Data quality is emerging as the critical factor in AI success. While data volume increased 32 times from 2010 to 2020, high-quality sources may be exhausted within years. Organizations face significant challenges with 97% of data in some companies being unstructured. Privacy concerns mount as nearly half of businesses input non-public information into AI tools. The companies that master data collection and organization will likely dominate the AI landscape.
While artificial intelligence (AI) often grabs headlines with its impressive capabilities, the true power behind these systems remains largely invisible to the public eye. Behind every AI system is data – vast amounts of it. Experts warn that the growing appetite for data to train AI is outpacing the supply of quality information.
The volume of data created globally has exploded, increasing 32 times from 2010 to 2020. Yet not all data is equal. AI models need clean, well-organized information to make accurate predictions. Some researchers worry that high-quality data sources could be exhausted within a few years.
Data explosion doesn’t guarantee AI success – quality matters more than quantity, and the best sources are running dry.
Access to data varies worldwide. The U.S. ranks eighth globally in data openness while China ranks 93rd. This gap shows how different approaches to information sharing affect AI development. Most AI leaders (92%) say changing company culture is their biggest barrier to better data use. Over 77% of small businesses are now utilizing some form of AI technology, despite facing data security concerns that limit their integration potential.
Most business information isn’t neatly organized. In one large insurance company, 97% of data is unstructured – meaning it exists as text, images, or videos that computers can’t easily process. Despite advanced tools, human experts still must prepare much of this data.
Privacy concerns are growing too. Nearly half of businesses have entered non-public information into AI tools, and 69% worry about potential damage to intellectual property rights. Responsible handling of data is essential to maintain AI credibility while avoiding potentially serious legal repercussions.
Organizations struggle to create data-driven cultures. The percentage of companies describing themselves as data-driven has dropped from 48% to 37% in recent years. Just adding generative AI tools isn’t enough – companies need broader cultural change.
Hardware for processing data presents another challenge. Production of specialized AI chips is concentrated in just a few countries, raising concerns about supply chains and global access.
As AI continues to evolve, the organizations that succeed won’t necessarily be those with the flashiest AI tools, but rather those who master the unglamorous work of collecting, organizing, and responsibly using quality data. The adoption of synthetic data generation offers a promising solution to overcome real-world data limitations in AI development.
