One of the biggest challenges estimators face is defending their estimates. You may trust in your estimate, but how do you get others on board who might be unfamiliar with parametric estimating? Showing comparisons of your project to similar completed projects is one of the best methods of defending your choice of inputs and your final results. It’s also a method that nearly everyone understands. Unfortunately, relevant, high quality data to compare with isn’t always available.

There are 2 important trends related to this problem. First, high quality data is being protected more so than in the past. People recognize the value of good data, and they do their best to protect it from competitors and others who may use it against them. Second, lower quality data (i.e. with fewer details, unclear assumptions, or scrubbed clean of some useful information) is being constantly gathered and made widely available. This is especially true with government and defense projects, as the government seeks more transparency and knowledge sharing across organizations. Despite the lower quality, there is still much useful information that can be gleaned when used correctly. The first trend is a big part of the problem, but the second trend may hold the key to a solution.

High quality, recent data is a hot commodity in the world of cost estimation. Unison Cost Engineering gathers plenty of great data, but much of it comes with strings attached – we can use it to build models and update our algorithms, but sharing with other customers (who may be competitors) is a no-no. This helps ensure a highly accurate estimating model, but adds little in terms of defendability.

Publicly available data can prove useful to the cost estimation world in many ways:

  • It gives users more tools to validate and defend their estimates.
  • It can be used in conjunction with very high quality data to enhance accuracy and cover a wider range of projects.
  • It helps keep cost models up-to-date. We can actively look for trends in publicly available databases, identifying where our algorithms may need updating earlier and getting a head start on our research.