Big Data Analysis What Is It, Why Is It Important, And How Does It Work?

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  • 22nd Mar, 2021

What are Big Data and Big Data Analysis?

Big Data a hot buzzword nowadays because of the amount of data that is being generated every second by people worldwide. As the word itself shows, It is a collection of a vast amount of data, and analyzing them is Big Data Analysis. To some people, it might not be a thing of interest but people who understand Big Data, know that a vast amount of data can extract meaningful insights such as market trends, customer preferences, hidden patterns, and unknown correlations.

To people who do not understand its value, the data is just a big chunk of information but to those who can arrange those data, it is something through which you can make strategies for business plans.
Big Data Analysis enables us to make better decisions and prevent fraudulent activities. In the emerging market of now with a lot of online transactions and purchases, Big Data is fueling up everything. Whether it be from streaming music or videos or it is searching for something on the web, whatever we do on the web, it is useful for a Big Data analyst to bring something out of it.

Through traditional and modern tools data that stored, processed, and analyzed. The sources of such data are social media platforms, search engines, and networks because only Facebook generates more than 500 terabytes of data every day which includes, photos, videos, messages, and more. Now you can imagine the amount of information that there is everywhere.
All the data from such social media platforms and networks combines up to form Big Data. In the raw form, the data of no use, therefore by processing, analyzing, and converted into useful information.

Why is Big Data Analysis important?

From making strategies for business to preventing risks and loss in business, Big Data Analysis is important. Here is a few importance of Big Data Analysis. Better and quicker decision making
With the help of big data analysis, a company can make plans on whether to open a new outlet or expand a business in a particular area. Different factors such as the population, demographics, accessibility of the location can determine the scale of the business, expected customers, and more. We can take the example of Starbucks using Big Data analysis to decide on opening a new outlet.

Risk management

Big Data Analysis helps to identify fraudulent activities and discrepancies. An organization can narrow down the list of suspects and find the root causes of the problem. A financial organization such as a bank use this strategy to solve problems of fraud and similar problems.

Improve customer experience

Companies can look at social media platforms like Twitter to monitor tweets to find out the experience of the customers regarding their delays, journeys, and so on. When customers are publicly addressing their issues and offering solutions it can help a company to know about the problem and find a potential solution for it. We can take Delta Airlines as an example which looks at the customer's tweets about their issues and suggested solutions.

Product development and innovation

There are companies like Rolls-Royce that use Big Data Analysis to analyze the efficiency of engine designs and improvements. Other such companies can also use Big Data analysis to develop their product and do innovations.

How does Big Data Analysis work?

Big Data Analysis is a long process that involves many steps to reach the final stage to make the data useful. There are 8 phases in Big Data Analysis.

Phase one:

First, the reason and goal for the analysis helps in determining so that evaluation is possible to do.

Phase two:

The data identified would be helpful for the analysis. Identification of the data is done from a broad variety of data sources.

Phase three:

The collected broad variety of data then filtered so that corrupt data can be removed.

Phase four:

After filtering the data, data extraction is done. With the help of a tool, the data not compatible is transformed into a compatible form.

Phase five:

The data aggregation process where the data with the same fields across different datasets are integrated into one place.

Phase six:

After aggregation, now the process of data analysis starts. Evaluation of the data done with the help of analytical and statistical tools so that useful information can be discovered.

Phase seven:

Big Data analysts can now produce graphic visualization of the analysis with the help of tools like Tableau, Power BI, and QlikView.

Phase eight:

The final step of the process where the final analysis of the result done. The final results of the analysis made available to the business stakeholders so that they can take action.

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