- The data value problem. The traditional view of valuing all data equally leads to difficulty in identifying quality data as the volume of data increases.
- Quality vs. quantity. The theoretical framework of information theory does not capture the quality of information, only the quantity measured in bits.
- Undervaluation of product designs. The technology industry has historically undervalued the impact of their product designs on energy and materials.
As the world is flooded with information and data, many have given up on the idea of organizing, editing or making sense of it all. We have instead become creators and hoarders.
Part of the problem lies with how the technology industry and much of science has traditionally viewed information. “Information theory does not distinguish between random bit states and quality information,” physicist Melvin Vopson explains. “Let’s say this podcast becomes 50 megabytes. If you have 50 megabytes of the data that results in no message, nothing. It’s completely gibberish, random characters. That amount of information will have an identical energy content to 50 megabytes that can contain, maybe, the secret of the Kennedy assassination, or the secret of what is dark matter. The quality of information is not captured in this framework, in this information theory. It’s just a volume, a quantity measured in bits. And I do have a problem with this. I wish I would know how to improve the theory.”
The Problem With Valuing All Data Equally
One consequence of this theoretical framework is that all data is valued equally. When all data is seen as potentially valuable, then as data volumes increase, quality data becomes harder to identify because it is an increasingly smaller proportion of the overall data that’s out there.
It’s the same with energy and materials when it comes to how the technology industry has traditionally behaved. Energy and materials were seen as cheap and essentially infinite. Technology designers rarely consider the material impact of their product designs. Energy reduction is indeed taken into account for mobile users, but historically energy consumption was not seen as important.
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The Hidden Costs of the Tech Industry's Free Services
This thinking translates into the economics of free, which so pervades the current tech industry. The tech industry is seeking to hide the costs. “There is a problem here,” Vopson states. “Because there is a cost associated with storing this information and keeping the servers running. It’s stratospheric. It’s incredible how much energy it needs. So, I predict there will be a moment of reckoning where all these digital services and things … we will not be able to do these things for free for too long. There will be some sort of cost associated with storing information because the growth is so stratospheric, it’s unsustainable.”
The Need for a More Mindful Approach to Data and Information
Another consequence of the tech industry’s contempt for energy and materials is that it acts as a data pusher, trying to hook people on creating more photos, sending more emails, watching more Netflix and YouTube, etc., etc. We don’t have Data Centers. We do have Data Dumps. Data Landfills. Data Rubbish Heaps. Consequently, over 90% of the data we create has little or no value, but has a huge and growing cost to the environment. “I couldn’t agree more,” Vopson states. “It’s almost insane. We talk about climate change, or food crises or energy crises, but… we’re sleepwalking into this crisis. It’s almost invisible. At a committee meeting, in fact, there was an academic who said: ‘I thought we could upload infinite amounts to this server. That there are no limits.’ And that was a physics academic. How can you make a statement like that? Everything is finite on our planet. We have a finite quantity of everything.”
Related Article: It's Time We Got Smart About Energy Around Data Centers
More Wisdom, Less Junk Data
We must slow down. We must clean up after ourselves. We must view data not just as an object to be stored but as a means by which we transfer knowledge and wisdom. We need more wisdom, and a lot less junk data.
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