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What You Should Know When Handling Big Data

Data analytics conceptYou will need large amounts of information to make machine learning work. Sometimes, not all of that info is relevant to your project. But it is only through large sample sizes can you detect trends or anomalies. Here are some of the facts you should know when handling gigabytes of information.

Information has multiple uses

A full stack developer for big data knows that information is reusable. The info remains valid even after using it in a single project. For example, you can reuse old records to answer other current and future problems.

 Processed data is a real resource

As long as the numbers are accurate and current, people are less likely to commit a mistake in their decisions. This makes big data a valuable resource that you should safeguard. In addition, release it only when there is a real need for it.

The value is dependent on the usage

Information that remains relevant after using it for several projects is a valuable commodity – you only need to know how to harness it properly.

Focus on behavior, not cause

The information gathered is focused on the behavior rather than the cause. For example, the information you gathered can predict if a shopper decides to buy. But it can’t answer why they are buying the item.

Information is power, but it comes with risks

You could use the information to make predictions of future events. There is, however, a risk that the statistics you gathered are wrong. Even if they are accurate, people may draw the wrong conclusions. Keeping this in mind, make sure that the figures you gathered are corroborated by other sources.

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Quality is still better than quantity

You’re probably familiar with the term “GIGO” or garbage in, garbage out. No matter how large the figures you collect are, they are useless if most of them are irrelevant to your project. Make sure that the information is of good quality before taking it into consideration.

It’s complex

Multiple records can hide some complex information that end users could not easily detect. Failing to consider this will likely result in coming up with a wrong conclusion.

Big data is useful for machine learning and making informed predictions. Information is reusable, and it’s a real resource worth protecting. You should know the risks involved, so make sure you take the appropriate steps to use reliable big data management tools so that you will have the accurate information you need to make good business decisions.

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