The data landscape has changed significantly since its beginnings in the 1960s. The field of data analysis alone has undergone multiple transformations over the last decade: it has become digitalized and its focus has shifted towards analysis big data to adapt to the changing digital landscape with enhanced data processing and storage opportunities. Today, data analysis is transforming again due to the rise of generative artificial intelligencethat changes the way we work with data, starting with code generation and go up to data visualization.
Data visualization is an integral part of data storytelling and a powerful tool that can influence business decisions. This is also one of the areas where AI is making significant improvements. Automation, personalization, and improved collaboration are just some of the benefits AI brings. AI And machine learning (ML) are rapidly changing the way we interact with and present various information.
Most AI-powered data visualization techniques are still evolving and are however mainly used by data teams. Thus, the field has not yet reached democratization. Additionally, the use of AI in data visualization brings challenges and risks, such as data privacy, security, and increasing business user training costs.
Let’s take a deeper look at how AI is changing data visualization, what we’re already benefiting from, and what improvements we can expect in the future.
AI-powered data visualization technologies
- Natural language queries/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 increasing rapidly. It is predicted that, by 2025, the world will generate over 180 zettabytes of data annually. Most of this data will be unstructuredwhich means that effective data management and data visualization techniques will be more important than ever.
Given the scale of the data being created, the human ability to manage it would collapse without the recent development of generative AI. AI can work with volumes of data that are unimaginable to us and analyze information almost in real time. AI also interprets data to recognize patterns that a human eye might easily miss.
Additionally, the AI has improved computer science And cleaning. AI identifies missing data and inconsistencies, which means we get more reliable data. datasets for effective visualization.
Personalization is another advantage offered by AI. AI-powered tools can personalize visualizations based on defined goals, context, and preferences. For example, let’s say a sales team wants to track quarterly performance. In this case, AI can automatically generate a dashboard with line graphs highlighting sales trends, bar graphs comparing different regions, and a customer engagement heat map. This saves time and can also be useful when looking for alternative or more creative ways to present the available data.
Last but not least, AI has improved collaboration. Widely used platforms like Power BI can integrate AI-powered 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 suggest different data presentation scenarios, e.g. recommend sentiment analysis visualization for the marketing team and predictive models of revenue trends for the finance department.
However, what AI has yet to achieve is the democratization of data. Non-technical users (for example, people sales, marketing, productAnd customer support departments) still struggle to leverage data, create dashboards, and collaborate with data teams. Although AI is expected to be useful in this area, we are simply not there yet. Today, there are many different tools available on the market, and all of them have advantages and disadvantages. Unfortunately, the industry has not focused enough on developing the best visualization solution.
AI-powered visualization techniques
Although there is still a long way to go, AI and ML have already shown great potential for improving various data visualization techniques. Some companies use these techniques to gain a competitive advantage, while others are still evaluating the risks.
Interactive visualization is one of the areas in which AI clearly demonstrates its potential. For example, use natural language queries (NQL) for data visualization makes it easy to obtain valuable insights into data trends. You can simply provide relevant data and ask a AI-powered chatbot to view a bar chart comparing last year’s sales to this year’s sales. This simplified process makes data analysis more accessible 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 on the real world, creating immersive visual experiences. It is particularly useful for visualizing geographic data. While traditional maps offer a top-down perspective, AR mapping systems take existing mapping technologies, such as GPS, satellite images and 3D models, and combine 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 headsets can view real-time data on pipeline conditions, pressure levels and maintenance needs, reducing the need for frequent checks of actual equipment in real life.
Business users will definitely 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 simple language. This information can become the basis for data visualization.
For example, data scientists can use NGL tools like ChatGPT or OpenAI’s Narrative Science to automatically generate business intelligence reports and highlight key points and trends. Instead of manually sifting through complex data sets and creating graphs, NLG tools can be used to analyze the data in seconds and produce a detailed summary report.
Real-time data visualization is crucial to monitor recent trends and identify anomalies to make quick decisions. AI can power real-time dashboards and interactive data feeds that generate a dynamic view of data, allowing users to track changes and respond to events on the fly.
This technique can help with many business initiatives, such as fraud detection (AI-based dashboards can track millions of banking transactions per second), market trend analysis (AI tools can monitor real-time sales performance in various regions) or social media tracking. publish performance in a real-time dashboard.
AI-based data visualization techniques have already been applied by some companies and may become more widely used in the near future. However, before we can achieve widespread adoption, we need to address challenges.
The Pros and Cons of AI-Driven Data Visualization
When it comes to AI, data privacy and security are the hot topics. Using AI for data visualization also raises a question of ethical responsibility and the need to represent data fairly. These challenges must be addressed very seriously.
Data privacy must be at the top of the priority list, along with transparency of data sources and collection methods. Using publicly available data and deep access to the nature of the data collected can reduce privacy-related data management errors. Security risks can be minimized using reliable AI tools to avoid costly costs. data leaks.
Another challenge is data silos. Companies often struggle to integrate data from various sources and across different internal business systems. This complicates data visualization because the information may be in different formats and may not be easily compatible. Acquiring data from different business departments can be another challenge. Data silos are a complex problem and the best solution varies greatly from case to case.
Finally, data democratization itself, including user education, can also be a major problem for many companies. Even AI-based visualization techniques still require technical expertise. Ensuring that everyone in the organization knows the broad context of business data and interprets it appropriately creates a lot of additional work for data teams, from the need to integrate different data tools used by different teams, to constant internal training. .
The future of data visualization
We live in exciting times where AI is transforming almost everything it touches. In the area of data visualization, businesses can already benefit from some benefits: automated insights, improved data processing and cleaning, personalization and better collaboration.
Soon, we can expect even wider adoption of AI as it rapidly powers data visualization techniques. Just ten years ago, we could hardly 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 remain vigilant and always consider the limitations of AI. Data leaks, mismanagement of private data, and algorithmic fairness are some of the challenges that businesses must properly consider when moving towards AI-based data analytics.
