Sample size always plays a role in data science, but there are certain instances where risk, time or expense will limit the size of your data: You can only launch a rocket once; you only have so much time to test a much-needed vaccine; your early-stage startup or B2B company only has a handful of customer data points to work with. And in these small data situations, I’ve found that companies either avoid data science altogether or they are using it incorrectly. One of the more common issues in applying AI is blindly relying on historical data for predicting future situations -- I call this “assuming the past is the future.”
A common example of this is when we assume the model that has worked so well for us in previous markets will work the same “magic” when we use it to launch products in a new market. The problem is, our new market -- the future -- is completely different from the past market, which leaves us with poor judgement, incorrect predictions, and lackluster business results.
Instead of assuming the past is the future, here are three ways to better apply AI to small data sets:
Continue reading: https://www.informationweek.com/ai-or-machine-learning/3-ways-to-better-apply-ai-to-small-data-sets
A common example of this is when we assume the model that has worked so well for us in previous markets will work the same “magic” when we use it to launch products in a new market. The problem is, our new market -- the future -- is completely different from the past market, which leaves us with poor judgement, incorrect predictions, and lackluster business results.
Instead of assuming the past is the future, here are three ways to better apply AI to small data sets:
Continue reading: https://www.informationweek.com/ai-or-machine-learning/3-ways-to-better-apply-ai-to-small-data-sets
