114R-20Intermediate12 min read

Project Historical Database Development

A beginner's guide to project historical database development — systematically capturing the cost, schedule, and productivity data from completed projects so it can sharpen the estimates, norms, and benchmarks of every project to come. Built on AACE International RP 114R-20.

What a historical database is

This lesson closes a loop that's run through the whole platform. Reliable estimating depends on good norms (Planning & Scheduling); norms depend on good measurement (productivity); and factored estimates, location factors, and escalation all rest on historical data (Cost Estimating). The historical database is where all that captured data lives — the organization's accumulated memory.

Why build one

  • Better estimates — Real actuals — productivities, unit rates, factors — make future estimates accurate instead of guessed.
  • Credible benchmarks — Completed-project data is what you validate new estimates against (Cost Estimating Lesson 30).
  • Living norms & factors — Productivity norms, location factors, and escalation indices stay current as new data flows in.
  • Organizational learning — The company gets smarter with every project, instead of repeating the same estimating mistakes.

Building a usable database

Raw actuals aren't directly reusable — they have to be captured in a consistent structure and normalized so projects can be compared. The essentials:

Making it stick

Make capturing data a mandatory closeout deliverable, store it against the same code of accounts the estimates use (Lesson 2), normalize consistently, and treat the database as a maintained asset — not a one-off. The payoff compounds: the more projects feed it, the sharper every future estimate, norm, and benchmark becomes.

Nine things to remember

  1. A historical database captures actual data from completed projects for reuse.
  2. It closes the measure → norm → estimate loop across projects.
  3. It feeds better estimates, benchmarks, norms, and factors — the whole estimating chain.
  4. The data window closes at project end — capture it as a closeout deliverable.
  5. Capture → normalize → store → reuse is the pipeline.
  6. Normalize for date, location, and units — raw actuals aren't comparable.
  7. Store against the same code of accounts the estimates use.
  8. Make it a maintained asset — the payoff compounds with every project added.
  9. It's a quiet competitive advantage — organizations that keep it estimate better.

Glossary

Actuals
The real costs, quantities, and productivities recorded.
Benchmark
A reference value from comparable completed projects.
Closeout
Project completion — when data must be captured.
De-escalation
Adjusting past cost back to a base date.
Feedback loop
Using actuals to improve future estimates.
Historical database
A repository of actual data from completed projects.
Normalization
Adjusting data to a common date, location, and units.
Productivity norm
An expected rate derived from historical data.

Check your understanding

1A project historical database is valuable mainly for:
2Historical data is only reliable if it is: