Data portals solved a critical problem: making datasets discoverable, structured, and reusable. But discovery is only half the journey. The next bottleneck is interaction.
Most users still need help turning a business question into a useful answer. Even with dashboards in place, teams often bounce between tools before they can act.
Queryless explores the next step: chat-first data access built on top of governed data foundations.
The Core Idea
Instead of asking users to enter a BI tool first, let them start in interfaces they already use:
- Telegram
- Slack
They ask a question in plain language. Queryless returns a clear answer in chat, with analytics built in (summary + table/chart output). For deeper work, users can open an on-demand report or dashboard view.
Dashboards are still valuable, but they become a second step, not the first.
Why This Fits the DataHub World
If your organization already invests in quality metadata, governance, and clear models, you can treat chat interfaces as a new interaction layer over those assets.
That means:
- discovery and governance remain in your portal stack
- question-answer interaction moves into everyday chat workflows
- analyst oversight remains a quality and trust control
In other words: this is not a replacement for portal architecture, but an extension of it.
Practical Workflow
- A user asks a data question in chat.
- Queryless generates a structured response (summary + table/chart).
- The response can be shared in the same conversation.
- If needed, a deeper report/dashboard view is generated on demand.
This reduces context switching and shortens time-to-first-answer for recurring business questions.
What To Watch During Adoption
Teams testing this model should define guardrails early:
- metric definition ownership
- acceptable confidence and quality thresholds
- escalation points for analyst review
- which question types stay chat-only vs. require deeper report workflows
The best results come from explicit governance, not improvisation.
Try It
Queryless is live in Telegram at @queryless_bot with public datasets for experimentation.
If your team runs a DataHub-style stack and wants to evaluate chat-first data access, this is a practical way to test where it adds value first.
Read the original product post: Talk to your data.