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When it comes to either research, marketing or deep learning data visualization is very if not the most important part of the workflow, when thought to throw we can see data visualization has not really advanced in the past few years, we have seen scientific computing tools and very helpful data visualization libraries like matplotlib but what is the next revolutionary thing that can happen? what is the next big thing in big data?
To understand the future, we have to understand the past and present. So journey with me through time so we can understand and take part in the future.
Data visualization as it was.
Data visualization has evolved significantly over the centuries, transforming from rudimentary representations to sophisticated tools integral to modern data analysis.
Early Beginnings:
The roots of data visualization can be traced back to ancient civilizations. Early humans used cave paintings and symbols to depict information about their surroundings and activities. For instance, the Lascaux Cave Paintings in France, dating between 17,000 and 12,000 years ago, include depictions of animals, human figures, and symbols.
17th Century: The Dawn of Statistical Graphics
The 17th century marked a pivotal moment with the introduction of statistical graphics. In 1644, Flemish astronomer Michael Florent Van Langren created one of the earliest known line graphs, illustrating the varying estimates of the distance in longitude between Toledo and Rome. This visualization highlighted the discrepancies among astronomers’ measurements, showcasing the power of graphical representation in conveying complex information.
18th Century: Development of Chart Types
The 18th century saw the creation of fundamental chart types. Scottish engineer William Playfair is credited with inventing the bar chart, line chart, and pie chart. His works, such as “The Commercial and Political Atlas” published in 1786, introduced these visual tools to represent economic data, laying the groundwork for modern data visualization techniques.
19th Century: Visualization for Social Reform
In the 19th century, data visualization became a tool for social change. Florence Nightingale, a pioneering nurse and statistician, used polar area diagrams—often referred to as “Nightingale Roses”—to illustrate mortality causes during the Crimean War. Her visualizations effectively communicated the need for sanitary reforms in military hospitals, demonstrating the impact of data visualization on public health policies.
20th Century: Digital Transformation
The 20th century witnessed the digitization of data visualization, propelled by advancements in computing technology. Interactive graphs, 3D charts, and complex visualizations became possible, enabling more dynamic and detailed data analysis. Edward Tufte, a pioneer in the field, emphasized the importance of clear and effective visual presentations of data, influencing modern practices.
21st Century: Big Data and Interactive Tools
In the 21st century, the explosion of big data has led to the development of advanced visualization tools capable of handling vast datasets. Interactive dashboards and real-time data visualization have become essential in various industries, facilitating informed decision-making and uncovering insights from complex data.
As we can see data visualization reflects humanity’s ongoing quest to understand and communicate information effectively, but is past we have not created a way to understand the data in better way we have gone to visualize bigger and bigger data.
Data visualization Research as it is.
Data visualization techniques mainly have three properties: VOLUME, VARIETY, and VELOCITY. But if we use psychoanalysis in this area, we would understand the human aspect of all these different techniques as a way for the human mind to make connections and see relationships in the data. As the dimensions of the data grow, it becomes hard to apply techniques. For example, it’s easy to understand 1D data like 1 > 2, and 2D data is harder to visualize. 3D data is nearly impossible if it has a complex sinusoidal property. 4D is impossible since we don’t know what it looks like.
In AI, we work with 15D data and rely on visualization techniques for insights, which have obvious limitations.
Data visualization as it could be!
Humans have a very complex personality shaped by their beliefs, emotions, and experiences, and this data is more than 15D. Humans have 27 emotions, our sensations, and god knows how many fundamental beliefs, we generate a complex domain of choices, selecting the best one out of them. For example, what to do when you don’t have money for the dish you already ordered? Of course, you can rob the damn place, but it’s better for your wife to pay. After all, she is your life partner.
Humans have an exhausted size of working memory, and representing everything in numbers is not possible. We cannot make meaning out of 15D data, so we must somehow correlate data with emotions for better visualization. This won’t result in an understanding of the data but rather better intuition of the data.
How we can do this?
Frankly, I don’t know, BCI (Brain Computer Interface) is something that can help us do this, but not realistic to think that every data engineer is down to stick 2 electrodes in his brain, I think LLMs can help is scratch the surface for this technique, by using it’s Word2Vec system I think it can help us with it’s semantics system intuitively understand the given data.
Of course this doesn’t connect data representations directly with emotions but if you think what are words ? a way to communicate emotions and by this fundamental property of linguistics we might be able to intuitively understand the 15 to 20D data.
Conclusion
This is something I am going to research about to me it does seem, ferly innovative and I hear by coin this term as Manas-Sophia (मनस-सोफिया)
Manas (मनस – Hindi/Sanskrit):
- Refers to the mind, intellect, and thought process in Sanskrit. It captures the human emotional and cognitive dimension.
Sophia (Greek):
- Means wisdom or knowledge in Greek. It symbolizes deep understanding and insight.
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