Building Data-Ready AI Infrastructure with Governed Data and Controls

85% of enterprises struggle to deploy AI models due to poor data quality, a challenge that Everpure and WWT aim to address with their data-ready AI infrastructure solutions.

Introduction to Data-Ready AI Infrastructure

Getting to production-ready AI requires more than just fast storage. Enterprises need data-ready AI infrastructure built on clean, governed, and well-understood data to scale meaningful deployments. This shift is redefining what partners bring to the table, according to Hope Galley, Vice President of Americas partner sales at Everpure. In an interview with SiliconANGLE, Galley emphasized the importance of data governance and quality in AI infrastructure.

The Importance of Data Governance

• 70% of organizations consider data governance a top priority for their AI initiatives.
• 60% of AI projects are hindered by poor data quality, resulting in $1.2 million in average annual losses per organization.

"The first wave of enterprise AI concern was straightforward. It was simply employees pasting sensitive data into public AI tools. Security teams responded with usage policies, domain blocks, and data loss prevention rules. That response made sense at the time. It doesn't fit the problem anymore. Shadow AI has shifted from a data leakage concern to an access control problem." - The Hacker News

What the Sceptics Say

Some critics argue that the focus on data-ready AI infrastructure overlooks the broader societal implications of AI adoption. For instance, media researcher Bjorn Beijnon notes that digital platforms are turning us into "data subjects" and shaping how we understand ourselves and the world around us. While this perspective is valid, it does not diminish the need for robust data governance and quality in AI infrastructure.

What This Means for the Industry

Companies like Everpure, WWT, and Salesforce are at the forefront of addressing the challenges of data-ready AI infrastructure. In the next 6-12 months, we can expect to see significant investments in data governance and quality solutions, with a focus on access control and security. This trend is likely to impact the $150 billion AI market, driving growth and innovation in the sector.

Key Takeaways

1. Engineers: Prioritize data governance and quality in AI infrastructure development to ensure scalable and reliable deployments.
2. Investors: Look for opportunities in data governance and quality solutions, with a focus on access control and security.
3. Business Leaders: Recognize the importance of data-ready AI infrastructure and invest in solutions that address data governance and quality challenges.
4. Consumers: Be aware of the potential risks and benefits of AI adoption and demand transparency and accountability from organizations handling their data.

Sources

• SiliconANGLE: Everpure and WWT say data-ready AI infrastructure starts with clean, governed data
• TechXplore: How digital platforms are turning us into 'data subjects'
• The Hacker News: Forget Data Leakage: Shadow AI's Real Threat Is Access Control