Undeletable: Data in the new age of AI

Four thousand years ago, data stored on clay tablets tracked economic activity in the cradle of civilization. In the 17th century, the world’s first scientific journal contained works of Isaac Newton and other geniuses. In 1890, punch cards captured and quickly tabulated extensive U.S. Census data.

Human beings record what they value. That’s a constant in history. But the means of storing and accessing data improve over time. Advances in media – from manuscripts to bound books and eventually hard disk drives – have fundamentally driven social and economic progress by capturing, democratizing and monetizing information.

Today’s relentless digital transformation is yesterday’s Gutenberg or Netscape leap. What especially sets AI apart from information storage and utility in the past is the almost inconceivable amount of data it generates and processes.

The Library of Congress has 1.2 billion objects of digital content amounting to 31 petabytes of data. Put that into perspective. Analyst firm IDC forecasts that in 2029 alone, the world will generate that amount every two seconds for an annual total of nearly 527 zettabytes.

‘Why are we deleting all this data?’

The question of the day for organizations is how to approach so much data.

“I talk to leaders all over the world,” says Seagate CEO Dave Mosely. “Five years ago, they’d complain, ‘Why are we storing all this data? Why not just delete it?’ Now I hear, ‘Why are we deleting all this data? Help us store it.’”

Why the shift? Bob O’Donnell, president and chief analyst at TECHnalysis Research, explains: “One of the most unappreciated parts of the Generative AI revolution we’ve been experiencing is that organizations are now finally able to leverage vast amounts of data that, previously, had been basically ignored.”

Arthur Lewis, president of infrastructure solutions group with Dell Technologies, agrees: “There is absolutely a revolution that’s under way, and it’s largely driven by artificial intelligence.”

How valuable is this “revolution” in unlocking value? “We see four top use cases today with our customers – content creation, customer service, sales chat bot and coding assistance,” Lewis says.

What comes next?

“The analytical capabilities of GenAI tools are finally delivering on the promise of big data analytics and opening up the possibilities for new insights,” says O’Donnell.

“On top of that, given the insatiable need for more data in the AI model training process, we’ve also seen a dramatic rise in the creation of mountains of synthetic data that can be used to make AI models even more effective,” he says.

New insights and more effective AI models widen the path to innovation.

For example, research by McKinsey & Company points to significant opportunities for AI to accelerate R&D. This is critical to growth and prosperity because R&D productivity has been slowing down in many key sectors. Said another way, “each dollar spent on R&D has been buying less innovation over time.”

McKinsey believes AI will drive breakthroughs by producing a wider range of design options, enabling quicker evaluation and speeding up product development.

For example, AI tools can produce 3D, photorealistic renderings that surpass traditional computer-assisted design software in quantity and quality; design novel rocket engines; and help chemists create entirely new proteins.

The research from McKinsey finds that AI has the potential to as much as double the pace of R&D in some industries, with overall improvements resulting in annual economic value from $360 billion to $560 billion.

These numbers do not factor in the “downstream effects” or broader social benefits of innovation. McKinsey concludes that “if AI can help to bend the curves of innovation productivity, that would improve the quality and duration of life of future generations.”

That’s the potential value of AI – and all the data underpinning it.