In today's digital age, data is vital. It drives innovation, growth, and decision-making. But data alone is not enough. We need to make sense of it, to find the hidden patterns, trends, and insights that can help us understand the world better.
That is where data visualization comes in - the representation of data through use of common graphics, such as charts, plots, infographics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.
However, AI has dramatically enhanced how we can visualize data. Not only can it help to identify rich insights, but it can also do so quickly, acting as a partner and copilot for data scientists.
Broadly speaking, AI tools have the potential to revolutionize the way we approach many workflows, not least in empowering 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 leveraged to inform strategic decision making.
With that in mind, let’s explore some of the ways we can use AI to supercharge data visualization, what you need to bear in mind for your business, and quickly look ahead to what the future of data science might look like for enterprises in the UK.
Supercharging data analysis through AI
Data visualization was once reserved for experts and formal data analysis, but in the age of AI, has become a tool and fundamental skill accessible to all. Nonetheless, for experienced data analysts, AI has created a whole new world of possibilities.
AI can help streamline the data visualization process by automating some of the 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 dedicate more time to strategy analysis and problem solving, maximizing their impact while minimizing the manual labor that would traditionally be required, something which will most likely make their job more rewarding too.
When working with such large amounts of data, mistakes are also inevitable, but AI can act as a safety net to catch the tiny mistakes a human may miss. This can help improve the quality and reliability of outputs, reducing human errors, biases, and inconsistencies. Working together, AI can help data scientists to validate and verify data visualisation results, as well as to provide confidence intervals and uncertainty measures.
Automate and personalize your data visualizations
AI can also help data scientists to explore new and innovative ways of looking at data visualization, by generating novel and diverse visualization options, as well as by combining and integrating different visualization techniques and modalities. It can also work together with data scientists to help personalize and customize data visualization outputs, and to enhance the aesthetic and appeal of visualized data. A paper from Microsoft Research, recently outlined how researchers created a new Data Formulator tool, powered by AI, which simplifies the process of creating visualizations by allowing data analysts to define data concepts through natural language or examples, which the tool then transforms into structured data for visualization in various formats.
We also know 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, providing textual explanations of the main findings and insights. AI can also provide suggestions and recommendations for the best types of visualizations to use for different data scenarios and audiences. For instance, AI can help data analysts choose the most appropriate charts, colors, and layouts to convey their message effectively.
Ensuring your business is ready to take advantage of the opportunity
AI implementation done correctly could save your workers over 390 hours of working time per year, a near 2-hour saving per day, according to research by Viser and Censuswide.
To make sure your business is ready to take advantage of AI and data visualization however, 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 rely on having accurate, consistent, and reliable data. You need to invest in the right people and technology to ensure that your data is well-structured, well-documented, and well-governed, so that you can avoid errors, inconsistencies, and biases in your analysis and presentation.
- Train and upskill your staff: AI and data visualization require a combination of technical, analytical, and creative skills. You need to provide your staff with the necessary tools, training, and support to use AI and data visualization effectively and ethically. You also need to foster a culture of curiosity, collaboration, and experimentation, so that your staff 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, enhancing customer experience, or discovering new opportunities. You need to define and align your objectives clearly and measure your progress and impact with relevant metrics.
Imagining the future of data visualization with AI
AI can also open 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 across industries.
For augmented reality (AR) and virtual reality (VR) technologies, you could 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 across various industries, 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 visualisations, such as paintings, music, and animations. These applications can have diverse uses across different domains, such as art, design, fashion, and media. Finally, another future application is within Explainable AI (XAI), a branch of AI that aims to make AI systems more transparent, interpretable, and accountable.
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 which has been important to Microsoft since the beginning of our AI journey, as we released our pioneering Responsible AI Standards.
Ultimately, AI and data visualization are two powerful forces that can enhance our understanding and communication of data, as well as unlock new possibilities and opportunities for data scientists and the data visualization industry at large. 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 industries.
AI can help us improve productivity, personalisation, and future possibilities of data visualisation, making data more meaningful and actionable for everyone. AI and data visualisation are not only tools, but partners, in our quest to make sense of 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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