Bad data leads to poor predictions, bias, flawed insights and unintended outcomes. To address these risks, companies invest heavily in data cleaning, validation and governance - an essential, time-consuming, complex process.
It's often possible to use other data sources to corroborate the metrics you're trying to measure. For example, I worked with a retailer who claimed their inventory data was unreliable - a major issue.
Sometimes, a dataset earns a bad reputation due to 'noisy outliers' that receive disproportionate attention. While noticeable, these errors often represent a small proportion of otherwise accurate data.
For analysts, prioritizing better measurement and understanding the business context behind their data is critical. That's why analysts must lead the efforts to optimize data for AI.
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