The data landscape has changed significantly since its early days in the 1960s. The field of data analytics alone has 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 data analytics is transforming once again due to the rise of generative artificial intelligence, which is changing how we work with data, starting with code generation and going all the way 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 driving 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 quickly changing the way we interact with and present various information.
Most AI-powered data visualization techniques are still evolving and are mainly used by data teams, however. Thus, the field has yet to achieve democratization. Moreover, the use of AI in data visualization brings challenges and risks, such as data privacy, security, and the increasing costs of business users’ 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.
AI-Powered Data Visualization Technologies
- Natural language querying/AI chatbots.
- Augmented reality/3D modeling.
- Natural language generation.
- Real-time data visualization.
AI Is Changing the Way We Work With Data
The amount of data created, captured, copied, and consumed around the world is growing rapidly. One forecast projects that, by 2025, the world will generate more than 180 zettabytes of data annually. Most of this data will be unstructured, which means that efficient data management and data visualization techniques will be more important than ever.
Considering the scope of the data created, the human ability to deal with it would collapse 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 nearly real time. AI also 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 data sets for effective visualization.
Personalization is yet another benefit AI offers. AI-powered tools can tailor visualizations based on set goals, context and preferences. For example, suppose a sales team wants to track quarterly performance. In that case, AI can automatically generate a dashboard with line charts highlighting sales trends, bar charts comparing different regions, and a heat map of customer engagement. This saves time and can also be helpful when looking for alternative or more creative ways to present the data at hand.
Last but not least, AI has enhanced collaboration. Widely used platforms like Power BI can integrate AI-driven features that respond to user input and feedback, helping different teams create and update interactive and dynamic visualizations. So, if different teams with different goals use the same data set, AI can come up with various data presentation scenarios, such as recommending sentiment analysis visualization for the marketing team and predictive revenue trend models for the finance department.
What AI hasn’t managed to achieve yet is data democratization, however. 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. Although AI is expected to help here, we’re just not there yet. Today, there are many different tools available on the market, and all of them have some advantages and some disadvantages. Unfortunately, the industry hasn’t been focused enough on developing the single best visualization solution.
AI-Powered Visualization Techniques
Though there is still a long way to go, AI and ML have already shown great potential for improving various data visualization techniques. Some companies are employing these techniques to gain a competitive advantage, while others are still weighing the risks.
Interactive visualization is one of the fields where AI is demonstrating its clear potential. For example, employing natural language querying (NQL) for data visualization enables a simplified way to gain valuable insights into data trends. You can simply 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 particularly useful for geographic data visualization. Although traditional maps provide a top-down perspective, AR mapping systems use existing mapping technologies, such as GPS, satellite images, and 3D models and combines them with real-time data.
For example, big oil and utility companies use AR for on-site data visualization of oil fields, reservoirs and pipelines. Engineers wearing AR helmets can see real-time data about pipeline conditions, pressure levels, and maintenance needs, reducing the necessity to do frequent checks of the actual equipment in real life.
Business users will certainly 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 tools like OpenAI’s ChatGPT or Narrative Science to automatically generate business intelligence reports and highlight key points and trends. Instead of manually sifting through complex data sets and creating charts, NLG tools can be used to analyze the data in seconds and produce a thorough summary report.
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 fraud detection (AI-driven dashboards can track millions of bank transactions per second), market trend analysis (AI tools can monitor real-time sales performance across various regions), or tracking social media post performance in a dashboard in real time.
AI data visualization techniques have already been applied by some companies and may become more widely used in the near future. Before they can be widely adopted, however, we have to address the challenges.
The Pros and Cons of AI-Based Data Visualization
When it comes to AI, data privacy and security are the hot topics. 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 makes data visualization complicated as information can come in different formats and may not be easily compatible. Acquiring data from different 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 itself, including user training, can also be a big pain point for many companies. Even AI-driven visualization techniques still require technical expertise. Making sure that everyone in the organization knows the vast context of business data and interprets it in a proper way creates a lot of additional work for data teams, from the necessity to integrate different data tools used by different teams, to constant in-house training.
The Future of Data Visualization
We’re living in exciting times where AI is transforming nearly everything it touches. 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’s even more broad adoption 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. We must stay vigilant and always consider AI limitations, however. Data leaks, private data mishandling, and algorithmic fairness are some of the challenges that businesses must properly consider when turning to AI-powered data analytics.
