Data virtualization plays a crucial role in the seamless integration of AI and analytics. It permits data from varied origins to exist and be handled as a holistic entity, helping business systems understand and interpret data from diverse sources conveniently. This process can be most aptly described as a bridge, facilitating seamless access, and display of data from different external source systems whenever required.
The significant role of data virtualization becomes evident in the functioning of AI and analytics. It's crucial to note that AI, as well as analytics, rely heavily on data. In fact, the quality and accuracy of the data can directly influence the efficiency of the AI tools and analytical systems. Therefore, it's integral to maintain a system that can readily unify and provide data as and when needed, which is exactly what data virtualization does.
Crucial to the operation of both AI and analytics, data virtualization prevents the possible obstruction and delay that can occur due to traditional data sourcing methods which often involve time-consuming processes such as data extraction, transformation, and loading (ETL). By allowing for on-demand data access without the necessity for physical data movement, data virtualization lets AI and analytics function in a more efficient and effective manner.
It is clear, thereby, that data virtualization is essentially unifying data for better, seamless, and smoother operations of AI and analytics. Not only does it eliminate unnecessary hurdles, but it also accelerates the entire process, leading to improved results and higher productivity. This makes data virtualization a key player in the world of AI and analytics.
Disclaimer: The above article was written with the assistance of AI. The original sources can be found on IBM Blog.
