HKSM

Work performance data is the raw, factual observations and measurements captured while work is being performed, such as dates, quantities, hours, costs, and defect counts. It has not yet been analyzed or compared against plans; analysis turns it into work performance information.

Key Points

• Raw, unprocessed facts gathered during execution (e.g., times, counts, costs).
• Collected as close to real time as possible to support timely control.
• Not analyzed or evaluated; no variances, trends, or explanations yet.
• Feeds later steps that produce work performance information and reports.
• Accuracy, timeliness, and traceability are critical for reliable decisions.
• Often captured automatically by tools, with supplemental manual entries.

Purpose

Provide a factual, time-stamped record of what actually happened during project work. This enables monitoring and controlling by supplying the inputs needed for analysis, forecasting, and reporting.

Data Sources

• Schedule tools: actual start/finish, percent complete, remaining duration.
• Timekeeping systems: hours logged by resource and activity.
• Cost systems: committed amounts, actual costs, receipts.
• Quality tools: test results, inspection measurements, defect counts.
• Risk and issue logs: new risks flagged, issues raised, events occurred.
• Procurement and inventory systems: deliveries received, quantities, dates.
• Change intake: change requests submitted, IDs, timestamps.
• Operations/DevOps tools: builds, deployments, incidents, performance metrics.

How to Compile

• Define required metrics, data fields, IDs, units, and collection frequency.
• Automate capture from tools where possible; standardize manual forms for the rest.
• Time-stamp entries and tag them with source, activity/work package, and version.
• Validate for completeness and plausibility; resolve missing or conflicting values.
• Store in a controlled repository (e.g., project database or tool) with access rules.
• Do not compute variances or trends here; keep this set raw and auditable.

How to Use

• Transform data into information by comparing to baselines and thresholds.
• Update dashboards and status reports after analysis and contextualization.
• Feed forecasts (e.g., EAC, schedule projections) and trend analyses.
• Trigger control actions when indicators breach limits or show adverse patterns.
• Support root-cause analysis by tracing back to detailed, time-stamped facts.

Sample View

• 2025-03-10 09:12 — Activity A123 actual start recorded: 09:00.
• 2025-03-10 17:45 — Team logged 36 hours on Work Package WP-07.
• 2025-03-10 18:00 — Cost booked today for Contract C-04: USD 8,450.
• 2025-03-10 14:30 — Unit test results: 95 passed, 7 failed.
• 2025-03-10 15:10 — Defects found: 5 new, IDs D-221 to D-225.
• 2025-03-10 11:05 — Delivery received: 200 units of Component X, PO 5561.
• 2025-03-10 16:20 — Change request CR-017 submitted.

Interpretation Tips

• Confirm context (scope item, time window, units) before drawing conclusions.
• Distinguish data from information: if it includes analysis or variance, it is no longer raw.
• Look for completeness and consistency; missing or late data can bias results.
• Use aggregation and comparison outside this artifact to create actionable insights.
• Be cautious of vanity metrics; prioritize measures tied to objectives and baselines.
• Maintain traceability from summarized indicators back to these original entries.

PMP Example Question

A team updates actual start/finish times, hours worked, and the number of defects found each day. The project manager plans to create the weekly status report. What are these daily entries considered?

1. Work performance data
2. Work performance information
3. Work performance reports
4. Lessons learned

Correct Answer: A — Work performance data

Explanation: These are raw measurements collected during execution. Analysis and comparison to the plan turn them into information, and formatting for distribution creates reports.