Kevin Liedtke
Bug Lifecycle Dashboard
An end-to-end dashboard revealing ticket bottlenecks across customer support and engineering, empowering leadership with the data to shift resources where they mattered most.
At a Glance
The move: replace anecdote-driven blame between support and engineering with one deterministic pipeline leadership actually trusted.
The Challenge: Bottlenecks Without Proof
Bug resolution was slow, and teams often blamed each other without shared evidence. Customer support was frequently labeled the bottleneck, but we lacked data to confirm or challenge that claim. We needed a unified view that showed where issues stalled across the full lifecycle, from initial customer report through engineering resolution, and one that allowed leadership to slice the data by org hierarchy (manager, director, VP).
My Role: Full-Stack Builder & Dashboard Designer
I owned the end-to-end build, from data access and modeling through the dashboard experience design. The goal was simple: make bottlenecks measurable and easy to explore at every level of the org.
C#
Queried bug systems and built the data bridge.
Python
Cleaned, normalized, and pre-processed data to keep the dashboard fast.
Power BI
Designed visualizations and interaction patterns for quick executive-level insight.
Approach: A Deterministic Pipeline for a Trustworthy View
The primary constraints were disjointed HR and bug data, limitations in Power BI for dynamic lists and deep grouping, and performance issues when relying on native transformations alone. I combined C# for issue querying, Python for preprocessing, and a Power BI dashboard designed for clarity and speed, keeping the model deterministic, reducing transformation overhead inside Power BI, and enabling slicing by team hierarchy without the dashboard becoming sluggish.
The dashboard needed to answer one question quickly: where is work actually getting stuck? I structured the experience around how leadership actually made decisions, by organization, ownership level, lifecycle stage, and elapsed time, so the data model could support both detailed investigation and executive-level scanning without becoming slow or overloaded.
Key tradeoffs:
What to precompute
Deciding what to calculate before the data reached Power BI versus what to leave interactive inside the report.
Detail vs. readability
Preserving enough detail for root-cause investigation while keeping the main views readable for leadership.
Stable model, visible problems
Turning inconsistent ticket data into a stable model without hiding the workflow problems the dashboard was meant to expose.
Full data pipeline from source systems through preprocessing to dashboard output. Click to open full size.
Results, Impact, and Influence
Trusted, ongoing visibility
Provided leadership with an ongoing, trusted view of bottlenecks across support and engineering.
Repeatable decisions
Enabled repeated, data-driven resource and prioritization decisions over time.
Bottleneck assumption disproved
Disproved the assumption that customer support was the primary bottleneck, refocusing attention on true delays.
The dashboard became a recurring reference point in leadership discussions rather than a one-time analysis.
Stakeholder Feedback
“Exactly the kind of visibility we need, our team is running efficiently, and now we can focus on where the real roadblocks are.”
“Great to see the data confirming what we've suspected. Let's share this with leadership to ensure we're prioritizing the right fixes.”
Why This Work Matters
This dashboard turned anecdotal frustration into defensible data. Leadership gained a single source of truth to guide prioritization with confidence, and the approach created a repeatable pattern for analyzing bottlenecks across other workflows over time.