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Busting The Common Myths On Big Data & Data Science

In order to find a value from the data, enterprise doesn’t need to have a lot of data, huge investment or a bigger work team. To accurately interpret with the data so as to extract value from it all that one need to have is curiosity, analytical skills, and good, clean data.

Every day we will be hearing a lot of facts about Big Data & Data Science. All of those facts may be true but there do exists a lot of myths. In this blog post, let’s discus about the key myths on Big Data & Data Science which we hear often

Commonly Heard Big Data Myths

The most commonly heard myths in relation to Big Data are

  • Big Budget, Big Teams, Big Platforms
  • Machines Are Better Than Humans

Let’s take a look at each one of these myths

  • Big Budget, Big Teams, Big Platforms

Most of the companies are of the misconception that in order to extract & analyze the data it requires Big Budget, Big Teams & Big Platforms.  They are of the opinion that Big Data & analytics is meant for large scale companies which have huge budgets.

The actual truth is that getting started with Big Data doesn’t require much. As a matter of fact there are a lot of companies which are having very limited revenue & resources but they are still working on the most sophisticated data science pipelines and processes to extract insights from the data. And they are very much successful in achieving their business goals & objectives through Big Data & Data Science.

  • Machines Are Better Than Humans

Most of the people are of the view that by adopting to advanced approaches in Machine Learning and Artificial Intelligence and apply these approaches to their data and things will be automatically better. They are of the view that machines can do a lot more than what we humans can do.

The actual fact is that machines can only do what a human tells them to do. There will always be a need for humans in the equation, at least for the foreseeable future.

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