Have you ever wondered what happens to the data you enter on the Internet? Have you ever had the feeling that the bank contacted you by phone?
Well, they use big data analytics to analyze the data of someone who is considered a potential for their business. Where do you get our data from?
Come and find out more about this big data analysis.
What is Big Data Analytics?
Before we dive into analytics, let’s first discuss what big data is. Big data is a special term for data that exceeds the processing capacity of conventional databases.
This is because the data is too large, moving too quickly and does not correspond to the structural capabilities of traditional database architectures.
Big data is processed by large companies, firms or organizations. These data are collected, processed and used by the company for specific purposes.
Well, the whole process of collecting, cleaning up, and analyzing big data is known as big data analytics. Big data analytics offers many advantages, especially for companies. One is to see new opportunities.
For example, when a sponsored post is shared on your Instagram timeline with preferences that match yours. Surely you will open it right away, right?
Well, this new opportunity is being seized by companies that use big data. Aside from benefiting from it, customers are also happy because there are recommendations that suit their needs and preferences.
So it can be said that big data analytics benefits two parties, the customer and the company. Then there are other benefits of big data, you know! What are you?
- Reduce production costs
- Accelerate decision making
- facilitate the development of new products according to the wishes and expectations of the target market
How big data analysis works
There is no special application that can capture big data on its own. This is done in several ways as well as a combination of several applications or software to be able to collect it all.
How does big data analytics work?
1. Machine learning
To collect data, an AI-based engine is used as a search engine. This machine quickly searches for and learns the data to be retrieved.
The machine automatically generates another model that can analyze larger, more complex and more precise data and deliver it even faster.
2. Data management
Before the data is provided to the company, the data must be checked and confirmed to the responsible authorities.
This is necessary so that the data used is high-quality data and is not artificially falsified data.
3. Data mining
Data mining technology enables data analysts to examine large amounts of data in order to find patterns in the data. With the results of this analysis, the company’s complex questions can be answered.
With data mining technology, analysts can input various data, mark important things and make data one of the solutions to influence decision making.
4. Hadoop
Is the name of one of the technologies used to store very large amounts of data. Hadoop itself is open source software that can be used to transfer data quickly.
5. In-memory analysis
By analyzing data using in-system storage technology, data analysts can quickly gain insights into the data.
This technology can quickly analyze, create new algorithms, create new models, and remove analyzes that are considered incorrect.
This technology should not only influence the decision-making of a company, but also create different scenarios as learning material.
6. Predictive analysis
This predictive technology uses data, statistical algorithms, and machine learning techniques to identify outcomes based on the historical data used.
Predictive analytics will provide predictions that will occur in the future, so companies are more certain of which decisions they will make later.
7. Text mining
This technology enables data analysts to analyze web articles, comment boxes, books, and other text-based parts of the web.
Typically, text mining is installed on blogs, twitter, polls, and even emails to find the hottest topics that business can build relationships with (potential) customers.
Steps to Implement Big Data Analytics
In Payumoney reporting, there are six steps in implementing big data analytics. These steps are usually referred to as The 6 Steps. What are you?
1. Data mining
Big data analysis focuses on two things, namely data mining and data extraction.
In simple terms, data extraction is a process of collecting data from web pages into a database. Data mining, on the other hand, is a process in which valuable knowledge is gained from databases.
2. Data collection
Big data doesn’t have an end button, so the data that goes into the database continues to grow as the world grows.
Not only is new data increasing, but data extraction must also continue to be performed to collect data changes that occur from each person.
The data extraction provides detailed information about each person and creates different scenarios.
3. Data storage
Saving data, especially large amounts of data, can certainly not be arbitrary.
Storage for good data storage provides an infrastructure with the latest data analysis engine. Not only that, good storage and plenty of storage space.
A lot of software is used to store large amounts of data. Some examples are Hadoop, Cloudera, and Talend.
4. Data cleansing
The data obtained from the big data analysis process is obtained entirely from the Internet. Of 100% of the data collected, 30-40% of the data may be inaccurate and not required by the company.
Hence, data cleansing with alias data cleansing is required to filter which data is needed or not. This way, data analysts don’t have to go through the hassle of analyzing and figuring out what data to use.
With this step, the data analyst immediately receives the data according to the company’s wishes, as it has been sorted automatically.
5. Data analysis
The largest part of big data analytics is of course data analysis. When analyzing the data, the data analyst goes into the habits of the audience and finds out which is most needed by the customer.
The analysis is the process of asking specific questions and finding the right answer. Qubole and Statwing are considered to be very powerful analysis tools for this process.
6. Data usage
Data is used by businesses, governments, government agencies, and even organizations for a variety of purposes and needs.
The question is, can everyone access big data and open data over the internet? Of course you can’t. To do this, you need a reliable data analyst and know how to process data.