From prehistoric cave paintings to the pictogram-based writing systems of the ancient Babylonians and Egyptians, the affinity for codifying information as visuals has been a defining, persistent characteristic of human behavior.
Many contemporary world languages are still heavily pictogram-based, with modern Chinese serving as the most long-lived example. And as human languages are in continuous evolution, so are the means and methods for encapsulating knowledge in images.
In the context of the information age, this human trait is manifest in the design of visually compelling, highly interactive experiences for sight-based faculties of human perception. By providing digital visual representations of physical objects, data visualization enables human operators to more easily manage vast data sets, glean insights from a myriad of information sources simultaneously, and perform powerful operations more intuitively and tactilely.
Data Visualization Today
Data visualization can enhance the value of information by incorporating motifs, objects, and imagery native to a specific use case and/or industry.
From agriculture data visualization used in prescriptive crop planning to augmented reality (AR) in financial services for mapping out data-driven wealth management scenarios, industry-specific applications of data visualization technology are enabling businesses and consumers alike to make better-informed decisions.
Indeed, global market demand reflects the growing pervasiveness of data visualization. In 2019, data visualization was valued at $9.06 billion and is projected to grow at a compound annual growth rate (CAGR) of 7.83% for a market size of $15.35 billion by 2026.
See more: How Data Visualization is Taking Form for Home Depot, Emblem Health, Singapore, Members First Credit Union, and Geospatial
5 Trends in Data Visualization
The following trends in data visualization reflect the general move toward use-case optimized visual experiences and the accessibility of data visualization across both devices and industries.
1. Emergence of High-Fidelity Digital Twins
Digital twins are virtual models of physical objects/systems created by pulling in data streams related to the physical asset in question (e.g., telemetry from onboard sensors monitoring temperature, vibration level, etc.). This enables the remote monitoring of performance and health/condition parameters, allowing for physical assets to be analyzed and assessed from afar.
In the past, these digital models were presented to users in the form of interactive dashboards and continuously updated metrics. Newer offerings such as Oracle’s IoT Asset Monitoring Platform and Microsoft Azure Digital Twins integrate data streams with 3D asset models for truly high-fidelity digital twins — the ultimate in data visualization.
3. Verticalized Data Visualization Offerings
As traditional industries undergo digitization, data visualization will become more specialized to the needs of specific industry audiences. For example, data visualization in shipping and maritime is enabling ship owner/operators to improve vessel performance and monitor safety and operational conditions. Similarly, the automotive industry is using data visualization to optimize vehicle product development workflows.
4. Data Visualization Optimized for Mobile
With over half of total page views in 2020 occurring on phones and handheld devices, site operators have been well-advised to focus on mobile in their user experience optimization and improvement efforts. Enterprise SaaS offerings and business software platforms are following suit by taking a mobile-first approach to data visualization. That is, they are prioritizing their data visualization and widget designs for optimal viewing on the small screen. For example, the mobile version of Salesforce offers a feed-first design, mobile-enhanced dashboards/reports, and a flexible “Component Visibility Rules” feature for defining which data visualization components are displayed on mobile devices.
5. AI-Powered Data Visualizations
Data analysis and management systems were some of the first applications to incorporate artificial intelligence (AI)/machine learning (ML) for automating information collection, analysis, and dissemination. Similar trends can now be observed in the data visualization space, with automated systems leveraging ML models trained on common user patterns and task execution to construct UI dashboards. These components are automatically fine-tuned for delivering relevant, unique visualizations and insights per user. In the future, software solutions will increasingly rely on AI/ML for optimizing data visualizations used in human-computer interactions (HCI) .
Gone are the days when data visualizations on the end user’s display were hindered by bandwidth and hardware resource limitations. Today’s edge machines and constrained devices routinely harness the cloud, while displaying results on inexpensive hardware: in most cases, with render capabilities and connection speeds comparable to that of a desktop on a high-speed wired connection. And, of course, the current global 5G rollout coupled with continuing advances in computing power/storage capacity (e.g., the general availability of graphical processing units) make the data visualization category an exciting space to watch in the near future.
See more: Top Data Visualization Tools for 2021