We had ideas. We built. We talked to customers. We changed.
How Research Shaped Our Product Strategy - From Features to Market Fit
When we started MyDecisive.ai (MDAI), we had dozens of potential use cases to address and even more features we could build. The question wasn't what we could do—it was what we should do first. Rather than guessing, we let our users tell us.
Let me tell you a story. We started out and built a visualization tool so that people could see all of the data running through their pipelines; where it was coming from; how it was being processed; where it was going to. A user could use the tool to see everything about their telemetry. We iterated, and refined. We showed it to potential users and found that…. they didn't care.
So who is our user?
First and foremost, before you can create a tool or experience, you need to know who your user is. We have identified three primary personas. 1) There are people who run MDAI clusters. 2) There are people who do OTEL in those MDAI clusters, and 3) There are people who just build services and apps and business functionality who just don't want to have to think about observability more than they have to.
Research has changed our path
We have conducted multiple interviews and surveys with technical professionals across DevOps, security, and development roles. Observability is a complicated field to say the least, and what we heard was eye-opening and fundamentally shifted our product strategy.
We made some initial guesses, but the results validated these guesses. Our users don't just want more dashboards—they want measurable results.
What we learned: Results over visualizations
Our MDAI Prioritization Survey of technical professionals revealed a clear pattern: teams are drowning in data but starving for actionable insights. The top-ranking features (a smaller number is a higher priority) weren't pretty visualizations or complex configurations—they were:
Here's a high-level flow:
- Efficacy metrics for filtration variables (Priority Score: 2.89)
- Global relationship configuration between services (Priority Score: 2.89)
- Cost analytics for logging impact (Priority Score: 3.44)
The message was unmistakable: teams need to prove their observability investments are working, not just see more data flowing through pipelines.
Universal needs vs. role-specific requirements
Another survey confirmed this insight while revealing something equally important: certain needs are universal. 100% of respondents wanted real-time data flow monitoring and pipeline state visualization. But 87.5% also demanded efficacy measurement—they need to know if their efforts are actually working.
This taught us a crucial lesson about market fit: build the universal foundation first, then layer on role-specific capabilities.
From Insights to Action: Our Three-Phase Strategy
The research gave us our roadmap:
Phase 1: Foundation (Immediate)
- Efficacy metrics framework
- Service relationship mapping
- Cost analytics core
