12 data science mistakes to avoid

Well-managed analytics initiatives can reap organizational gold. But succumb to one of these common mistakes, and your data science operations can quickly go wrong.

12 data science mistakes to avoid
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AI, machine learning and analytics aren't just the latest buzzwords; organizations large and small are looking at AI tools and services in hopes of improving business processes, customer support and decision making with big data, predictive analytics and automated algorithmic systems. IDC predicts that 75 percent of enterprise and ISV developers will use AI or machine learning in at least one of their applications in 2018.

But expertise in data science isn’t nearly as widespread as the interest in using data to make decisions and improve results. If your business is just getting started with data science, here are some common mistakes that you’ll want to avoid making.

1. Assuming your data is ready to use — and all you need

You need to check both the quality and volume of the data you’ve collected and are planning to use. “The majority of your time, often 80 percent of your time, is going to be spent getting and cleaning data,” says Jonathan Ortiz, data scientist and knowledge engineer at data.world. “That’s assuming that you’re even tracking what you need to be tracking for a data scientist to do their work.”

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