Big data, data science, and big data analysis are probably the hottest terms in the technology world today. But at the same time there are many misunderstandings and confusion about these terms, so people have started thinking from different directions, which are incorrect.
In this article, we will discuss the myths of these big data and their practical significance. Before looking at the myths of big data, you must have a little idea about big data, and data science.
What do big data, data science, and analytics mean?
Today, everything is connected to the internet. These things generate data every day, and businesses or organizations can use this data to obtain useful information about users, which is called big data. Data science and analytics can be defined as a process of managing and using this data to gain insights. There are many tools available to analyze these large data stores. As a result, all these terms are interrelated, revolutionizing the data world.
Big data is considered an oracle. The organization believes that without the support of big data, other enterprises will surpass them, and they will be the last match. As a result, thousands of myths surrounding big data have emerged. And, if you care too much about these myths, your overall business efficiency will be hindered.
Some of the most famous myths are discussed below.
- We have so much data, that small error doesn’t matter
IT executives believe that businesses now have to manage the large amount of data, so small data quality deficiencies are negligible due to the law of large numbers. According to the viewers, individual small errors would have no influence on the overall result in the data analysis.
However, the reality is a single error can have a smaller impact in relation to the total amount of data than before because the total amount of data is larger, the bottom line results in more errors due to the larger amount of data,” explains Ted Friedman, Vice President, and Analyst Gardener. “Therefore, the overall impact of poor data quality in relation to the overall data set remains the same. In addition, much of the data that companies use in connection with big data comes from outside or has an unknown structure and origin. That means data quality problems are even more likely. In the big data world, data quality is actually even more important. ”
- Bigdata technology makes data integration superfluous
Generally, it is believed that big data technology – and especially the ability to process information using a “schema-on-write” approach – will enable companies to read the same sources using multiple data models. Many believe that this flexibility enables users to determine how to interpret all data assets on demand. According to the prevailing opinion, data access is tailored to individual users.
In reality, most users of information clearly rely on “schema-on-write” scenarios. This is where the data is described and what is required, and an agreement has been made on the integrity of the data and how the data will affect the scenarios.
- With bigdata, everyone else is ahead of us
Interest in big data technology and services is at a record high. 73 percent of the companies surveyed by Gartner in 2014 are already investing in big data or plan to do so. However, many companies are still in a very early phase: only 13 percent of companies are already using their big data solutions.
How to get value from big data and how to get started are the two biggest challenges for companies. Many companies, Gartner explains, got stuck in the pilot phase because they didn’t link big data to business processes or specific use cases.
- Big data can predict everything about the future of business
Analytics can predict the trend utilizing Bigdata, yet it’s not only the data that drives the business. A business stands like rock on numerous components like the economy, HR, innovation, technology, etc. Therefore, only with a huge amount of data, you cannot predict anything when it comes to business.
Then here the question arises, what does big data do for data analysis? Well, predicting with the help of big data is all about extrapolating what will occur in the future by comparing the historical data. These data show what has happened previously. Regardless of whether you are analyzing real-time data or not, the result will be of some probability theory. Hence, it isn’t 100% correct. However, the predicted result will be more precise and accurate if the experimenting data is more relevant.
Yet the reality is, even the big data fact also fails to predict the accurate result, even if you have utilized or not utilize the sophisticated statistical analysis.
- Only big data is an autonomous technology
Big Data alone is not enough; other technologies are needed to secure them and make them more efficient. For example, strong security policies and safeguards protect data and prevent breaches. Mobility features provide employees with access to data from any device at any time, and cloud technology allows you to store, manage and back up your data. To effectively harness the power of “Big Data” and grow your business, you will need a suite of technologies working together. Your IT team must integrate these technologies, so that they behave like a transparent and efficient machine, offering faster processes and precise analyzes.
Concluding Remarks
Lastly, what matters most is the ability to bring the data altogether from various sources, in order to solve a business problem or tell a story about a customer. Therefore, the important thing is not to be overwhelmed by the myths and preconceptions that exist about Big Data.
Having a successful strategy in the analysis and treatment of company data is easier than you suppose. It only takes willingness, determination and desire to discover new information in the data.