In today’s digital age, data is crucial. It stimulates innovation, growth and decision-making. But data alone is not enough. We need to make sense of it, find the hidden patterns, trends and insights that can help us understand the world better.
That’s where data visualization comes in: the representation of data through the use of common images, such as graphs, plots, infographics, and even animations. These visual representations of information communicate complex data relationships and data-driven insights in a way that is easy to understand.
However, AI has dramatically improved the way we can visualize data. It can not only help identify rich insights, but can do so quickly, acting as a partner and co-pilot for data scientists.
Broadly speaking, AI tools have the potential to revolutionize the way we approach many workflows, not least by enabling us all to work more effectively. In fact, 70% of early Microsoft Copilot users reported increased productivity. For data scientists, this increased productivity has the potential to radically rethink how data is processed, visualized, and used to inform strategic decision-making.
With that in mind, let’s explore some ways we can use AI to supercharge data visualization, what you should consider for your business, and take a quick look at what the future of data science could look like for companies in the United Kingdom. .
Director of Azure Business Group, Microsoft UK.
Boost data analysis through AI
Once the preserve of experts and formal data analysis, data visualization has become a tool and fundamental skill accessible to everyone in the age of AI. Nevertheless, AI has created a whole new world of possibilities for experienced data analysts.
AI can help streamline the data visualization process by automating some tedious and repetitive tasks, such as data cleaning, preprocessing, and formatting. As AI automates routine tasks, data scientists can ultimately become more efficient. They can spend more time on strategy analysis and problem solving, maximizing their impact while minimizing the manual labor that would traditionally be required, something that will also likely make their work more rewarding.
When working with such large amounts of data, errors are also inevitable, but AI can act as a safety net to catch the small errors that a human might miss. This can help improve the quality and reliability of results, reducing human error, biases and inconsistencies. Working together, AI can help data scientists validate and verify data visualization results, and provide confidence intervals and uncertainty measures.
Automate and personalize your data visualizations
AI can also help data scientists explore new and innovative ways of looking at data visualization, by generating new and diverse visualization options, and by combining and integrating different visualization techniques and modalities. It can also work with data scientists to personalize and customize data visualization outputs, and to improve the aesthetics and appeal of visualized data. An article from Microsoft Research recently outlined how researchers created a new Data Formulator tool, powered by AI, that simplifies the process of creating visualizations by allowing data analysts to define data concepts using natural language or examples, which the tool then converts into structured data for visualization in various formats.
We also know that people have different ways of understanding information. Some prefer visual aids, others like written explanations, and some learn best by doing. AI tools can adapt to these preferences, making data more understandable for everyone. For example, AI can generate natural language summaries of data visualizations, with textual explanations of key findings and insights. AI can also make suggestions and recommendations for the best types of visualizations to use for different data scenarios and audiences. For example, AI can help data analysts choose the most appropriate graphs, colors and layouts to effectively convey their message.
Make sure your business is ready to take advantage of the opportunities
If AI implementation is done correctly, your employees can save more than 390 hours of work time per year, a savings of almost 2 hours per day, according to research from Viser and Censuswide.
However, to ensure your business is ready to take advantage of AI and data visualization, you need to take some steps to prepare your data, your people, and your goals.
– Invest in data quality and management: AI and data visualization depend on accurate, consistent and reliable data. You need to invest in the right people and technology to ensure your data is well structured, well documented and well managed so you can avoid errors, inconsistencies and biases in your analysis and presentation.
– Train and improve your workforce: AI and data visualization require a combination of technical, analytical and creative skills. You need to equip your workforce with the necessary tools, training, and support to use AI and data visualization effectively and ethically. You should also foster a culture of curiosity, collaboration, and experimentation so that your employees can explore new possibilities and insights with data.
– Define and align your objectives: AI and data visualization can help you achieve various goals, such as improving efficiency, improving customer experience or discovering new opportunities. You need to clearly define and align your objectives and measure your progress and impact with relevant metrics.
Imagine the future of data visualization with AI
AI can also open up new possibilities for the future. AI and data visualization are not static fields. They are constantly evolving and innovating, creating new opportunities and challenges for data analysis and communication between sectors.
For augmented reality (AR) and virtual reality (VR) technologies, you can create immersive and engaging data experiences, where users can interact with data in a 3D environment. For example, AR and VR can be used alongside AI to visualize spatial data such as maps, buildings and landscapes or to simulate scenarios such as climate change, natural disasters and urban planning. These applications can have significant impacts in various sectors such as tourism, education, healthcare and entertainment.
Generative adversarial networks (GANs) are a type of AI that can generate realistic and novel images, videos, and sounds from data. For example, GANs can be used to create synthetic data for training and testing purposes, or to generate artistic and creative data visualizations such as paintings, music and animations. These applications can have diverse applications in different domains, such as art, design, fashion and media. Finally, there is a future application within Exploreable AI (XAI), a branch of AI that aims to make AI systems more transparent, interpretable and controllable.
For example, XAI can be used to provide explanations and justifications for the decisions and actions of AI models, or to highlight the limitations and biases of AI systems. These applications can have important implications for various sectors, such as finance, law, security and ethics. Something that has been important to Microsoft since the beginning of our AI journey, when we released our groundbreaking Responsible AI Standards.
Ultimately, AI and data visualization are two powerful forces that can improve our understanding and communication of data, and unlock new possibilities and opportunities for data scientists and the data visualization industry as a whole. By combining the strengths of AI, such as automation, adaptation and innovation, with the strengths of data visualization, such as clarity, engagement and accessibility, we can unlock the full potential of data across all industries.
AI can help us improve productivity, personalization, and future possibilities of data visualization, making data more meaningful and useful for everyone. AI and data visualization are not just tools, but partners in our quest to understand the world around us. As the famous saying goes: seeing is believing. And with AI and data visualization we can see more, understand more and do more with data.
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This article was produced as part of Ny BreakingPro’s Expert Insights channel, where we profile the best and brightest minds in today’s technology industry. The views expressed here are those of the author and are not necessarily those of Ny BreakingPro or Future plc. If you are interested in contributing, you can read more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro