Why Dashboards Fail (And How to Build Data Like a Product)
ver 85% of dashboards fail. Learn the product-first data framework that drives adoption, trust, and measurable ROI.
Ali Z.
𝄪
CEO @ aztela
Table of Contents
Why Most Dashboards Fail
Over 85% of data and GenAI projects fail (McKinsey, 2025).
Not because of bad code. Not because Snowflake is slow. But because teams forget one thing:
They don’t treat their data work like a product.
Instead, they:
Build new ETLs.
Deploy another platform.
Track every metric under the sun.
All with no clear user, no defined decision, no measurable value.
Before you build another dashboard, pause. Here’s the 5-part framework we use to turn data into a real product—the kind that actually gets used, trusted, and drives ROI.
The 5-Part Framework to Build a Real Data Product
1. Identify Decision-Makers
Who will actually use it? Sales leaders, CS reps, finance execs? Don’t design for “everyone.”
2. Map Decision Points
What choice does the dashboard enable? Increase spend? Trigger a retention play? Adjust hiring plans?
3. Define Success Metrics
How will you measure adoption and ROI? Time saved? Revenue lift? Churn reduction?
4. Set Quality Thresholds
What’s the minimum acceptable level of freshness and accuracy? Real-time? Weekly? 95% coverage?
5. Establish Ownership
Who owns the logic, the UX, and the outcome? When something breaks, who fixes it?
Example: Churn-Risk Early Warning System
Product Goal: Reduce subscriber churn with early alerts.
Primary Users:
Marketing → trigger win-back offers.
CS → proactive account outreach.
Finance → incorporate churn into forecasts.
Data Signals (scored):
Engagement-gap index (60%) → 5+ day drop in usage.
Payment flags (25%) → declines, retry failures.
Dormancy (15%) → no logins in 7+ days.
Success Criteria:
↓ 15% churn in 6 months.
30% lift in win-back conversions.
95% of alerts acted on in 24h.
Now the team has clarity, accountability, and measurable impact.
Think Like a Product Team, Not a Service Team
Whether you’re building dashboards, copilots, or pipelines:
Start with business problems.
Ask execs what decisions they’re stuck on.
Define users, outcomes, and MVPs.
Score complexity vs value.
Ship in modular chunks, iterate weekly.
For more on prioritizing, see our data strategy framework.
Blunt Bottom Line
More dashboards, more tools, more pipelines—none of it matters without clarity, ownership, and measurable outcomes.
The teams that win treat data like a product: scoped, owned, iterated, and tied directly to ROI.
If you want to move from “dashboards no one trusts” to data products that drive decisions, Book a Data Strategy Assessment.
