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In the Internet of Things age, one thing that is limitless is the sheer volume of data that flows through it. Big data became the buzzword of this decade, as almost every enterprise, big or small, got acquainted with it. With popularity comes the price, and the myths surrounding the Big Data. Off late, Big Data myths has created a lot of confusion in the tech world.

In present time, it would not be wrong to say that almost every consulting firm and software solution has created their own definition of Big Data. Well, we would definitely vote against any kind of definition, because there is no particular way to define this situation. For this reason, it is essential to highlight and burst the myths surrounding Big Data technology.

Myth no. 1: The Entire Data is Accessible

The world of technology has come a long way. Today, the amount of data exists in Exabytes, not even in petabytes. Think about it, in the 15th century, the amount of data consumed by any average individual in his/her entire life is equivalent to what an average person consumes today in one day!  Recent Big Data Analysis shows that it is practically impossible for any individual or firm/organization to store and access the entire data on any particular subject.

In fact, even the giant like Google cannot access the entire big data pool. The software used by Google fetches search results only from the Surface web, rather from the Deep web. If we compare the size of Surface web to Deep web, the latter is almost 25 times larger than the former. It means that, whenever a search is made in Google, the result obtained represents anywhere between 4 and 6 percent of the entire information available in the web world.

So next time, if any company assures you on crawling web and extracting 100% of the data, do not believe them.

Myth no.2: Entire Data is Needed

It is definitely true that data can help any company with taking informed decisions backed by insight. However, when it comes to the volume of data available, you definitely need to rethink. This is a common myth among some organizations that huge data volume leads to better Big Data Analysis. Companies that are successfully leveraging the potential of Big Data do realize that it is impossible to capture the entire data, and it is not needed as well.

In the world of Big Data, new sources of information pop up on a daily basis. Quite obviously, not each and every source of information is valuable. When it comes to information available through Big Data, there is a difference between plenty of data and good data. Without any doubt, there is no dearth of low-quality data in Big Data, and most of it can be misleading.

For example, incorrectly tagged photos and videos can create a lot of difference between what you see and the reality. Hence, in order to make the data look sensible, you need to throw away the useless and incorrect data.

Myth no. 3: Offers Certainty

Companies that offer Big Data Analytics services often claim to provide the glimpse of the future. The assurance comes from the vast amount of data can help them in deciphering the upcoming trends or future investments. However, it is absolutely a myth that Big Data helps yield certainty.

No matter the volume of Big Data you have, it cannot comprehend the external variables that are not in your control. It can help you get a glimpse of the present and near future, which in turn will bring down the level of uncertainty or associated risks with executing on a decision. Those who think that Real-Time Big Data is all about completely eliminating the aspect of uncertainty from business, they need to think again, think hard.

Companies do carry out analysis of petabytes of unorganized or unstructured data to get a better understanding of customer sentiments. Nevertheless, it should not be confused with the elimination of variability; you would still find it there. You will still have to deal with the ups and downs of business.

Myth no. 4: Big Data Isn’t Here to Stay Forever

It is a myth, when you think that granular or unstructured data is always better. Depending on the granular data is like predicting the outcome of a football match based on the individual performance of a player during the first quarter. If you crawl web data from websites on real time basis, it won’t be of great use unless you retrieve the old data as well. Well, web scraping is a technique to extract data from websites, and it is quite similar to that of web indexing. It is so popular that in 2013 it was figured out that web scraping accounted for 23 percent of the entire web traffic.

The noise that surrounds Big Data is quite huge. Since the data is in coarse form, which means loads of noise, it is necessary to refine the granular data before using it for analysis. Otherwise it will be challenging to glean out practical insights for future decision making.

In Conclusion

Those were some of the biggest myths that surround Big Data. In addition, there are f other Big Data myths that are absolutely baseless and false. A few believe that it is possible to create self-learning program or algorithm for Big Data, which is absolutely a myth.

Hence, it is crucial for the organizations looking forward to incorporate the power of Big Data in their business to double check the fact and then only move ahead with the implementation.

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