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The reality is, even the big data fails to predict the accurate result, even if you have utilized or not utilized sophisticated statistical analysis.
FREMONT, CA: 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.
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. 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.
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.
Analytics can predict the trend utilizing Big Data, 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.
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. The IT team must integrate these technologies, so that they behave like a transparent and efficient machine, offering faster processes and precise analyzes.
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.