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In the Internet of Things, one thing that is limitless is the sheer volume of data that flows through it. With the passage of time, the amount of data is increasing at an exponential rate. No doubt it has lead to massive amount of data being generated, which not only includes the structured set, but the unstructured data sets as well. The whole thing can be summarized into one term, ‘Big Data’. In fact, I would certainly say that it is a buzzword that is being used quite often in the arena of Internet and IT. The amount of data here we are talking about sometimes become so large that it turns out to be difficult to process the same using conventional software and database techniques. However, it is also a truth that there are numerous myths that surround Big Data.
Big Data myths or the noise that surrounds this term has created a lot of confusion in the tech world. In present time, it would not be wrong to say that every consulting firm and software vendor has created their own definition of Big Data. Well, I 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 the myths surrounding Big Data technology.
Myth no. 1: The Entire Data is Accessible
The world of technology has come a long way, and the amount of data that exists in the present time is simply unfathomable. Today, the amount of data exists in Exabytes, not even in petabytes. To help you understand the situation, let us provide you an interesting fact. In the 15th century, the amount of data consumed by any average individual in his/her entire life is equivalent to what now an average person consumes in one day. Hence, as per the recent Big Data Analysis, it is practically impossible for any individual or firm/organization to store and access the entire data on any particular subject.
In fact, even Google cannot access all the information. 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 with that of Deep one, 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.
Even Big Data marketing companies find it tough to get their hands on the entire pool of information. It is not practical to access all the data that is generated on daily basis, based on the purchasing behavior of consumers. For this reason, if any company assures you providing 100% of the data, don’t believe them.
Myth no.2: Entire Data is Needed
It is definitely true that more amounts of data can help any company to better strengthen their marketing strategies. However, when it comes to the volume of data available, you definitely need to rethink. In order to make an informed decision for your business, there is absolutely no need to go for the entire data. This is a common myth among some organizations that more amount of Big Data Analysis will help them more. 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. Let’s take an example of email marketing. In this form of promotion, the behavior of potential customers with the emails they receive on daily basis can be analyzed. Based on the valuable insights, marketing companies can design their email marketing campaign. However, are all emails equally useful? Well, certainly not! Hence, it is better not to focus on the entire pool of data, which is practically impossible.
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 them 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 providing a glimpse of the future. They may say that 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 what amount of Big Data you have, it is not going to help you figure out absolutely everything including the external variables that are not in your control. It can just provide 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…hard.
Companies can definitely 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
If you heard someone saying or read it somewhere that Big Data technologies do not have any future, you definitely need to ignore the source of information. You certainly do not have to feel worried when investing your money on these technologies. You will not end up wasting your money in the long run, unless you under-utilize the potential of the same.
If truth to be told, in the next few years over 70% of the marketing campaign would carry the elements of Big Data Analytics. In the present time, Nate Silver is one of the most successful faces of Big Data. It was during the 2012 presidential election in U.S. when he predicted based on the Big Data that Barack Obama has 90% chances of winning. Without any doubt, Silver’s model turned out to be pretty much accurate. As a result, now everyone is looking forward to it.
With the passage of time, more and more companies are stepping into the business of data collection with the growing demand for data backed decisions and predictions in all company strategies. For this reason, there is a need for the organizations to understand and recognize the value of Big Data. Otherwise, it may get difficult for a company to stay ahead in the race of online marketing.
Myth no. 5: Granular Data is Better
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 data from websites on real time basis, it won’t be of great use unless you retrieve the old ones as well. Well, web scraping, also known as web data extraction 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, hence 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 by the senior leadership of a business.
To wrap up
Those were some of the biggest myths that surround Big Data. In addition, there are many more things related to Big Data that are absolutely baseless and false. Some people and companies believe that it is possible to create self-learning program or algorithm for Big Data, which is absolutely a myth. Few things have been overstated and some of the facts have been under rated regarding Big Data. 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 part.