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Key takeaways
- Traditional BI is built ahead of time by a data team. Conversational analytics answers questions on demand for the person asking.
- Traditional BI wins on shared, governed metrics for many viewers. Conversational analytics wins on new questions and follow-ups.
- Governance varies most between conversational tools, so ask who sees what and where data is processed before you choose.
- Many teams use both: BI for monitoring, conversational analytics for investigating what the dashboards raise.
Traditional BI tools assume a modelled data warehouse, a data team and dashboards built ahead of time. Conversational analytics tools let people ask a question in plain English and get an answer from their data straight away. Neither approach replaces the other in every case.
What each approach is
- Traditional BI: dashboards and reports built by analysts on a modelled warehouse. Examples include Power BI, Tableau and Looker.
- Conversational analytics: questions answered on demand, from files or connected data. Examples include DashboardGPT and Julius AI.
Side-by-side comparison
| Traditional BI (e.g. Power BI, Tableau, Looker) | Conversational analytics (e.g. DashboardGPT, Julius AI) | |
|---|---|---|
| Who builds the analysis | Analysts and data team | The person asking the question |
| Setup before first answer | Days to weeks (modelling, permissions) | Minutes (upload a file) to a few days (connectors) |
| Best for | Monitoring known metrics across a company | Exploring new questions, following up on answers |
| Follow-up questions | New chart, new dashboard tile | Ask about the selected answer |
| Governance | Strong: central metrics, row-level security | Varies by tool; check workspace roles and audit logging |
| Data sources | Connected warehouse, typically modelled | Files and, on some plans, connected warehouses |
| Where answers live | Dashboards and reports | Chat thread or canvas, depending on the tool |
| Typical cost | Per user, often with separate data-team costs | Per user or free tier, check current pricing |
Where traditional BI wins
- Shared metrics that must match across teams, defined once and reused everywhere.
- Row-level security, so each viewer sees only their own region, team or customers.
- Scheduled reports for many viewers who read the same numbers every week.
- Large warehouses with established models that a data team already maintains.
Where conversational analytics wins
- One-off or exploratory questions that no dashboard was built to answer.
- Teams without a data team, where nobody has time to model data or build reports.
- Analysis someone wants to follow up on: break an answer down, filter it or ask why it changed, without starting over.
Governance questions to ask before you choose
- Who can see which data, and can you limit it by workspace or role?
- How are metric definitions shared, so two people asking the same question get the same number?
- Can you see how each answer was worked out?
- Where is your data processed, and what is sent to the AI model?
- Can you use your own AI provider key?
Which should you choose?
- Company-wide, governed metrics: traditional BI.
- Exploration and follow-ups without a data team: conversational analytics.
- Both: use BI for monitoring and conversational analytics for investigating what the dashboards raise.
How DashboardGPT fits
DashboardGPT is a conversational analytics workspace. Each answer lands on an infinite canvas as a chart, a metric or a written insight, and you can select any answer to ask a follow-up about it. Charts and the calculations behind them run on Apache Superset, and How it's calculated shows the steps behind every number. Managed AI is included, or you can bring your own AI key so model usage is billed by your provider. The Team plan is $40 per user per month, with workspace roles, shared canvases and Google Sheets, Google Analytics and Search Console connections.
The limits: warehouse connections are coming soon, so today it works with uploaded files, Google Sheets, Google Analytics and Search Console. Sharing and workspace roles start on Team, and SSO and audit logging are on Enterprise. If you already run Superset dashboards, see how a canvas compares with Superset dashboards. For a wider comparison of tools, read the best AI data analysis tools for non-technical teams.
Ready to try it on your own data? Sign up free, see pricing or contact us.