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Don’t Make Big Data Mistakes

BigDataMistakeWhile data mining provides a massive return on investment when it’s done correctly, there are a lot of pitfalls. In fact, many data mining projects either fail to launch or fail to provide value. recently covered some of the biggest reasons why data projects fail. Failing to set real goals for the way that data will be used tops the list of most commonly made mistakes. If you don’t ask the right questions, the data will ultimately be useless to you.

Attempting to set objectives that the data does not support is another common mistake. Data scientists can only work with the data that you’ve been gathering, not the data that you will gather or that you hope to gather.

Making sure the data is consistent and complete is another very important step. This will help you get the best possible results. Many data scientists spend most of their time preparing data to ensure that results are not skewed or subject to “confirmation bias,” where only the data that supports the “gut feelings” of decision makers is taken into consideration.

These problems and necessary steps demonstrate why practical training and targeted strategies are so necessary to any predictive analytics program. You can’t just rush into data mining. Dumping all of your data into any old program won’t produce the kinds of brilliant results that your company both needs and wants. Models must be carefully constructed if you’re going to get the most out of your data.

Are you ready to get started? Then sign up for TMA’s free webinar or register for predictive analytics training courses today!

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