The evolution and future of interactive data visualization (Part 4)

Web 3.0 — The start of the decentralized, user-owned and immersive web, approx. 2014–2024

The next step of digital reality

By the year 2014, we realized we needed more than just sharing our opinions and content through the existing platforms as the intermediaries, so Web 3.0 and other initiatives like the Fediverse were born as a more decentralized internet. The new web allows us to not just read, and write on the internet, but also to own what we do there. A web built around decentralized governance, and also around new and improved technologies that pave new ways of processing information and building more personalized and immersive experiences: machine learning (ML), artificial intelligence (AI), virtual reality (VR), and augmented reality (AR).

Keeping track of physical life in our digital space

With better coverage of internet connection and improved transfer speeds, devices became smaller and less energy-consuming. A whole new type of data capturing devices became popular. This was the birth of the smart movement, which resulted in smart watches, smart homes, smart industries, and smart cities. These devices captured data using different types of sensors and enabled us to keep track of what was happening in the physical space. All this slowly contributed to making our physical lives part of our digital life and vice versa.

The flip side of these new tracking devices also became apparent: digital data has consequences in the physical world. For example, an open fitness tracking app that brings us together, once revealed the location of secret US army bases in Afghanistan.

Easier tools to create interactive visualizations

But beyond the growing amounts of user-generated data and their implications, the new web also started hosting new tools that allow to keep track of organization data in a structured ways, and other easy-to-use tools that allow to create interactive data visualizations.

By this time, the technologies to compile and chart quality data have improved to the point of allowing for the integration of interactive data visualization across platforms and targeted towards different users. On one hand, we find interactive data visualizations for the general user, like those integrated in personal devices that we use in our regular daily lives (sports, leisure, weather, health…), or as part of visual narratives and storylines that keep us informed or learning about the state of the world and major events (like scrollytelling articles in news sites). On the other hand, we also find interactive data visualizations designed for more targeted users, like industry-specific business intelligence tools and dashboards used for internal strategizing and planning, or power user-oriented and custom visualizations for research purposes.

Data amounts skyrockets

We didn’t only find new and more convenient methods to chart data for specific user groups and their preferred interfaces. Over the course of time, the costs to store data plummeted.

With all the data generated by companies, governments and people, the amount of data we store globally rocketed. Besides that, new currents of data are continuously added to the existing streams of data. ‘Big data’ becomes super big.

We now increasingly rely on the help of algorithms based on machine learning and artificial intelligence to automatically detect what information has priority. These algorithms, controlled by ourselves or others, have the power to consciously or unconsciously influence what the end user consumes.

In this sense, in the new web, we don’t keep track of everything anymore; but we or others determine more and more the rules of what we see instead. Data visualization becomes a tool to give us an insight into all available data and help us to define the rules that provide us with information, and with that our view of the world.

Controversy

Data is now captured, exchanged, and stored everywhere and is analyzed by many. In some cases, for good causes and in other cases, for illegitimate purposes. Sometimes, it can also unintentionally turn into a negative societal impact.

We now also talk about ‘Big Tech’ (companies like Alphabet (Google), Amazon, Apple, Meta (Facebook), and Microsoft) and the big influence that these technologies, software and social media platforms have on our society.

2014–2024 future visions:
More and more books are published also pointing out examples where things went wrong with the use of big data and data visualization. Books like the Weapons of Math Destruction and New Dark Age paint a sinister future of our digital and physical life. The fiction George Orwell described in his famous 1984 book seems to become partially reality. Also series like Black Mirror and movies like The Circle focus on the negative impact data can have for humanity.

Movie example: The Circle movie, released in 2017, based on the novel by Dave Eggers

The bright future of tech and data full of potential, turns into a dark future vision, full of threats.

Redefining the internet

New initiatives of the decentralized Web3 and others like the Fediverse were born as a movement that advocates for less influence by the Big Tech and a decentralized internet owned and governed by its users. This can also lead to new ways of keeping track of our data, such as not storing and accessing it via the Big Tech companies but saving it ourselves on our own (collectively owned) infrastructure.

Interactivity for new dimensions

Data visualization can also become a more immersive experience in the metaverse through the use of AR and VR technologies (developed by Meta and other tech actors like Microsoft, Nvidia, or Roblox). Even in this new technological space, data visualization will continue to make the most of advancements in other fields. For example, Unity, originally developed for the video game sector, is now being implemented in the data visualization and animation movie industry.

AR visualization example: EnARgy experiment by CLEVER°FRANKE in 2017 showcasing the amount of energy use by each device in a room using object recognition and

However, as it has happened throughout history, not every innovation has been well received by users. Google Glass was released in 2013 as an early prototype of AR. This head-up display, running on natural language voice commands and connected to the internet, offered a new way of finding and representing visual information that did not do well in the market (and, in fact, it is no longer commercially available).

Data display example: Microsoft Hololens 2, released in 2019, showing a digital twins and holoportation demo

Similarly, the first affordable and crowdsourcing-funded Occulus VR set still doesn’t have the amount of users as many predicted when it was first introduced. In fact, it is estimated that VR headset sales worldwide declined 12% in 2022.

Regardless of their reception, these have been, however, bold attempts at challenging the traditional WIMP (windows, icons, medium, pointer) interfaces by allowing interactions through voice prompts that take advantage of the continuously progressing natural language processing capabilities.

Data visualization example: Mashrooi platform made in 2017, to showcasing to progress of real estate projects in Dubai, designed by CLEVER°FRANKE

AI morphs data into new forms

At the end of the Web 3.0 era, AI tools like Dall-e, Midjourney, and ChatGPT emerged and took the internet by storm. The tools showcase how huge amounts of text and images can be interpreted by AI and used to generate realistic visual and textual results based on the user’s desires. The results seem to be generated by humans, while they aren’t. The tools are built upon massive datasets, consisting of mainly human-generated content. The analysis of those datasets using powerful algorithms and models that consume enormous computing power result in these new AI models and tools.

The impact on our data and visual culture is yet unknown, but is already leading us to many questions like: What is the role of a creator in a world powered by AI models? Is the owner of the data used to train an AI also (partly) the owner of the AI model? Can we distinguish AI-generated content from real-life situations and human-crafted content? Will ‘the best’ data visualization eventually be generated automatically by an AI? What will be the implications of AI for privacy and accuracy?

The AI tool landscape is growing rapidly, and also includes tools that help us easily analyze and visualize data to extract insights. These AI-powered analysis methods range from simple tools, like enhanced Google Sheets, to more complex ones, enabling us to turn a dataset into digestible information automatically.

AI tool example: Google Sheets Explore Tool by Tom Mullaney (note: currently unavailable in Google Sheets)

In addition to allowing for data analysis, the new AI tools also give us new means to visualize data in new and unexpected ways. Like we at CLEVER°FRANKE did with this experiment using AI tools and the paintings of Hieronymus Bosch to showcase deforestation in the world since the date Bosh was born:

User needs become society needs

As it happened in previous versions of the web, we now see how technological developments force us to understand how users can make the most of them, and also how addressing user ethical demands becomes a core factor of the internet experience.

The new ways of processing and interpreting information as well as the new formats, interfaces, and devices used to visualize it are still in exploration and refinement. But we’ve seen over history how pioneering through technology and design eventually builds a larger user base on a longer term, one that will also be more private, personal, fair, sustainable, and egalitarian.

Even if we are still exploring the opportunities, capabilities, and limitations of Web 3.0, data visualization becomes both a tool to understand and track the technologies, but also an active participant of it, taking advantage of the developments in data processing and communication.