Part 3/11:
Addressing Challenges in Data Management
A significant part of the discourse focused on existing challenges:
Pipeline Processing Delays: Traditional data pipelines are time-consuming, leading to latency issues that hinder real-time AI applications.
Data Quality and Reliability: Ensuring the integrity and accuracy of data remains a persistent concern, especially when feeding AI models that depend on high-quality input.
Lack of Governance: Without proper governance frameworks, managing data lineage, security, and compliance becomes complex.
The speaker emphasized that, despite over three decades of advancements, current platforms—built on machine learning, deep learning, and neural networks—must be reassessed in the context of agentic AI and generative models.