Part 6/9:
The rapid pace of technological advancement influences how organizations generate, process, and analyze data. Innovations in cloud technology, artificial intelligence, and machine learning are enabling more sophisticated analytics and automation, but also introduce new complexities such as handling unstructured data, removing cloud-specific values, and maintaining model accuracy.
Synthetic data generation is seen as a promising avenue for overcoming data shortages, enhancing analytics models, and enabling more robust AI applications. Nonetheless, it introduces issues like security, model bias, and compliance that organizations must carefully address.