Big data, data science, and big data analytics are perhaps some of the hottest terms in today’s technology world. But, at the same time there is a lot of misunderstanding and confusion about those terms. So people start thinking in different directions which may not be correct.
In this article, my effort would be to discuss those big data myths and their actual meanings.
What are the meaning of big data, data science and analytics?
Before knowing about the myths of big data, you must have knowledge about the term big data, data science and analytics. Nowadays, everything is connected to the Internet. These things generate data every day, which can be utilized by businesses or organisations to get useful insights about the users, and this is called big data.
Data science and analytics can be defined as a process of managing and utilizing such data for getting insights. There are lot of tools available for analysis of these huge stores of data.
So, all these terms are connected with each other and revolutionize the world of data.
Why there is a myth about big data?
The newest innovation in the market is that of Big Data. Big Data is considered to be a deity of sorts. It is so important for businesses that all major houses believe that without the support of Big Data, other businesses will overtake them and they will be the last in the race. Thus, thousands of myths surrounding Big Data have appeared. And, if you concentrate a lot on these myths, then your overall business efficiency can be hampered.
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What are the big data myths?
There are many myths surrounding Big Data. Many people are either overly enthusiastic about adopting it, or fear about its adoption. These myths can seriously hamper your decision making skills. Some of the most well-known myths have been discussed below.
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Big data is hype
It is a very popular opinion of the masses that big data is over hyped. They believe that big data is actually nothing but the “same old data”, though in humongous amounts. They believe that there is nothing new in the concept, except that only big data scientists can read the information from the data. This and the additional costs included for technology makes it even more expensive. Thus, there is expected that big data won’t be used by the smaller businesses for a few years.
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All problems are big data problems
Businesses believe that any problem related to analytics is a big data problem. However, every thing isn’t a big data issue. For example, if you are trying to match some terabytes of information to a couple of fields according to a few conditions, it really isn’t a big data problem.
Big data can predict the future
This one is not completely a myth, but rather it is what some would call a half-truth. Correct use of big data can really give you some insights for prediction of the future, but these insights are based on historic data. This means that the insights will depend of the data which was analysed and the requirements or the questions of the user. Therefore, big data is not 100% reliable for future predictions.
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Big data is only for big organisations
Many companies believe that big data is only for big companies with big budgets. This is the reason why mostly big companies use big data solutions. Big data requires a lot of capital for technological setup and manpower. However, as the cost of these components will decrease, the power of these technologies will increase too, as more start-ups will be able to use such technologies. At the same time, we must remember that cloud computing is also making these technologies and platforms available to the smaller organizations at a lower cost. So, big data is become affordable to all types of organizations
Big data is better but messy
In big data, accuracy of the insights depends completely on the magnitude and reliability of the data being analysed. So, this would mean that if you analyse the wrong type of data, then your insights will be wrong too.
Large amounts of wrong data may also lead to bad decisions. Another example of this is messiness of the data, as analyzing big data isn’t very easy work. However, as analytical solutions are becoming more and more user-friendly, it’ll be easier to analyse the data.
So, the challenge is to make this messy data (big data) clean and then analyze it to get proper insights.
Big data technologies are matured
Actually, big data technologies are simply a network of different types of software with special features for computing large volume of data, and it evolves with time. Thus, big data technology isn’t completely matured as there are many flaws in these network/eco-system components. It still is not completely mature enough to analyse the recent flood of diverse data types. Big data will gradually mature, as more and more people start adopting it.
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Big data will replace existing data warehouses
This is a really dangerous myth. Big data is still not evolved enough to serve the needs of every type of data related issues. And, we must also remember that big data technologies/platforms are not a replacement for traditional data warehouses or RDBMS. Big data is for specific requirements and should not be applied everywhere. So, big data is not to replace current data warehouses, though it may meet some requirements of data warehouses in near future.
Big data strategy is only an IT responsibility
Having an IT section in a company really helps, as the IT department often sets up the various kinds of software and hardware required for big data. However, only a dedicated IT team isn’t enough to deploy a big data strategy. The big data strategy helps in taking better decisions, so the department in charge of the decision must carefully evaluate the solutions.
Hadoop is the ultimate solution for big data
Hadoop is often considered to be the best big data solution. However, there are many other alternatives to Hadoop. The solution actually depends upon your own requirements.
Big data is new
The term big data is new, and the data available today is also very new. But the concept of big data and its uses are very old. Many companies used big data before it was officially called “big data”. So, this myth is not entirely true.
Are these myths really important?
These big data myths are very obstructive and can result in bad business decisions. These myths can make you waste your precious resources, which would have otherwise been used to increase your businesses flexibility. These myths can even let you miss important opportunities for your business and lead to bad decisions. Thus, you should know the full truth, as half-truths can be really dangerous for your business.
What are the actual realities?
The actual realities are quite different from the myths. Often, these myths are only half-truths and rumours, which may be spread by ignorant people who don’t understand the concepts related to big data and data science. These myths can be harmful for your business or organisation, so the myths along with the realities are given in this article so that you don’t get confused about big data.
Big data is a relatively new concept. Its proper adoption can lead to better business decisions, more sales and customer satisfaction. However, like all new concepts, this too comes with a lot of untrue facts, which are mainly rumours spread by ignorant people. Believing in these rumours can hamper progress and lead to many other troubles. Thus, you must know how to tackle these rumours and ensure that your business works properly. For this reason, this article has discussed about the ten biggest myths related to big data, so that you can think sensibly.