Supports Advanced Analytics: Unstructured data enables advanced techniques like deep learning and neural networks to tackle problems that structured data alone cannot solve, such as image ...

Enterprises across every industry are increasingly recognizing the power of machine learning. It can turn any organization’s data into valuable insights—insights that have the potential to ...

Machine learning and AI projects often require integrating multiple data sources, such as structured and unstructured data. Integrating data from different databases or data formats can be complex ...

The differences between structured and unstructured data mean that traditional database systems and modern AI database systems handle information in different ways.

Key Takeaways The transition requires upskilling in Python, statistics, and machine learning.Practical experience with ...

The data lakehouse architecture is leading this change—particularly for machine learning (ML) and advanced analytics—by combining the strengths of both data lakes and data warehouses.

Now, with artificial intelligence (AI) and machine learning (ML) in the mix, there are different considerations for leaders using data analysis to help inform decisions and strategies.

AI chatbots have risen in popularity because they make it easy to find what you are looking for using a simple, conversational text prompt. Snowflake is now bringing that capability to company data ...

To get the most accurate AI responses, companies need to augment LLMs with proprietary, real-time structured and unstructured data from their own applications, data warehouses, and data lakes. An ...

Anomalo uses machine learning to replace traditional rules-based systems and automatically detect and alert teams about data quality issues across both structured and unstructured data.

With its latest release, Monte Carlo becomes the first data + AI observability platform to close this gap, providing AI-powered support for monitoring both structured and unstructured data types.

Monte Carlo said its move into unstructured data monitoring represents a key milestone in its broader mission to provide visibility across the full data and AI application lifecycle.