Part of:

10 Big Myths About Big Data


With the rising popularity of big data, many misconceptions about it are out there. We try to debunk the falsities surrounding big data.

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 may start thinking in different directions, which may not be correct.

In this article, we’ll discuss those big data myths and their actual meanings.

What Do Big Data, Data Science and Analytics Really Mean?

Before looking at the myths of big data, you must understand 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 organizations 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 Are There Myths About Big Data?

Big data is considered to be a deity of sorts. It is so important for businesses that organizations believe that without the support of big data, other businesses will overtake them and they will be last in the race. Thus, thousands of myths surrounding big data have popped up. And, if you concentrate too much on these myths, then your overall business efficiency can be hampered.


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 are discussed below.

1. Big Data is Just Hype

It is a very popular opinion of the masses that big data is overhyped. They believe that big data is actually nothing but the “same old data,” just in humongous amounts. They believe that there is nothing new in the concept, except that only 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 the expectation that big data won’t be used by smaller businesses for a few years.

2. All Problems Can Be Solved With Big Data

Businesses believe that any problem related to analytics is a big data problem. However, not everything is 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.

3. 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 on the data which was analyzed and the requirements or the questions of the user. Therefore, big data is not 100% reliable for future predictions.

4. Big Data Is Only for Big Organizations

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 decrease, the power of these technologies will increase too, and more startups 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 becoming affordable to all types of organizations. (For more on big data and cloud computing, see Big Data in the Cloud – How Secure is Our Data?)

5. Big Data Is Better, But Messy

In big data, accuracy of the insights depends completely on the magnitude and reliability of the data being analyzed. So, this would mean that if you analyze 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 analyze the data.

So, the challenge is to make this messy data (big data) clean and then analyze it to get proper insights.

6. Big Data Technologies Are Matured

In actuality, big data technologies are simply a network of different types of software with special features for computing large volumes of data, and it evolves with time. Thus, big data technology isn’t completely matured, as there are many flaws in these network/ecosystem components. It still is not completely mature enough to analyze the recent flood of diverse data types. Big data will gradually mature, as more and more people start adopting it.

7. 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 issue. 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 meant to replace current data warehouses, though it may meet some requirements of data warehouses in the near future. (To learn about storing big data, see How Big Data Impacts Data Centers.)

8. Big Data Strategy Is Only an IT Responsibility

Having an IT department in a company really helps, as it often sets up the various kinds of software and hardware required for big data. However, a dedicated IT team alone isn’t enough to deploy a big data strategy. The big data strategy helps in making better decisions, so the department in charge of the decisions must carefully evaluate the solutions.

9. 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 best solution actually depends upon your own requirements.

10. 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 actually 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 cause you to waste your precious resources, which would have otherwise been used to increase your businesses flexibility. These myths can even cause you to 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.


Big data is a relatively new concept, and 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 rumors spread by ignorant people. Believing in these rumors can hamper progress and lead to many other problems. Thus, you must know how to tackle these rumors and ensure that your business works properly.


Related Reading

Related Terms

Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…