Clearing Up Misconceptions about Data Forecasting amid COVID-19
Michael Wu,
COVID-19 has disrupted the way companies do business, forcing many to reconsider how they use complex algorithms and models for forecasting a variety of tasks, from sales to budgets to when certain items come back in stock. These are unprecedented times, so it’s surprise that historical data is no longer a good predictor of the future.
Many have debated whether it’s best to tune and adjust existing models trained with historical data, or completely rebuild with a better understanding of today’s “new normal.” The answer will depend on how adaptive your models are and how fast they can learn from the recent data. Before making potentially business-altering decisions that could impact the customer experience, leaders need to take a step back and make sure they understand a common misconception: the difference between machine learning models and true AI models.
Understanding the Difference Between Static Machine Learning and True AI
It’s natural to want to draw causal conclusions from correlated events. Unsurprisingly, many blame COVID-19 for why “their forecasting models have stopped working.” In reality, the more likely cause has to do with the type of model used. More often than not, teams use machine learning models that do not learn constantly, as opposed to a true AI model, which can learn and refine its model continuously.