What is Big Data?
Big data is explained as huge amounts of data that traditional data storage or processing unit cannot store or process and it is a term that has become recently popular. Data in Petabytes i.e. 10^15 byte size can be considered as Big Data. Due to the large amount of data produced by human activities and machine operations, the data are so intricate and vast that they cannot be comprehended by humans or analyzed with a matching relational database. Big Data, according to Gartner means – “Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” However, this large amount of data offers useful statistics for organizations when assessed appropriately with modern equipment, so that it can help them to make better decisions to improve their business.
Big data has certain characteristics and types. These are what we are going to cover in this article. Let us start with the types of big data.
Types of Big Data
There are three types of big data – structured, unstructured, and semi-structured. Each type serves a particular purpose. Below given is the detailed explanation of each type.
- Structured data – Any data that have some pre-determined or fixed organizational properties or formats, and can be stored, analyzed, or processed is called structured data. It is easy to evaluate and sort. Because of its fixed format, each field is unique and can be retrieved individually or in combination with data from other fields. Thus, it allows the rapid collection of data from multiple locations. Over time, geniuses in computer science have had great success in developing technologies and extracting value from them, working with such fixed data.
- Unstructured data – unlike structured data, any data with no particular or pre-defined format is considered unstructured data. Unstructured data consists of information such as numerals, facts, and dates. In addition to being large in size, unstructured data poses various challenges when it comes to processing to derive value from it. Pictures we post on Instagram or Facebook and videos we watch on other platforms are examples of unstructured. Though a large amount of data is available with organizations, they have no idea of obtaining value from it as the data is in its raw form.
- Semi-structured data – semi-structured data is a mixture of structured and unstructured data, which indicates that it has the characteristics of both data forms. It consists of information that fails to have a specific structure and does not match the relational databases.
Characteristics of Big Data
Now we know the types of big data, let us move to the characteristics of big data. The characteristics of big data can be divided into 5 Vs – Volume, Variety, Velocity, Value, and Veracity.
- Volume – The main characteristic of any data is the size of the data which is measured in terms of exabytes, petabytes. The amount of data created and stored in a Big Data system is referred to as its Volume. These large amounts of data require the use of much more powerful and advanced processing technology, better than a standard laptop or desktop CPU. The best example for such a massive volume of data is found in Instagram or Twitter, where the audience spends a lot of time watching videos, posts, liking, commenting, etc. With this ever-exploding data, there is great potential for analysis, pattern discovery, and more. There is a huge possibility for evaluation and discovering a pattern with these ever-growing data.
- Variety – Variety includes data types that differ in format and the way it is structured and made ready for processing. If spreadsheets and databases were the main sources in the initial data, images, videos, PDFs, emails, etc., have become more prominent these days. Top media companies like Google, Pin interest, and others generate data that can be stored and analyzed later.
- Velocity – The rate at which data is gathered affects whether the way data is categorized as large data or general data. Most of this data should be accessed in real-time, in order to enable the systems to handle the speed and quantity of data generated. The processing speed of the data refers to the availability of more data than before, at the same time, it also indicates that the data processing speed should be much higher.
- Value – Another significant issue to be considered is Value. It is not just the amount of data we store or process that matters. It is about the value and reliability of the data and also about storing, processing, and evaluating data in order to obtain statistics.
- Veracity – This refers to the reliability and quality of the data. The value of big data cannot be questioned if it meets the highest quality and has reliable features. It is almost true in the case of working with data that is updated live. Hence, the authenticity of data needs to be verified and balanced at all levels of the process of Big Data collection and processing.
Suggested Read:
Processing Big Data using Apache Hadoop
3 Things you Didn’t know Big Data could Do
Importance of Data Mining and Predictive Analytics
Advantages of Big Data
Though Big Data has many benefits, here we are discussing some of the major important benefits, in the form of points.
- Businesses will be able to use outside intelligence to make decisions.
- Enhanced customer experience and satisfaction.
- Early risk detection associated with products and services.
- Improved functional efficiency.
Wrap up
We live in an advanced world where multiple technologies emerge every day. Technology is constantly attacking us in all areas of our lives. In recent decades, there has been a tremendous growth of data with the increased use of mobile phones, social networks, streaming videos, and the Internet of Things platform. As big data is generated on a large scale, it can become a significant asset for various companies and organizations, thus offering help to them to find new statistics and improve their businesses.
This article examines the basics of Big Data – definition, types, and features that any data scientist must be aware of.
Author: Mubarak Musthafa
Vice President of Technology & Services at ClaySys Technologies.