If artificial intelligence (AI) is the engine driving the 21st-century enterprise, then data is the fuel for AI initiatives. AI relies heavily on real-time and historical data to build predictive models and to make intelligent decisions.

However, when there are issues with the management, integrity, quality, protection and security of data, including the accuracy and consistency of the data used in AI initiatives, this can negatively impact AI and machine learning outcomes. This includes the movement of data that’s generated and stored in a multicloud environment, including private and hybrid clouds along with data generated on the edge. Data integrity – which includes the security, control and protection of data – is the foundation for successful and trustworthy AI and machine learning (ML) initiatives.

This is already causing problems with recent AI deployments. According to a recent Harvard Business Review study, almost half (47%) of newly created data records contain at least one critical error.

To help address these issues, the movement and scalability of data and models between cloud environments is critical since AI is compute-intensive for training and fine-tuning. Meanwhile, organizations need the ability to access and process data for AI initiatives quickly and efficiently while collecting data to update models periodically.

“The biggest challenge for customers (business tech executives) is the inablity to connect their private company data to an AI model,” said Luke Congdon, Senior Director of Product Management at Nutanix. “AI models are based on massive amounts of public information, but usually that public information knows nothing except for what’s on the external website about any particular company. So, safely getting their internal and proprietary data connected to a model is not straightforward.”

In this HMG Strategy research report, you’ll discover:

• How data integrity and quality can impact the success – or downfall – of AI and ML initiatives
• Recommendations for ensuring the quality of data in a multicloud environment
• Advice for protecting data that resides in and is transferred to public, private and hybrid cloud environments to prevent the unprotected utilization of private and proprietary data
• The business and operational benefits of ensuring the integrity of data in hybrid multicloud-fueled AI initiatives