Building Data-Ready AI Infrastructure with Clean Governed Data Sources

80% of AI projects fail due to poor data quality, highlighting the need for clean, governed data in AI infrastructure, according to recent findings from Everpure and WWT.

Introduction to Data-Ready AI Infrastructure

The concept of data-ready AI infrastructure is gaining traction, with 75% of enterprises planning to increase their AI investments in the next 2 years, as reported by SiliconANGLE. This shift towards data-ready AI infrastructure is driven by the need for clean, governed, and well-understood data to support meaningful AI deployments.

Benefits of Data-Ready AI Infrastructure

• Improved data quality: Data-ready AI infrastructure ensures that data is accurate, complete, and consistent, which is essential for reliable AI models.
• Increased efficiency: With data-ready AI infrastructure, data is readily available and easily accessible, reducing the time and effort required for data preparation.
• Enhanced collaboration: Data-ready AI infrastructure enables seamless collaboration between data scientists, engineers, and other stakeholders, facilitating the development of AI models.

What the Sceptics Say

Some experts argue that the focus on data-ready AI infrastructure is misplaced, as it may distract from the more pressing issue of developing effective AI algorithms. They claim that the quality of data is not the primary bottleneck in AI development, but rather the lack of skilled talent and insufficient computational resources.

What This Means for the Industry

The emphasis on data-ready AI infrastructure is expected to have significant implications for the industry, with major players like Salesforce already taking steps to address data quality issues, as seen in their recent response to the Klue app integration incident. In the next 6-12 months, we can expect to see a surge in investments in data governance and management tools, with companies like Everpure and WWT at the forefront of this trend.

Key Takeaways

1. Engineers: Focus on developing data governance and management tools to support data-ready AI infrastructure, with a emphasis on data quality and accessibility.
2. Investors: Invest in companies that prioritize data governance and management, such as Everpure and WWT, and expect significant returns in the next 2-5 years.
3. Business Leaders: Prioritize data-ready AI infrastructure in your organization, and allocate sufficient resources to support the development of clean, governed, and well-understood data.
4. Consumers: Be aware of the importance of data quality in AI development, and demand that companies prioritize data governance and management to ensure the development of reliable AI models.

Sources

• SiliconANGLE: Everpure and WWT say data-ready AI infrastructure starts with clean, governed data
• TechXplore: Wearable glove turns data into heat and touch for more personal insights
• The Hacker News: Salesforce Disables Klue App Integration After OAuth Token Abuse Exposes Customer Data
• BleepingComputer: Nintendo confirms data stolen in WebMD subsidiary cyberattack