Data Quality Problems

Accurate forecasts require pertinent data that are current, complete, and free from error. Almost everyone has heard the familiar warning about the relation between data quality and forecast accuracy: "garbage in, garbage out." However, this statement is true in ways that are not immediately obvious. For example, if a manager wants to forecast demand for consumer or producer goods, it is often better to input incoming orders rather than shipments because shipments are sometimes subject to production delays. Similarly, the timing of order fulfillment is sometimes subject to delays in transit that are beyond the control of the shipping firm.

In addition to carefully considering the quality of data used to generate forecasts, the quantity of available data is also important. A general rule is: The more data that can be subject to analysis, the better. Some advanced forecasting software that works on desktop personal computers can function with as few as five data points. However, forecasts that result from such paltry bodies of data are often simplistic, if not trivial. Although the collection of large samples of data on market transactions can be expensive and tedious, the payoff in forecast accuracy can justify the effort.

If monthly data are seasonal in nature, it is important to have an extended time series to facilitate forecast accuracy. Most forecasting software programs used to monitor monthly activity require a minimum of 2 years of data (24 observations) to build a seasonally adjusted forecast model. Practically speaking, 2 years of monthly data are often not enough; 5 years of monthly data (60 observations) are typically necessary before a high level of monthly forecast accuracy can be achieved. Of course, most forecast software works with data of any periodicity, be it hourly, daily, weekly, monthly, or annual in nature. The ultimate consideration that must be addressed is whether the quantity and quality of data analyzed are sufficient to shed meaningful light on the forecast problem being addressed. The acid test is: Can useful forecasts be generated?

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