and the decision maker are not the same individual. If a forecast is to be successful, the decision maker needs to be provided with a product consistent with what was expected when the process was initiated. One of the best ways to assure that this happens is to involve the decision maker in the forecasting, so he or she is aware of the underlying assumptions and deliverables and feels ownership in the process.3
Data are the backbone of any forecast, and the most important characteristic of a data set is its credibility. Using credible data increases the probability that a forecast will be valuable and that better decisions will be made from it. Data can come in a variety of forms, but the data used in forecasting are of two types: statistical and expert opinion. The Vanstons have provided criteria for assessing both data types before they are used in a forecast (Vanston and Vanston, 2004).
For statistical data, the criteria are these:
Currency. Is the timeliness of the data consistent with the scope and type of forecast? Historical data are valuable for many types of forecasts, but care should be taken to ensure that the data are sufficiently current, particularly when forecasting in dynamic sectors such as information technology.
Completeness. Are the data complete enough for the forecaster(s) to consider all of the information relevant to an informed forecast?
Potential bias. Bias is common, and care must be taken to examine how data are generated and to understand what biases may exist. For instance, bias can be expected when gathering data presented by sources who have a specific interest in the way the data are interpreted (Dennis, 1987).
Gathering technique. The technique used to gather data can influence the content. For example, subtle changes in the wording of the questions in opinion polls may produce substantially different results.
Relevancy. Does a piece of data have an impact on the outcome of the forecast? If not, it should not be included.
For data derived from expert opinion, the criteria are these:
Qualifications of the experts. Experts should be carefully chosen to provide input to forecasts based on their demonstrated knowledge in an area relevant to the forecast. It should be noted that some of the best experts may not be those whose expertise or credentials are well advertised.
Bias. As do statistical data, opinions may also contain bias.
Balance. A range of expertise is necessary to provide different and, where appropriate, multidisciplinary and cross-cultural viewpoints.
Data used in a forecast should be scrutinized thoroughly. This scrutiny should not necessarily focus on accuracy, although that may be one of the criteria, but should aim to understand the relative strengths and weaknesses of the data using a structured evaluation process. As was already mentioned, it is not possible to ascertain whether a given forecast will result in good decisions. However, the likelihood that this will occur improves when decision makers are confident that a forecast is based on credible data that have been suitably vetted.
It is, unfortunately, possible to generate poor forecasts based on credible data. The data are an input to the forecast, and the conclusions drawn from them depend on the forecasting methodologies. In general, a given forecasting methodology is suited to a particular type of data and will output a particular type of result. To improve completeness and to avoid missing relevant information, it is best to generate forecasts using a range of methodologies and data.
Vanston offers some helpful discussion in this area (Vanston, 2003). He proposes that the forecast be arranged into five views of the future. One view posits that the future is a logical extension of the past. This is called an “extrapolation” and relies on techniques such as trend analyses and learning curves to generate forecasts. A contrasting view posits that the future is too complex to be adequately forecasted using statistical techniques, so it is likely