Data visualisation, a representation of data that is formed in a way to reach its audience faster and better. It may result in basic graphics such as charts, plots or in much cooler graphics by giving it a go on infographics, drawings or even animations.

Way back in the pre-digital era, this meant manual plotting on graph paper using rulers and protractors. When computers made their appearance, softwares like Excel revolutionised this process and this is how we had pie charts and scatter diagrams within a few clicks. But even then, large data sets was a challenge. They were like huge oceans, full of potential insights but too big to navigate through manually.

Now with the AI in visualisation, cluster algorithms can group huge amounts of data into meaningful categories without the smallest human intervention. Imagine it like diligent librarians who categorise and organise a huge library of information so we can understand and interpret it more easily.

The developers, AI specialists are the architects who build robust structures that can handle a deluge of data. They weave algorithms and ensure that the system can not only understand huge data sets but also anticipate user queries. For them, every line of code is a step towards making the platform more responsive and insightful

The computing power of AI may come up with its own idiosyncratic interpretation every now and then which underlines the importance of human oversight in the world of AI.

Designing data with AI

Designers are the storytellers, translating the language of data into a visual narrative. They often sketch, repeat and double think over the right colours to use. In fact, the importance of colour choice in design is not at random because it is needed to have an understanding on the psychology of colour, cultural and contextual considerations. The strategic use of colour combinations can dramatically affect the user experience. Decisions like ensuring colour contrast for better accessibility are not just aesthetic choices; they are crucial for creating inclusive designs.

Even though AI may very occasionally need human supervise, yet we remain grateful for their complexed coded algorithms. The possibilities now are endless on transforming complicated computations into accessible, intuitive visual stories.The transformative power of AI has broadened the spectrum of what we can achieve with representing data.

Conclusion

As we’ve journeyed shortly through the evolving ways of data visualisation, it is evident that our search for understanding is deeply sourcing from storytelling. From calculating data with our fingers, following primordial maps to today’s dynamic AI-driven visuals… we have embarked on an immersive journey of constant innovation and discovery. The digital age has given this journey a huge boost. We are now no longer just calculating numbers, but weaving emotional, data-driven stories. So readers, your canvas awaits a remake as we’ve seen what AI can craft with visuals that breathe, adapt, and resonate.