Ignoring it can undermine the ‘prediction’ in predictive models.
- Credit providers that extend credit to the wrong people see default rates rise and margins collapse.
- Political polling organizations that rely on historic or overly optimistic voting patterns suffer a hit to their reputation when actual election returns diverge from their prediction.
- Marketers may ignore promising segments of prospective customers—and miss out on the incremental revenues they would deliver—by being too wedded to historic impressions of who buys or finds their products useful.
To avoid such outcomes, data analysts in these fields and others need to correct hidden or ingrained biases that skew models.