Tech innovations have reshaped visual reporting elements. Holographs, three-dimensional animations, and real-time dashboard updates increase designers’ and analysts’ methods for data visualization. Meanwhile, generative artificial intelligence (GenAI) can help actualize a no-code revolution in configuring trend depictions. This post explores these advanced data visualization techniques for business insights.
What is Data Visualization?
Data visualization encompasses graphs, scatter plots, diagrams, blueprints, and photos describing performance trends or product mechanisms. Today, online maps use visual signals to alert drivers about incoming traffic, while disaster managers designate urban areas prone to floods and earthquakes with color-coded plans.
Likewise, product designers and engineers study stress-strain variations using virtual 3D models to experiment with ideas. They want to enhance durability and prevent ergonomic problems based on observational discoveries. Accordingly, multidisciplinary teams must develop adequate skills and leverage relevant data visualization tools to accomplish similar objectives.
Analysts can develop waterfall charts to represent step-wise increases and decreases in values based on changes in external factors, like stakeholder participation in a customer journey or sales. At the same time, bar charts, matrices, dashboards, scatter plots, and heat maps are valuable in understanding trends.
Types of Advanced Data Visualization for Business Insights
1| Advanced Pie and Donut Charts
When small sectors in a pie chart require an additional breakdown, data presenters create another pie chart and connect the two using arrows or monochromatic color schemes. However, the presentation becomes cluttered if you employ this data visualization strategy for multiple sectors. So, interpreting data will be more challenging.
Advanced pie and donut charts describe multiple related values using concentric “donuts.” They eliminate the need to create multiple pie charts. Each sector of an inscribed donut ring has projections, like bar charts, providing a granular breakdown. Since established firms in data visualization services want to improve data and insight presentation, they integrate advanced pie and donut charts.
2| 3D Visualization
Three-dimensional data visualization offers an enhanced depiction of real-world phenomena. It has remarkable applications in geological surveys, product performance simulation, multimedia projects, and eCommerce marketing.
You can study gas particle movements, vibrations, heat transfer, physical deformations, and crowd behavior in disasters on your workstation's 2D screens. Besides, the three-dimensional visualization facilitates model rotations. So, you can observe the changes from different perspectives for problem identification.
3D graphs and models can reveal ergonomic hurdles patients encounter while using wheelchairs. They can help track urban areas for exploring air pollution variations between industrial, commercial, residential, and administrative zones. Likewise, automobile and safety gear providers can test safety parameters in several hypothetical accidents via simulations.
3| Bullet Graphs
A bar on a color-coded background describes the gap between actual performance metrics and predefined goals. This approach is a bullet graph, vital to streamlining progress and productivity tracking. Instead of a broad scope, the bullet graph serves specific performance metrics.
Therefore, it differs from pie charts and tree diagrams. However, you can align bullet graphs for distinct performance metrics on the page vertically or horizontally.
4| Holographic Visualization
Generating a 2D expanded view to explain minuscule details and patterns on a screen limits interactivity. As a result, investigations into hologram-based visualization have increased. Equipment like motion tracking systems, passive 3D stereoscopic glasses, and lasers empower researchers to bring two-dimensional data to life.
You can project holograms for 3D and 2D graphs without using motion trackers. In this case, you will require conventional clicking and pointing devices to manipulate the holographic depictions. Additionally, you want to optimize your data models using computer-aided design (CAD) software to make them hologram-friendly.
5| Correlation Matrix
A correlation matrix describes direct or inverse proportionality between two statistical variables. You can also convert it into color-coded visual charts. It allows an ease of pattern detection. Moreover, data scientists require it for exploratory and confirmatory factor analyses.
Python, a popular and user-friendly language, has matplotlib and seaborn data visualization libraries to generate correlation matrix visuals. Seaborn helps create heat maps.
Conclusion
Conventional data visualizations must become more comprehensive, interactive, and dynamic to unlock business insights vital to continuous growth. Therefore, data scientists, engineers, and analysts have dedicated efforts to modernizing visual representations for better pattern recognition.
Advanced data visualization techniques include 3D models, holograms, concentric donut charts, correlation matrices, and bullet graphs. Furthermore, the no-code platforms streamline these methods through drag-and-drop experiences.
Given the rise of GenAI applications, the world might get prompt engineering specialists to help innovate data visualization strategies. After all, this discipline must keep evolving for robust conflict resolution and disruptive reporting innovations.
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