What are Big Data and Big Data Analysis?
Big Data is a hot buzzword nowadays because of the sheer amount of data. As the word 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. Still, 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 data warehouse/ data lake or storage of data. But to those who can arrange those data, it is something through which you can make strategies for business plans.
Benefits of Big Data Management and Big Data analytics
Big Data Analytics enables us to make better decisions and prevent fraudulent activities. In the emerging market, with many online transactions and purchases, Big Data is fueling up everything. Whether it be from streaming music or videos or searching for something on the web, whatever we do on the web, it is helpful for a Big Data analyst to bring something out of it.
Traditional and modern tools store, process, and analyze data. The sources of such data are social media platforms, search engines, and networks because only Facebook generates more than 500 terabytes of data daily, including photos, videos, messages, and more. Now you can imagine the amount of information that there is everywhere.
All the data from social media platforms and networks combine to form Big Data. In the raw form, the data is of no use; therefore, processing and descriptive analytics and converted into useful information. In more technical terms, you need to take on the process of data mining and data management to use the information in a data warehouse. For this, many data engineers and those involved in data science use predictive analytical AI to predict future.
By doing this, they help their AI machines learn faster while reducing the work for the data engineers.
Why is Big Data Analysis critical?
Simply put, this is important from making strategies for business to preventing risks and losses in business. Here are a few importance:
Better and quicker decision making
With the help of big data descriptive analytics, a company can decide 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 this form of data management to decide on opening a new outlet.
Risk management
This form of management 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 uses this strategy to solve fraud and similar problems.
Improve customer experience
Companies can look at social media platforms like Twitter to monitor tweets to find out the customers’ experiences regarding their delays, journeys, and so on. When customers publicly address their issues and offer solutions, it can help a company to know about the problem and find a potential solution. We can take Delta Airlines as an example, looking at the customer’s tweets about their issues and suggested solutions.
Product development and innovation
Companies like Rolls-Royce use data management and data science to analyze the efficiency of engine designs and improvements. Other companies can also use this technology to develop their product and innovate.
How does Big Data Analysis work?
This long process 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 analysis’s reason and goal help determine so that evaluation is possible.
Phase two
The data identified would be helpful for the analysis. Identification of the data is made from a wide variety of data sources.
Phase three
The collected wide variety of data is then filtered to remove corrupt data.
Phase four
Data extraction is done after filtering the data. The data not compatible is transformed into a compatible form. This process can also be called Data Mining.
Phase five
In the data aggregation process, the data with the same fields across different datasets integrates into one place.
Phase six
After aggregation, now the process of data analysis starts. Analytical and statistical tools help with the evaluation of the data to discover useful information.
Phase seven
Big Data analysts can now produce graphic visualization of the descriptive analytics with the help of tools like Tableau, Power BI, and QlikView.
Phase Eight
The final step of the process is the final analysis of the result. The final results of the analysis are available to the business stakeholders so they can take action.
Conclusion
In essence, Big Data is a large chunk of data or a data lake that many data engineers use. But before people can use it, they need to go through the process of data mining through proper data science. The use of big data is not limited to data engineers. Software engineers use it to help the machine learn, along with software development, and web developers use it to develop websites as well.
We hope you found this helpful. And as always, thank you for reading till the end!