The meaning of Big Data Analytics is the concept of processing large amounts of data. Usually this amount of data is seen in digital memory capacity. In big data analysis, this amount of memory can reach tens of terabytes for one-time analysis. If the analysis is carried out involving several stages, the amount of data will increase significantly.
From the discussion of the benefits and uses of the technology above, you now have a clearer picture of the role of Big Data Analytics. For those of you who want to learn about big data analysis better, it is highly recommended to read about case examples and their real-world applications. Here are some interesting concrete examples that you can try to dig into better for research:
- Amazon: Advertising Marketing and Buying Habits of Recommendations
This example of using Big Data Analytics is clear. Amazon is a large international marketplace company. They serve hundreds of thousands of customers every day. The data from the visitors can certainly be used.
Amazon uses this data to create patterns of visitors’ buying behavior. Amazon also retrieves user data from visitor accounts that are tied to Google’s search history. So it’s not surprising that Amazon could use buying habits data for ad recommendations. This ad will later link back to Amazon so that visitors return to shopping if they are interested in the product or price in the ads.
- General Electric looking for a Fuel-Efficient and Eco Friendly Aircraft Design Solution
General Electric is an aircraft manufacturer. To carry out design innovations, they must have strong data about aviation. The detailed flight analysis data is usually up to hundreds of gigabytes for a single flight across the Atlantic Ocean.
To improve data accuracy, General Electronics ensures that all their aircraft transmit the data. There are thousands of aircraft in operation made by this company now. The amount of data collected must be very large. From this big data, they easily make engine designs that are fuel efficient and more eco friendly every year.
- RapidSOS: Assisting Police by Providing Relevant Data In Emergencies
An example of Big Data Analytics that is more related to public safety is from RapidSOS. This company manages applications for emergency needs. When users can’t easily call the police, this app can send a signal to 911 automatically.
In addition to sending help signals and contacting the police, the application also sends accurate information about its users. This data is in the form of location track records, personal information and also various other aspects that make it easier for the police to carry out their work. Many cases are easier to solve thanks to additional data from the RapidSOS application.
- Netflix: Investment and Development of Data-Driven Broadcasting Programs
In the world of entertainment, Big Data is also used to make good customer service arrangements. Netflix tries to take the viewing data of their subscribers and create a map of the popular shows.
Of all the popular shows, the details are again taken the pattern. Starting from the creator of the show, friends of the show, actors who play a role there, duration of the show and various other data will be processed to show what shows are trending.
If you already have trend data, Netflix can invest in shows that have the potential to be popular. In addition, Netflix can create original shows with various popular aspects from the analysis results. So it’s not surprising that Netflix currently has a lot of hit shows and its collection of popular movies is growing fast.
Why Use Big Data Analytics?
Technology is now more capable. Data can be processed automatically by coding, retrieval can be via the internet and the analysis process is also more detailed thanks to AI. So don’t be surprised if many startup companies can do big data analysis more easily in this era.
Big data in today’s modern era usually involves human behavior by monitoring habits. For example, spending data, data on where people live, data on how long people work and even posting trends on social media can be data for analysis.
If the number of individuals whose data is taken is up to hundreds of millions of people, you can see patterns and a clear picture of the market. For businesses, this can be a source of inspiration and data to develop an accurate business strategy.
Simplify Mapping Supplier Network
Big Data Analytics is also important for multi-level production. Companies that have a production flow from processed raw materials to the market definitely need complex data for mapping supplier networks.
Each production layer will need a different supplier. Each supplier needs their own analysis to ensure the quality of raw materials. In addition, each production layer must be aligned so that the performance flow is more efficient. For this process, moving data will be difficult to observe and result in confusion in decision making.
If you use big data analysis with a cloud system, the information will be grouped and entered directly into the algorithm. From here, AI and machine learning will organize information to make it easier to process. An automated analysis system will be applied from there and generate user-friendly info. Without this big data technology, decisions for network suppliers could be contradictory and even more complicated.
Some Sectors Using Big Data Analytics
Big data analytics is needed by banks and securities to monitor financial markets through network activity monitors and natural language processing to reduce fraud. The Exchange and Trading Commission use big data analytics to ensure that no illegal trades take place by monitoring the stock market.
In the communications and media sector, big data analytics is used to deliver real-time event reporting around the world on multiple platforms, such as mobile, web and TV simultaneously. In addition, the music industry also uses big data to monitor the latest trends so that it can be used by “auto tuning software” to produce interesting songs.
One of the most interesting sectors that also use big data analytics is the sports sector. This sector uses big data analytics to understand the viewing patterns of various events in a given region and monitor the performance of individual players and teams with analytics. Examples of sporting events that use big data analytics are the cricket world cup, FIFA world cup, and Wimbledon.
The healthcare and healthcare sector uses big data analytics to collect public health data so that they can respond more quickly to a person’s health problem and identify the global spread of new types of viruses. A real example of the application of big data analytics is the analysis of the spread of the COVID-19 virus in the world. In the education sector, big data analytics is used to update and improve the literature in various fields.
Several universities in the world use big data analytics to monitor and track student and faculty performance and map student interests according to different subjects through attendance. Big data analytics is also used in the manufacturing sector to improve productivity and supply management so that companies can allocate production resources optimally and generate maximum profits.