Engineering Metrics Platform

Designed and developed a comprehensive metrics and analytics platform that provides engineering teams with actionable insights to improve productivity, quality, and delivery performance.

Role

UX Research, UX Design

Team

Engineering Managers, Developers, Data Scientists, Developers, Product Managers, Compliance Specialists

Tools

ChatGPT, DXA Research, Qualtrics, Yammer, Figma, Dovetail, PowerPoint, Azure Data Explorer, Jira

Executive Summary

Engineering Analytics Platform

Led the research and design of a comprehensive engineering metrics platform that provides teams with actionable insights to improve productivity, quality, and delivery performance. This solution combines advanced analytics with intuitive visualizations to help engineering leaders make data-driven decisions.

40%
Faster Insights
86%
Time Savings
20K
Users

The Tension Points

Data Visibility Gap

Engineering teams lacked comprehensive visibility into their performance metrics, making it difficult to identify improvement opportunities and track progress over time.

Scattered Metrics Sources

Engineering data was scattered across multiple tools and systems, requiring manual aggregation and analysis that was time-consuming and error-prone.

Actionable Insights Need

Existing metrics tools provided raw data but lacked the context and insights needed to drive meaningful improvements in engineering practices and outcomes.

Strategic Approach

Research & Discovery

Analyzed existing dashboards and gathered feedback from engineering teams via interviews, Yammer discussions, and DXA research. Created a detailed breakdown of roles and levels, along with goals and challenges. Conducted surveys using Qualtrics to identify and prioritize metrics of interest. Used Dovetail to capture interview insights, organize highlights, tag sentiments, and classify persona data.

Design & Iteration

Created personas with ranked priorities and validated them through user feedback. Developed early Figma designs and dashboard components based on research. Prototyped dashboards showing metrics visualization on the right and persona priorities on the left. Ran usability tests to validate design concepts and improved layout based on results. Progressed designs through ordered Figma screens with refined color and component consistency.

Implementation & Results

New designs tested at an average comprehension time of 49 seconds—exceeding the usability benchmark of under 60 seconds. Created a report highlighting testing outcomes and shared findings across teams via PowerPoint. Final metrics were developed and implemented using Azure Data Explorer. Work was documented and tracked in Jira for cross-functional visibility and accountability.

Process & Methodology

Research & Discovery

Conducted comprehensive research through interviews, Yammer discussions, and DXA research. Used Qualtrics for surveys and Dovetail for organizing insights, creating detailed role breakdowns and persona classifications.

Design & Iteration

Developed personas with ranked priorities and created Figma prototypes. Conducted usability testing to validate design concepts and refined layouts based on user feedback and testing results.

Implementation & Results

Achieved 49-second comprehension time, exceeding usability benchmarks. Implemented final metrics using Azure Data Explorer and documented progress in Jira for cross-functional visibility.

Impact

Engineering Performance Transformation

The Engineering Metrics Platform fundamentally changed how teams understand and improve their performance by providing comprehensive, actionable insights that were previously inaccessible or scattered across multiple tools.

Before Metrics Platform

  • Time consuming and error-prone manual data analysis
  • Inconsistent data sources and formats
  • Manual data analysis - Teams relying on individual interpretation of scattered metrics
  • Limited cross-functional visibility - Findings and progress not systematically shared across teams

After Metrics Platform

  • Dashboard concepts tested with 49-second comprehension time
  • Consistent data sources and formats
  • Azure Data Explorer implementation for streamlined analysis
  • Comprehensive visibility with clear actionable insights

Data-Driven Engineering Culture

This project established new standards for data-driven decision-making in engineering, enabling teams to continuously improve their practices and outcomes based on objective metrics.

Visual Assets

Metrics Dashboard

Main metrics dashboard showing comprehensive engineering performance data

Performance Metrics

Detailed performance metrics and analytics visualization

Team Analytics

Team analytics and productivity insights dashboard

Quality Metrics

Quality metrics and defect tracking visualization

User Personas

User personas and requirements analysis for metrics platform

SPOG Analysis

Single Point of Truth (SPOG) analysis and data architecture

Tools & Technologies

Figma

Interface Design

Qualtrics

Survey Creation

Azure Data Explorer

Data Visualization

Dovetail

Research & Insights