Processing tools. The system should incorporate tools that assess impact, threshold levels, and scalability; detect outlier and weak signals; and aid with visualization.
System attributes. The system should be global, persistent, open, scalable and flexible, with consistent and simple terminology; it should also support multiple languages, include incentives for participation, and be easy to use.
Environmental considerations. Financial support, data protection, infrastructure support, and auditing and review processes must also be considered.
Building a persistent forecasting system can be a complex and daunting task. Such a system is a collection of technologies, people, and processes. The system being described is not a software-only system. It is important to understand both the power and the limits of current computer science and not try to force the computer to perform tasks that humans can perform better. Computers are great tools for raw data mining, automated data gathering (“spidering”), statistical computation, data management, quantitative analysis, and visualization. Humans are best at pattern recognition, natural language interpretation and processing, intuition, and qualitative analysis. A well-designed system leverages the best attributes of both human and machine processes.
The committee recommends that a persistent forecasting system be built in phases and over a number of years. Successful Web-based systems, for example, usually use a spiral development approach to gradually add complexity to a program until it reaches completion.
The committee outlined eight important steps for performing an effective persistent forecast for disruptive technologies. These steps include:
Define the goals of the mission by understanding key stakeholders’ objectives.
Determine the scope of the mission by ascertaining which people and resources are required to successfully put the system together, and meet mission objectives.
Select appropriate forecasting methodologies to meet the mission objectives given the requirements and the availability of data and resources. Develop and use methods to recognize key precursors to disruptions, identifying as many potential disruptive events as possible.
Gather information from key experts and information sources using ongoing information-gathering processes such as assigning metadata, assessing data sources, gathering historical reference data, assessing and mitigating biases, prioritizing signals, and applying processing and monitoring tools.
Prioritize forecast technologies by estimating their potential impact and proximity in order to determine which signals to track, necessary threshold levels, and optimal resource allocation methods.
Optimize the tools used to process, monitor, and report outliers, potential sources of surprise, weak signals, signposts, and changes in historical relationships, often in noisy information environments.
Develop resource allocation and decision-support tools that allow decision makers to track and optimize their reactions as the probabilities of potential disruptions change.
Assess, audit, provide feedback, and improve forecasts and forecasting methodologies.
This is the first of two reports on disruptive technology forecasting. Its goal is to help the reader understand current forecasting methodologies, the nature of disruptive technologies, and the characteristics of a persistent forecasting system for disruptive technology. In the second report, the committee plans to summarize the results of a workshop which will assemble leading experts on forecasting, system architecture, and visualization, and ask them to envision a system that meets the sponsor requirements while incorporating the desired attributes listed in this report.