free page hit counter

Big Data: Characteristics, How it Works, Importance for Business

Big data is a term to refer to a large amount of information that can later be utilized in making decisions. Especially in this all-digital era, many things require data as the main reference. Although the concept of big data may not be very familiar to the ear, it actually plays many important roles in almost all activities, you know. Then what are the other benefits and examples of the application of big data in our daily lives? Check out the full review below!

What is Big Data?

Big data is a term that expresses a large volume of data. But, you need to know, it is not the amount of data that is important, but the purpose of the person with the data. In short, big data is an advanced technology with larger capacities and more complex data sets.

This data set has such a wide scope that traditional data processing software will not be able to manage it. You can use this huge data capacity to solve business problems that you may not have been able to handle before.

Why Is Big Data Important?

big data isPhoto by George Morina from Pexels

Big data is one of the most in-demand niches in the development and complement of enterprise software today. Big data is a socio-technological phenomenon triggered by the rapid growth of information volumes.

The world of technology is undergoing very rapid and rapid changes, and big data is a solution in the automation and development of AI (Artificial Intelligence) technology. Google and other top-tier companies are already using machine learning processes to get more accurate precision in providing services.

As technologies around the world become more synchronous and interoperable, big data will become the core that connects things. With the ability to collect large volumes of international data efficiently, you will also be able to better understand and manage various phenomena.

Characteristics of Big Data

Big data has its own characteristics, you know. What are they approximately? The 5 characteristics of big data are as follows:

1. Volume

In this era of the industrial revolution 4.0, you can see exponential growth in data storage because data is now more than just text data. You will find data in the format of large videos, music, and images on your social media.

Currently, having Terabytes and Petabytes on a storage system in the company is considered reasonable. As databases grow, applications and architectures built to support data also need to be reevaluated..

2. Velocity

The growth of data and the popularity of social media have changed the way we view data. News and radio channels allow you to receive news faster. Now, people are vying to reply to a post on social media to update it with recent events.

On social media, sometimes messages sent a few hours or minutes ago are known as “old messages”. In essence, data regulation is now almost real time and the update time span has been reduced to a fraction of a second.

3. Variety

Data can now be saved in various formats. For example database, excel, csv, Access or other simple text files. However, sometimes, you need data that is not available in traditional formats, for example in the form of videos, SMS, pdfs or something that you may not have thought of.

It is the obligation of an organization to regulate these data. That would be very easy to do if they had data in the same format or addressed variations in those file formats with big data which is an advanced technology.

4. Value

Value means that big data has a high value if it is processed in the right way or can also be called a very meaningful data. For example, the biodata of an employee in one of the hosting service companies will not be of value for the purposes of analyzing sales predictions to customers. The data may be considered unimportant, but it can be of great value to something else. Data that has no value in various aspects will be filtered in the big data analysis system.

5. Veracity

The last characteristic of big data is Veracity, which is vulnerability in terms of accuracy and validity so that it requires depth to analyze it in order to produce the right decisions.

Big Data Concepts

The concept of big data as a whole consists of the integration, management, and analysis of data. The complete concept of big data is as follows:

1. Data integration

Big data brings together data from many different sources and applications. Traditional data integration mechanisms, such as ETL (extract, transform, and load) are no longer relevant when applied to the concept of big data.

It takes new strategies and technologies to analyze big data on a terabyte scale, or even petabytes. During the integration, you need to enter the data, process it, and ensure that the format is available in the form necessary for your business analysts.

2. Data management

Big data needs a storage area that can store data in any form. With big data, you can do the processing you want. Many people choose big data storage solutions such as cloud. The cloud is gradually gaining popularity as it supports your current computing requirements and allows you to use features as needed.

3. Data analysis

The practical value of big data will be felt when you analyze and act on your data. Because that’s where you’ll get new clarity with visual analysis from a variety of data sets.

You can explore the data more deeply to make new discoveries and share those findings with others. You can also build data models with machine learning and artificial intelligence.

Big Data Architecture

Big data architecture is an overall structure that represents the logical and physical system of big data itself, managed with the use of good storage technology, a server network that can be accessed at any time, and sophisticated algorithms.

There are several important points of illustration of big data architecture, namely:

  • Data source or data source. This data source comes from various sources such as personal data from potential online store consumers.
  • Data aggregator as a big data processing tool. This tool will receive the data and distribute it. There are 2 ways that can be used, namely:
  • Real time streaming processor (analyzing data is real time).
    Hadoop, which is a huge data storage area.
  • If the data is considered light, the stage after the real time streaming processor is that the data will be directly stored in the data store or data storage area.
  • But if the calculated data is very large, it will go through Hadoop. Data must also be processed with a non-real time
  • processor system. Then, after that, the new data can be stored in the data store.
  • The data stored on the data source will be accessible in a short time. However, with the condition that the data management runs well, because otherwise the data will be chaotic and less beneficial.

Big Data Functions

The presence of big data in life is certainly able to facilitate all user activities. What are they? Here’s a list of big data functions.

1. Find the cause of a problem in real time

First, the big data function is the inventor of real-time problems. The use of big data can also minimize failures. After analyzing it, the analysis results can be displayed directly or in real time.

2. Detect an anomaly in the business structure

Furthermore, the function of big data is to detect forms or processes of activities that deviate and stop due to errors from the technical and non-technical sides. In addition, big data will also plan several options to overcome these anomalies more quickly and precisely to help the company’s business activities.

3. Assist in making an appropriate decision

The last function of big data is to help with decision making. Nowadays big data is often used for intelligent technology systems such as IoT (internet of things) and AI (artificial intelligence), where the task is to provide and store the data /information needed in the development of a product.

Benefits of Big Data

Big data has been implemented in almost every industry such as:

1. Healthcare: Collect public health data for faster response to individual health problems and identify the global spread of new diseases;

2. Banking: Monitoring financial markets;

3. Education: Monitor and track student performance and map students’ interests in various subjects;

4. Retail: Analyze consumer behavior and supply chains and personalize their e-commerce for a better experience;

5. Insurance: Handling claims through predictive analytics;

6. Media and Entertainment: Keep up with the latest trends;

7. Transportation and Logistics: Route planning, monitoring, and traffic management; and

8. Manufacturing: Allocate production resources optimally.

Leave a Comment