Forecasting, or the ability to predict important business figures, from production volume to turnover, creates a significant competitive asset, however, 2020 data may be distorted.<\/p>\n\n\n\n
Although the pandemic data is not inaccurate or incorrect, it is unusual because it is unlikely to give any indication of future trends.<\/p>\n\n\n\n
Forecasters should beware of data drift, which is essentially an unusual term for GIGO: Garbage In, Garbage Out.<\/p>\n\n\n\n
When this data is fed into previously accurate forecasting models, GIGO takes control and produces inaccurate results.<\/p>\n\n\n\n
The obvious solution is to repeat prediction algorithms and embed real-time machine learning or other technical improvements in your prediction processes that are less based on historical data.<\/p>\n\n\n\n
This can be a costly and time-consuming effort, especially for companies that do not have significant internal data science.<\/p>\n\n\n\n
Identify key processes, and the people who interact with them, where predictions such as sales and marketing, production planning, staffing, or forward-looking areas can play an important role, are good places to start your research.<\/p>\n\n\n\n
The ultimate goal is to identify the people who use forecasting data to do their job and influence their decision-making.<\/p>\n\n\n\n
Make sure that consumers of forecasting data and their management are aware of the risks associated with the use of forecasting tools.<\/p>\n\n\n\n
The results of these tools, which do not pass the snooping test, should be investigated and questioned.<\/p>\n\n\n\n
It is very encouraging for entrepreneurs to form a small team of data and forecasting experts who can help their colleagues revise forecasts, or run ad hoc runs that ignore anomalous data.<\/p>\n\n\n\n
In too many companies it is forbidden to “questioning the machine,” whether explicitly or unofficially.<\/p>\n\n\n\n
By enabling employees to use their own experience and knowledge, we ensure that the company is not misled by forecasts fed with data from extraordinarily odd years.<\/p>\n\n\n