Before data apps, faster meant swapping spreadsheet slog for analytics at the speed of drag-and-drop. Smarter meant uncovering relationships and trends in static views. Business Intelligence (BI) — that bastion of historical reports and lagging metrics — solved the early 2000s problem of too much data, too little insight.

Data was declared the new currency, and a user-friendly, point-and-click software abstracted away the need to manually wrangle data, in theory. Today, AI is being grafted onto these same BI platforms, promising smarter recommendations and natural language queries, but the core model still assumes a passive consumption of pre-modeled insights. On the opposite spectrum is the data application.

An analytical data app pushes past presenting static data to initiate actions, allowing transactions that integrate with backend systems. Data apps range from prototypes, internal tools, to client-facing data products, and required code knowledge, although far less than full stack solutions