merely reading. This means it provides real-time engagement tools that enable users to relate to the displayed information, zoom in and out, highlight relevant information, see more details, ask questions, change the parameters, compare scenarios, filter, conduct what-if analysis, and generate new insights.

Auto Focus

When you are walking through a garden filled with flowers, what is the first thing you notice? It is usually the brightest, most colorful flowers that catch your eye. It is the same with autofocus in advanced data visualization. Autofocus highlights the most important or most useful information and makes them stand out from the rest of the data set. This saves you the stress of having to look for them manually. Autofocus quickly captures the attention of the users and ensures that these data do not go unnoticed. The advantage of this is that these data, if overlooked, can impact decision-making.

Animation

Advanced Data Visualizations are also animated, and this comes with many advantages. First, it allows for multiple layers of information to be displayed within the same display space. It is also more captivating, keeping viewers engaged for a longer period and aiding their understanding.

How Advanced Data Visualization Is Used to Drive Actionable Insights

Actionable Insights refer to conclusions derived from data. These conclusions can be turned directly into action because they provide an understanding of the future and inform what action or response should be taken. It will be difficult for any business to survive without actionable insights because without them, making the right decisions and achieving desirable outcomes becomes impossible.

Advanced Data visualization can be used to draw actionable insights. This is perhaps the reason for the exponential growth of its market size. According to Stratviewresearch, the data visualization market is expected to grow from USD 2.8 billion in 2020 to 5.3 billion in 2026. Furthermore, organizations that invest in data visualization tools, according to Gartner, are more likely to discover timely information than those that rely on traditional techniques.

All the features of Advanced Data Visualization discussed above, such as multidimensional views, show relationships between variables. Multidimensional views make it easy for users to visualize data in different dimensions, for instance, viewing sales by time, product category, region, sales channel, etc. This gives a deeper insight into customers’ buying behavior, the effectiveness of each media channel, and overall marketing strategy which may not be apparent using traditional visualization techniques.

User-interactiveness offers flexibility. Unlike static visuals, it allows users to interact with the data and manipulate or personalize it to suit their tastes. Autofocus gives more prominence to outliers, making it easy for users to pay attention to the most important data set useful in making actionable insights.

Advanced data visualization provides moving illustrations that engage viewers better and guide them through a sequence of events, unlike static charts and graphs. In other words, it not only engages viewers but also allows them to better understand the context and importance which helps in drawing better actionable insights.

Advanced-Data Visualization Tools and Techniques

There are many Advanced Data Visualization tools, including Tableau, D3.js, Power BI, Canvas.js, and so on. These tools contain advanced visualization techniques such as density maps, parallel coordinates interactive dashboards, 3d Visualizations, Sankey diagrams, and choropleth maps, among others, which are used to draw actionable insights for well-informed decision-making.

Density Maps

Density maps are used to visualize density differences in geographic distributions across a landscape.

Sankey Diagrams

Sankey diagram is an example of an advanced data visualization tool and when it is used to visualize data, it shows the flow of a unit of measurement from its origin to its destination. The magnitude of the physical flow is shown by the size of the links.

Heat Map

Heatmaps are an advanced data visualization component where values are depicted using a grid of colored squares. Heatmaps are quite useful in identifying clusters, trends, and outliers within large datasets.

Scatter Plots

A scatter plot is an advanced data visualization that shows the relationship (patterns or correlations) among different variables.

Network Graphs

Network graphs also show relationships or connections between elements. The elements are represented as points or “nodes” and their connections are with links.

Conclusion

Advanced Data Visualization techniques provide a better understanding of data to draw better conclusions and make better decisions. There are many Advanced Data visualization tools to choose from, but the right tool will be best suited for your needs and the purpose you want to use it for.