Natural Language Processing has quietly become one of the most transformative layers in the modern data stack. What started as simple keyword search has evolved into systems that understand context, intent, ambiguity, and domain-specific terminology. Today, business users can ask complex analytical questions in plain English – no SQL, no dashboards, no training required. This(Read More)
Natural Language Processing has quietly become one of the most transformative layers in the modern data stack. What started as simple keyword search has evolved into systems that understand context, intent, ambiguity, and domain-specific terminology. Today, business users can ask complex analytical questions in plain English – no SQL, no dashboards, no training required.
This shift raises a bigger question about the future:
If NLP continues to improve, do we eventually move beyond dashboards, filters, and menus altogether? Will conversational interfaces become the primary way people query data, trigger workflows, and make decisions?
Some believe NLP will democratize data more than any BI tool ever has, while others argue that language-based systems still lack precision, reliability, and governance.
