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Big Data & Data Science Misconceptions & Their Actual Reality
Big Data & Data Science are among the most debated aspects of technology in the present age of the digital world. Speaking of Big Data & Data Science, there are several myths & misconceptions that you will be seeing doing round on the internet.
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In this blog post, let’s discuss a few of the common myths in Big Data, which we hear often.
Big Data & Data Science Are Only Large Scale Enterprises-
The common misconception people have regarding Big Data & Data Science is that they are only meant for large scale enterprises that usually have a big budget and big teams. The reality is that both Big Data & Data Science initiatives are as valid for a small company as they are for the world’s largest companies.
When it comes to analyzing data, the only difference is that smaller enterprises are new start-ups. They would be adopting a different approach for analyzing and extracting the information from the data sets. The set of approaches may vary based on business objectives & goals.
More Data Is Better-
Most of the people are of the assumption that more data leads to better results. This is just a myth & it’s always reliable to invest in good data management than trying to collect more & more data.
In the quest for accurate insights, collecting enormous volumes of data wouldn’t simply justify the actions. Adding more data isn’t always the answer. We shouldn’t overlook the fact that better accuracy leads to less trustworthy models as there are high chances for the model to be over-optimized. As data increases, it becomes complex for Data Scientists to precisely clean & analyzes the data.
So in most cases, it’s always ideal to invest in a sound data management system than from investing in more data.