We live today in the Internet age; With each passing second, more and more data is created. Analyzing this huge data or Big Data as they are commonly known requires a lot of time and effort. Data scientists and data analysts must have the required knowledge and Big Data Skills Analyze Big Data to derive significant insights from them. They can then help their organizations understand the market and predict market trends, consumer buying behaviors and create products and services that effectively meet customer requirements.
What is Big Data?
Big data is a term given to a collection of large volumes of data that has grown over time. This data collected over time can provide information and insights that can be used for the benefit of businesses and other organizations. Big data is so complex and so vast that it is impossible to store and process them using traditional storage and computing methods. If this information exists in its raw form, is properly derived and analyzed, organizations around the world can use it to uniquely develop their business plan and generate profits.
Types of Big Data
The sheer volume and complexity of Big Data are not the only features that make Big Data processing so difficult. Information can not be easily extracted from Big Data in its raw form. First you need to turn them into easily structured data. There are three types of structure-based Big Data, these are:
- Built-in
- Not built
- Semi-built
The structure of Big Data is very vital. Depending on the data structure, data analysts can deduce the information. All data undergoes the ETL process (extraction, transformation, loading) before it can be analyzed. This process involves harvesting the data, designing it to be readable, and then storing it for later use. For each type of Big Data, the ETL process is different.
Structured data
This is the easiest form of Big Data to work with. It is organized and has defined parameters. Structured data is quantitative data such as age, contact, expenses, debit / credit card numbers, etc. Because structured data is already quantifiable, it is easier to process and analyze in the program. However, only 20% of the big data is built-in, the rest is semi-built-in data but mostly unstructured data.
Unstructured data
These are the disorganized data that are most difficult to process and analyze. Data analysts struggle with working with unstructured data, which is a big part of raw Big Data. Almost all information generated on the Internet around the world is mostly unstructured. It takes a long time to process and analyze. However, the information extracted from this form of data is very rewarding and useful for business enterprises.
Semi-built
As the name implies, this form of Big Data is partially and partially unstructured. Most often, semi-structured data is unstructured data with metadata attached to it. These can be data such as location, time, email address, etc. They are not as difficult to work with as unstructured data and can be used to identify patterns with the help of metadata. AI and machine learning can then process these patterns and analyze them to produce meaningful information.
Characteristics of Big Data
The characteristics of Big Data are defined by 5V – volume, speed, variety, truthfulness and value. Data scientists use Big Data 5V to identify the importance of the information that will be derived from them. If the data is not useful or customer-focused, then it is not advisable to process it. Thus, by understanding the characteristics of Big Data, business enterprises can save a lot of time and effort for processing Big Data.
Initially, there were only 3 Big Data characteristics known as 3V Big Data – volume, speed and variety. Over the years, two more have been added – value and truths to create Big Data’s 5V. These are described below:
volume
Volume refers to the amount of data. It describes the size of the data available in the raw form on the Internet that needs to be collected. When the data collected is large enough they are considered Big Data. This is the main feature of Big Data.
speed
It refers to the speed of data creation and its movement. Big data speed describes the flow of information through the Internet over a period of time. This characteristic can be useful to identify current market trends and predict future patterns in a particular period.
diverse
It describes the nature and variety of the data available. It represents all types of data including unstructured raw data, structured data as well as semi-structured data. These can be in the form of different sources that may or may not change over a period of time.
veracity
This is the quality and accuracy of the data collected. In Big Data there may be some inaccuracy or not be useful or there is missing information. By understanding the truths of Big Data, data analysts can judge the quality of the information collected.
value
The latest feature of Big Data describes whether the data collected can provide valuable information and add value to a business. This determines whether organizations can take advantage of the information extracted from Big Data to their advantage.
The Importance of Big Data
Big Data has benefited the medical and health care sectors to identify and analyze disease risk factors around the world. The current plague crisis in which the world finds itself is one of the main examples of big data exploitation. Energy industries and commercial organizations use information gathered through Big Data analytics to analyze requirements, manage customer expectations and predict market behavior.
Organizations that use Big Data can make informed decisions about their products and services. Big data analytics offers organizations a competitive edge over their business counterparts. The banking and manufacturing sectors use Big Data to create new products and services. Big Data Certificates It is a new and revolutionary field and has enormous potential to develop. Learning about tools and technology that will help analyze Big Data offers many benefits in terms of career growth and promotion ability.
