The evolution and future of interactive data visualization (Part 5)

The future of interactive data visualization, predictions from 2024 onward

Interactive data visualization is an essential component in the current state of our web. But like everything else, it must evolve to keep pace with technological and societal progress. As our world becomes increasingly data-driven and technologies like AI, the Metaverse, and the decentralized web gain momentum, pushing interactive data visualization to the next level is crucial. In this article series, we explore the drivers of the evolution of interactive data visualization over the past decades and the challenges ahead.

Progress is built upon learnings from the past. In just three decades we’ve made major advances in interactive data visualization as a result of progress in four influential drivers of change:

  1. Technology development
    Technology development resulted, on one hand, in improved processing power that gives way for more complex and faster analysis. On the other hand, we gained new interaction and (portable) display possibilities with new hardware and software tools. Extensive and more reliable networks were built and improved, transfer speeds increased, and so did the ways in which we collect and store data, which allowed us to create, share, and access data anywhere.
  2. Available data
    There is more and more data available, in better quality and more open to everyone. We moved from collecting data on a global/ country/organization level to very personalized datasets. The goal of data collection shifted from keeping track of our world on a meta-level to keeping track of our world on a micro-level.
  3. Usability and user base
    By the increased availability and affordability of hardware and software the user base grew rapidly. More users started creating and using interactive data visualizations. This also led to the demand for easier-to-use tools to create and interact with data visualization, and along the way, conventions on how to interact with these tools were settled.
  4. Societal values
    Ultimately, all these developments resulted in users asking for greater alignment with societal values, which are our common shared ethical demands on the impact of technology on society, like privacy, governance, equality, or sustainability.

The progress and knowledge generation across these four drivers varied over time. The technological sector and society have assigned different weights and priorities to them, which has brought us to today’s state of the web and online interactive data visualization.

The more recent inclusion of societal values in data collection and communication could normalize the widespread adoption of data visualization; following the consequences of the hype we experienced 5 years ago. However, we could also expect that with technology ever improving, new ways of collecting and consuming data will bring us new opportunities for data visualization adoption across fields.

A graphical representation of the lifecycle of a technology product, specifically using "Radar" as an example. It illustrates the stages of Invention, Refinement & Augmentation, and Productization over a span of approximately 20 years, depicted along a horizontal timeline at the bottom. The graph is presented as a curved line that starts flat and then rapidly rises towards the "Productization" stage, indicating a significant increase in value or impact, culminating at $1 billion (marked at the peak on the right side). The graph is color-coded with yellow shading that intensifies into red as it approaches the peak, and the three stages are highlighted with red ellipses.
The long nose of innovation’ model by Bill Buxton published in 2008

But this could also be the end of interactive data visualization development, and its currently fully adapted state could bethe best that it will ever be. If we look at Gartner Hype Cycle (which explains the “maturity, adoption, and social application of specific technologies”), we could expect data visualization to have already plateaued years ago. And if we look at “the long nose of innovation” model by Bill Buxton (which suggests that there’s a lag of 10 years between technology development and adoption), we already surpassed the 1 billion market size of data visualization by a mile. Did we really reach the end of the development of interactive data visualization?

Where is our vision?

In the 1980s, we dreamed of interactive screens and real-time analysis. Tron painted us a future where we could become part of a virtual reality, and literature hinted at immersive digital worlds. Today we seem to have stopped dreaming or, at least, we have stopped having a hopeful and optimistic lens. Movies portray future visions as how we already thought the future would look like years ago, or predict a dark and somber future. And no real long-term future visions seem to be shared by corporations anymore.

Have we stopped dreaming about the benefits that information/data visualisation can bring us?

As we look at the progress from the past three decades, we can’t help but wonder what the new ways of consuming, creating, and sharing information will be. We believe that we, as designers and creators of interactive visualizations, can still push it to a new level.

Paralised patiend using Neuralink to play chess

Do we still need data visualization in the future?

These ambitions to continue progress are already resulting in new and promissing technologies. With tools like Neuralink, that allow users to have a direct connections from their brain with their computer, we can even question if we still need ways to visualize data, now that we are almost able to connect basically our brain to every (open) data center in the world. By incorporating AI analytical techniques and the power of super computers into these products, we could potentially derive conclusions from that data, extend our brains, and use all the available knowledge in the world in real time. Will all the world’s data eventually become instant wisdom for everyone, skipping the steps of transforming data into information (by analyzing and visualizing it), and turning it into knowledge (by creating insights / stories)?

A pyramid diagram that represents the hierarchy of data transformation, progressing from "Data" at the base to "Information," "Knowledge," and "Wisdom" at the top. An arrow curves around the left side of the pyramid, pointing upwards, illustrating the flow or progression from raw data to wisdom. The pyramid is divided into four horizontal sections, each labeled with one of these concepts, indicating the stages of processing and understanding data.

Our vision of the the next major driver for the future

We don’t think it is reasonable to expect from new AI techniques and greater data access to instantly derive the ‘right’ wisdom at the ‘right’ moment. Interfaces will continue to be essential to manage the information that makes up our thoughts.

“19th century culture was defined by the novel, the 20th century culture by cinema, the culture of the 21st century will be defined by the interface.”

By Lev Manovich author of books on digital culture and new media, and founder of the Cultural Analytics Lab.

After exploring the drivers that have shaped today’s interactive data visualization, we expect these three main pillars to have a significant impact on how we will visualize data in the coming 10 years:

1. Adaptive personalized insights
Democratizing access to quality-data-derived knowledge will require the implementation of different new methodologies and AI techniques to control our digital devices (i.e. using natural language in voice commands or through gestures). This will not only allow us to adapt the content of the message but also the form, level of understanding, etc. Progress in this field will make data insights more easily available for all types of media consumers across cultural and educational backgrounds, experience degrees, skillsets, physical conditions, or available time to digest the insight.

2. The meta interface
As our digital and physical worlds become more intertwined and complex, it is imperative that we maintain control over our data-driven world. Fortunately, AI tools allow us to structure data automatically, detect relationships, and extract valuable knowledge. In fact, we can now derive insights without manually exploring the data. However, to make informed decisions based on our available data, information, and knowledge, we require an overarching visual overview that can help us determine the most insightful message within data. This is why some experts refer to this era as ‘the age of the interface’, where interactive data visualization could potentially serve as the key interface for ‘directing’ our data-driven world.

3. Checking data quality
There is a growing need to keep track of the online information subjected to fake news, data privacy concerns, a decentralized web, and black-box AI solutions. We seem to be slowly shifting from a “more-is-better” attitude (big data, big tech…) towards a “higher-quality-is-better” attitude ( fact checking, crowd owned, …). How do we keep track of data quality? Will elaborate interactive data visualizations allow us to better understand how information is created, check sources and define what the quality of information is?


Although it now seems we could posses all wisdom in the world at a glance, our belief is that data visualization still has much potential to play a crucial part in addressing current and future societal challenges, empowering us to maintain oversight over emerging technological advancements, and ultimately equipping us with the insight to improve our own well-being, that of our environment, and the of digital realm. How this will look is for us yet unclear, and so there lies a huge challenge for designers, developers and information specialists to define this. But we believe that ‘the real interface to our data-driven’ world is yet to be discovered.

CLEVER°FRANKE is a data design and technology consultancy that creates data-driven experiences. We pioneer through data, design, and technology to unravel complexity and help people make sense of the world around them. C°F’s work results in data-driven web products, installations, visual identities, data design systems, and visions for global operating clients like Google, Warner Music Group, and the United Nations.

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