7 tips for overcoming predictive analytics challenges

Predictive analytics is a powerful tool, but one thing it can't foresee are the issues users encounter while deploying and using the technology. Here's some help.

7 tips for overcoming predictive analytics challenges

W. Edwards Deming, a pioneer in applying statistical techniques and predictive analytics to business processes, said it best. "The big problems," he observed, "are where people don’t realize they have one in the first place."

When it comes to predictive analytics, "big problems" are often not apparent during planning and early deployment, becoming a concern only when the technology fails to deliver anticipated results over time.

Simon Crosby, CTO of SWIM.AI, an edge device analytics software developer, acknowledges that many common predictive analytics challenges arise due to poor planning and unrealistic expectations. "Predictive analytics is not a magic wand that you can wave over a complex system or organization to automatically improve it," he explains. "Have a good idea of the kind of insight you’re after and pick a toolset that allows you to quickly form hypotheses and dynamically inject analyses into the data stream, searching for correlations or anomalies, or predicting future performance."

Here are seven tips successful predictive analytics adopters use to avoid or resolve common project challenges.

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