Interactive BI
Self-service dashboards the team actually opens.
Self-service BI fails for one reason: the dashboards are built for the people who built them. Interactive BI done right starts from how the user thinks, makes the next question one click away, and stays fast on real data volumes.
What's inside
Dashboards designed for the users — not the builders.
- 01
User-first dashboard design
Interviews with the actual users. The dashboard answers their top three questions in three clicks or fewer. Built and iterated with them in the room.
- 02
Drill-through patterns
From summary to detail in one click. From a number to the deals behind it. From a trend to the segment driving it. The interaction model the team learns once.
- 03
Performance on real data
Pre-aggregations, materialized views, query optimization. The dashboard stays under 2 seconds at production scale, not just in the demo.
- 04
Embedded analytics
BI inside your product or customer portal. Same semantic layer, branded to fit, accessed without leaving your app.
How an engagement starts
Interview users. Prototype. Ship. Refine.
- 01
User interviews
30 minutes with each user role. What do they ask, in what order, how often, what slows them down today? Output is the dashboard spec.
- 02
Prototype in week one
First clickable version inside a week. Users see real data, give real feedback. Iterate from there, not from assumptions.
- 03
Ship + refine
Live by week three. Two more rounds of refinement in weeks four and six. By month two it's part of the workflow, not a side tab.
Free 30-min review. We pick the audience worth building for first.
Common questions
- Which BI tool?
- Metabase or Lightdash for SMB self-service. Hex for the analytics team's exploratory work. Looker if you're already on it. Match the tool to the audience, not the trend.
- What about Tableau / Power BI?
- I work in both, but for SMBs the modern alternatives are usually a better fit — lower license cost, faster setup, better self-service experience.
- Can users build their own?
- Yes — that's the goal. Once the semantic layer is right, business users can build new charts on top without breaking the definitions.
Also under Analytics
Business Intelligence
Dashboards that answer the question. And surface the next one.
Semantic Layer
One definition of every metric. Across teams. Across tools.
Automated Reporting
The Monday brief, written by the data. Not the analyst.
Agentic Data Intelligence
Agents that watch the metrics. And explain the spike before you ask.