This Work Performance Data Software Development workbook contains the raw, unanalyzed observations and measurements collected during ProjectAdm's 28 sprints — the actual percent complete values, actual cost figures, defect counts, and story point tallies that Eduardo Montes gathered from the team's tools (Jira, GitHub, time-tracking system, and QA test suite) before any analysis or interpretation was applied. Work Performance Data is the input to the analysis that produces Work Performance Information and Work Performance Reports.

What Is Work Performance Data?

Work Performance Data is a PMBOK 8 output in the Measurement Performance Domain representing the raw observations and measurements identified during activities being performed to carry out project work. Examples include percent of work physically completed, quality and technical performance measures, start and finish dates of schedule activities, number of change requests, number of defects, actual costs incurred, and actual durations. PMBOK 8 distinguishes carefully between Work Performance Data (raw facts), Work Performance Information (analyzed data with context), and Work Performance Reports (formatted communications for decision-making). This distinction matters because raw data can be misleading without analysis — a defect count of 47 in Sprint 8 is meaningless without knowing the baseline defect rate, the test coverage, and the scope of the sprint.

What This Work Performance Data Software Development Includes

The ProjectAdm Work Performance Data workbook contains six data-collection worksheets, one per measurement category:

• Cost Data — Actual cost (AC) per sprint and cumulative; labor hours by team member × hourly rate; infrastructure costs (AWS, MariaDB hosting); license costs; total AC by sprint compared to Planned Value (PV) from the Cost Baseline
• Schedule Data — Actual start and finish dates for all 214 activities; percent complete per activity at each sprint status date; activities started but not finished (in-progress count); activities completed ahead of schedule vs. behind schedule
• Scope Data — User stories completed per sprint (raw count); story points completed per sprint; backlog item counts (total, In Sprint, Done, Rejected) at each sprint end; change requests received (raw count before CCB review)
• Quality Data — Defects found per sprint by category (functional, UI, performance, security); defects resolved per sprint; open defect count at sprint end; test cases executed; test pass rate (%); PHPUnit test coverage (%) per sprint
• Risk Data — Risk trigger events observed per sprint; issues logged (ISS-001 through ISS-006 dates and descriptions); risk owner responses initiated; residual risk scores after response implementation
• Resource Data — Actual hours worked per team member per sprint; overtime hours; training hours; sick/leave days; resource utilization rate (%)

How Eduardo Montes Used This Work Performance Data Software Development

Eduardo Montes collected Work Performance Data from four automated sources every Friday: Jira (story point completion, backlog counts), GitHub (commit activity, PR cycle time, code coverage reports from PHPUnit), the AWS billing dashboard (infrastructure actual costs), and the team time-tracking tool (labor hours by person). The raw data was imported into this workbook without modification — a discipline that preserved data integrity. The actual analysis (calculating SPI, CPI, EV, SV, CV) happened in the Work Performance Information workbook, not here. This separation meant that if Eduardo or anyone else questioned a metric, they could trace it back to the raw source data and verify the calculation independently. ISS-002's impact is clearly visible in the raw schedule data: Sprint 5 shows only 31 story points completed vs. 38 committed — 3 sprint days lost to database investigation before the root cause was identified.

How to Use This Work Performance Data Software Development Document

When building your own Work Performance Data Software Development collection system, automate the data collection wherever possible and keep raw data separate from calculated metrics. The ProjectAdm approach — automated import from Jira/GitHub/AWS into this raw data workbook, then separate calculation in the Work Performance Information workbook — took 2 hours to set up in Sprint 1 and saved Eduardo approximately 1.5 hours of manual data entry per sprint for 28 sprints (42 hours total). More importantly, it eliminated transcription errors and made the measurement system trustworthy: the team knew the numbers came from the source systems, not from manual entry that could contain mistakes.

Ready to create your own work performance data log? Download the blank Work Performance Data Template (PMBOK 8).

• Download the Work Performance Data Template — PMBOK 8 (blank, ready to use)
• Read the full guide: Work Performance Data in PMBOK 8

Want to go deeper? The PMBOK Guide 8th Edition is the definitive reference for modern project management. Get your copy and use it alongside these examples to build a solid, practical understanding of every performance domain.

Format: Microsoft Excel (.xlsx) | Project: Software Development (SaaS Platform) | PMBOK Edition: 8th (2025) | Domain: Measurement