{"id":49473,"date":"2016-07-14T00:00:00","date_gmt":"2016-07-14T00:00:00","guid":{"rendered":"https:\/\/www.techopedia.com\/10-big-myths-about-big-data\/"},"modified":"2023-11-21T11:21:37","modified_gmt":"2023-11-21T11:21:37","slug":"10-big-myths-about-big-data","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/2\/31810\/trends\/big-data\/10-big-myths-about-big-data","title":{"rendered":"10 Big Myths About Big Data"},"content":{"rendered":"
Big data<\/a>, data science<\/a> and big data analytics<\/a> 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.<\/p>\n In this article, we’ll discuss those big data myths and their actual meanings.<\/p>\n 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.<\/p>\n Data science and analytics<\/a> 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.<\/p>\n So, all these terms are connected with each other and revolutionize the world of data.<\/p>\n 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.<\/p>\n 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.<\/p>\n It is a very popular opinion of the masses that big data is overhyped. They believe that big data is actually nothing but the \u201csame old data,\u201d just in humongous amounts. They believe that there is nothing new in the concept, except that only data scientists<\/a> 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\u2019t be used by smaller businesses for a few years.<\/p>\n 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\u2019t a big data problem.<\/p>\n 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.<\/p>\n 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<\/a> will be able to use such technologies. At the same time, we must remember that cloud computing<\/a> 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?<\/a>)<\/p>\n 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.<\/p>\n 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\u2019t very easy work. However, as analytical solutions are becoming more and more user-friendly, it\u2019ll be easier to analyze the data.<\/p>\n So, the challenge is to make this messy data (big data) clean and then analyze it to get proper insights.<\/p>\n 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\u2019t 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.<\/p>\n 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<\/a> or RDBMS<\/a>. 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<\/a>.)<\/p>\n 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\u2019t 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.<\/p>\nWhat Do Big Data, Data Science and Analytics Really Mean?<\/span><\/h2>\n
Why Are There Myths About Big Data?<\/span><\/h2>\n
What Are the Big Data Myths?<\/span><\/h2>\n
1. Big Data is Just Hype<\/span><\/h2>\n
2. All Problems Can Be Solved With Big Data<\/span><\/h2>\n
3. Big Data Can Predict the Future<\/span><\/h2>\n
4. Big Data Is Only for Big Organizations<\/span><\/h2>\n
5. Big Data Is Better, But Messy<\/span><\/h2>\n
6. Big Data Technologies Are Matured<\/span><\/h2>\n
7. Big Data Will Replace Existing Data Warehouses<\/span><\/h2>\n
8. Big Data Strategy Is Only an IT Responsibility<\/span><\/h2>\n
9. Hadoop Is the Ultimate Solution for Big Data<\/span><\/h2>\n