To leverage historical project data, you must develop standards and execute discipline in capturing historical data. That’s because you will use historical data for benchmarking purposes (e.g., I want to compare my current project to a collection of historical projects) and conceptual estimating (e.g., I want to use the average of five historical projects as the basis for my conceptual budget).
If you don’t capture historical project attributes in a consistent manner, your analysis may be wrong before you start. For example, consider a common metric for buildings: cost per gross square foot. If your organization does not have a standard, consistent way to measure gross square feet, your analysis of this common metric will not be accurate. Likewise, if you are in the Oil & Gas market sector comparing installation costs, and some projects include shipping costs while others do not, the resulting analysis will not be correct.
When you prepare the buildup of a single estimate, discipline and consistency aren’t as critical compared to the analysis across many estimates. It’s a real challenge for project history and benchmarking systems. It’s a real challenge for those pursuing big data strategies. If you want to compare data across a large volume of construction projects, or if you want to leverage the trend analysis over time, you must standardize your means of data capture and measurement, and you must have the discipline to execute to those standards. Otherwise, you’re just wasting precious resources that will result in disappointment.
So how disciplined are your processes? And how mature are your standards?