In addition to software development, the greatest challenge in the cost estimating and economic analysis  disciplines has long been developing a viable method for estimating conceptual-stage technology and system development. The industry, government, and institutional sectors have lacked effective methods and  historical data with which to produce reliable cost and schedule estimates in this early-life-cycle phase. 

This uncertainty in estimating and forecasting can often lead to vastly inaccurate budgets and expected  economic performance. As a result, cost overruns have long been a significant issue and concern across  both government and industry, especially in highly technical environments traversed by the Department  of Defense, the intelligence community, and civil agencies such as NASA. A range of realities drive these  estimating difficulties, and major acquisition policy reforms and methodology changes over the years have  improved general outcomes. At the same time, other factors, such as rapid development and acquisition  strategies and techniques, have increased estimating challenges. Accurate technology and system development estimating has therefore remained elusive, except for in focused and mature technology areas where  extensive technical, performance, and cost data are available.

First, the nature of new or immature technologies inherently suggests a lack of truly analogous systems  from which to generate a basis of estimate. Traditional micro-parametric estimating models are also driven  by fundamental engineering, design, or performance criteria that are generally unavailable in early design  and development stages. Furthermore, these models typically focus on narrow technologies, functional  areas, or environments and are often based on limited historical project data. These challenges are further  exacerbated by the shortage of data due to the protected nature of development efforts, which often contain  proprietary intellectual property, sensitive cost data, or classified information.