How AI Is Propelling Data Visualization Techniques

By Andrius Palionis, VP Enterprise at Oxylabs

The data landscape has changed significantly since its early days in the 1960s. Data analytics alone have seen multiple transformations in the past decade: It became digitized, and its focus shifted to analyzing big data to accommodate the changing digital landscape with enhanced data processing and storage opportunities. Now, once again, data analytics are transforming due to the rise of generative artificial intelligence (AI), which is changing how we work with data, from code generation to data visualization.

Data visualization is an integral part of data storytelling and a powerful tool that can influence business decisions. It’s also one of the areas where AI is generating significant improvements. Automation, personalization, and improved collaboration are just some of the benefits that AI is bringing to the table. AI and machine learning (ML) are contributing to data visualization techniques and changing how we interact with and present various information. 

However, most AI-powered data visualization techniques are still evolving and mainly used by data teams. Thus, democratization is yet to be achieved. Moreover, AI in data visualization brings challenges and risks, such as data privacy, security, and the increasing costs of business user training. Let’s dive deeper to see how AI is changing data visualization, what we already benefit from, and what improvements we can expect in the future.

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AI Is Changing the Way We Work With Data

The amount of data created, captured, copied, and consumed around the world is rapidly growing. Statistics forecast that by 2025, the world will generate more than 180 zettabytes of data annually. Most of this data will be unstructured, which means efficient data management and visualization techniques will be more important than ever. 

Considering the scope of the data created, the human ability to deal with it would fall rapidly if not for the recent development of generative AI. AI can work with volumes of data that are unimaginable to us and analyze information in near real time. AI also analyzes and interprets data to recognize patterns a human eye could easily miss.

Moreover, AI has improved data processing and cleaning. AI identifies missing data and inconsistencies, which means we end up with more reliable datasets for effective visualization. 

Personalization is yet another benefit AI has brought. AI-powered tools can tailor visualizations based on set goals, context, and preferences. For example, a user can provide their business requirements, and AI will provide a customized chart and information layout based on these requirements. This saves time and can also be helpful when creativity isn’t flowing as well as we’d like.

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Last but not least, AI has enhanced collaboration. Various tools can now respond to user input and feedback, helping teams create and update interactive and dynamic visualizations.

However, what AI hasn’t managed to achieve yet is data democratization. Non-technical users (e.g., people in sales, marketing, product, and client support departments) still struggle to employ data, build dashboards, and collaborate with data teams. While AI is expected to make a positive difference, we’re just not there yet. Today, many different tools exist in the market. All of them have some advantages and disadvantage. Unfortunately, the industry hasn’t focused enough on developing the best visualization solution. 

New Visualization Techniques: From Augmented Reality To Real-Time Data Streams

While there is still a long way to go, AI and ML have already shown great potential for improving various data visualization techniques. Some companies employ these techniques to gain a competitive advantage, while others still weigh the risks.

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Interactive visualization is one field where AI is demonstrating its clear potential. For example, employing natural language querying (NLQ) for data enables a simplified way to gain valuable insights into data trends. You can feed relevant data and ask an AI-based chatbot to show a bar chart comparing last year’s sales with this year’s. This simplified process makes data analytics more available to non-technical users.

Augmented reality (AR) and 3D visualizations combined with AI can make us feel like we’re in a video game. AR overlays data onto the real world, creating immersive visual experiences. It’s useful for geographic data visualization in particular. While traditional maps provide a top-down perspective, AR mapping systems use existing mapping technologies, such as GPS, satellite images, and 3D models, and combine them with real-time data. For example, Google’s Lens in Maps feature uses AI and AR to help users navigate their surroundings by lifting their phones and getting instant feedback about the nearest points of interest.

Business users will appreciate how AI automates insights with natural language generation (NGL). It converts data into easy-to-read reports and summaries and explains data trends and insights in plain language. These insights can become the basis for data visualization. For example, data scientists can use NGL to automatically generate business intelligence reports and highlight key points and trends. Such reports can help stakeholders understand complex data faster. 

Real-time data visualization is crucial for monitoring recent trends and identifying anomalies to make quick decisions. AI can power real-time dashboards and interactive data streams that generate a dynamic view of data, enabling users to track changes and respond to events on the fly. This technique can contribute to numerous business initiatives, such as detecting fraud, analyzing market trends, or monitoring social media performance in a dashboard.

Some companies have already applied these techniques. However, we must address the challenges before they can be widely adopted.

The Pros and Cons of Visualizing Data With the Help of AI

Data privacy and security are the hot topics when it comes to AI. Using AI for data visualization also brings out a question of ethical responsibility and the need to represent data fairly. These challenges should be addressed very seriously. 

Data privacy should be at the top of the priority list, along with transparency in data sources and collection methods. Using publicly available data and thoroughly accessing the nature of collected data can reduce privacy-related data mishandling. The security risks can be minimized using reliable AI tools to avoid costly data leaks. 

Another challenge is data silos. Companies often struggle to integrate data from various sources and throughout different internal business systems. This complicates data visualization as information can come in different formats and may not be easily compatible. Acquiring data from multiple business departments can be another challenge. Data silos are a complex issue, and the best solution will greatly vary case by case.

Finally, data democratization, including user training, can be a big pain point for many companies. Even AI-driven visualization techniques still require technical expertise. Ensuring everyone in the organization knows the vast context of business data and interprets it properly creates a lot of additional work for data teams.

Conclusion

We’re living in exciting times where AI is transforming nearly everything it comes close to. In the field of data visualization, businesses can already enjoy some benefits, too — automated insights, improved data processing and cleaning, personalization, and better collaboration. 

Soon, we can expect to see AI adopted even more broadly since it’s quickly propelling data visualization techniques. Just a decade ago, we could barely imagine that NQL, AR and 3D visualizations, NGL, and real-time dashboards would have anything to do with data visualization. Today, these techniques are changing the way we interact with data.

The future of data visualization is dynamic, adaptive, and user-friendly. However, we must stay vigilant and always consider AI limitations — data leaks, private data mishandling, and algorithmic fairness are several challenges businesses must properly consider when turning to AI-powered data analytics. 

Andrius Palionis is VP Enterprise at Oxylabs.