TABLE 7-1 Attributes of an Ideal Forecasting System




Data sources

Diversity of people and methods

Data should come from broad range of experts and participants from diverse countries, cultures, ages, levels of wealth, education, expertise, etc.


Diversity of sources

Data should be from a broad range of sources and formats, with particular attention to non-U.S. and non-English-speaking areas.



Key metadata should be captured, such as where, when, and how they were sourced as well as quality, measurements of interest, and resolution of data. Patterns can be distinguished by region, age of contributor, quality, etc.


Data liquidity, credibility, accuracy, frequency, source reliability

Should use multiple methods to ensure data accuracy, reliability, relevancy, timeliness, and frequency. Data should be characterized and stored in a way that makes them interchangeable/interoperable regardless of format or source from which they were gathered.


Baseline data

Collect historical, trend, and key reference data that can be used for comparison and analysis of new collections.


Diversity of qualitative data sources

Gather data using a variety of qualitative methods such as workshops, games, simulations, opinions, text mining, or results from other technology forecasts.


Diversity of quantitative data sources

Data should be sourced from a variety of data sets and types, including commercial and proprietary sources.

Forecasting methods

Multiple forecasting methodologies

System should utilize multiple forecasting methodologies as inputs to the system to reduce bias and to capture the widest range of possible forecast futures. Backcasting should be one of the processes used with a handful of initial future scenarios to begin the process of identifying key enablers, inhibitors, and drivers of potential disruptions, with particular attention to identifying measurements of interest, signposts, and tipping points. Vision-widening techniques (brainstorming, interviews, workshops, and open-source contributions) should be key components of the forecasting process.


Novel methods

System should consider incorporating novel methods such as ARG, virtual worlds, social networks, prediction markets, and simulations.



System utilizes qualitative forecasting methodologies.



System utilizes quantitative forecasting methodologies.

Forecasting team

Expert diversity and ongoing recruitment

Team should be diversified by country, culture, age, and technology disciplines, etc. Use culturally appropriate incentives to maintain required levels of participation.


Ongoing recruitment

Renew personnel and continually recruit new team members to ensure freshness and diversity of perspectives.


Public participation

Broad and diverse public participation is critical for capturing a broad range of views, signals, and forecasts. Application of culturally appropriate incentives and viral techniques to reach and maintain a critical mass of public participation.

Data output

Readily available

Data should be readily available, exportable, and easily disseminated beyond the system in commonly used formats.


Intuitive presentation

Output should be presented in a way that is informative and intuitive. Utilization of dashboards and advanced visualization tools.


Quantitative and qualitative

Raw quantitative and qualitative data and interpretive elements are readily available for further analysis.

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