What Is Big Data? Learn What You Need to Know

Think about the world we live in today. It is changing rapidly and continuously becoming smarter and more connected.  Every day we generate huge amounts of information or data and upload it to the internet, without any knowledge of its potential value.

This raw data often contains handy insights for businesses, and much more if you know where to look and how to use it. According to the research, the global big data and business analytics market annual revenue is expected to reach 274.3 billion US dollars in 2022. Because of poorly managed and poor quality data, the US economy is losing 3.1 trillion US dollars annually.

Business organisations, service providers, government agencies all use big data for producing value from the vast stream of data. The impact of big data is tremendous, and it cannot be ignored due to its huge potential benefits for every field.

Thus, we believe it is high time to learn about big data. If you are wondering, what is it all about? Please read the blog to know: what is big data? 4 V’s of big data, big data analytics and how it works.

What Is Big Data?

The data is growing exponentially with time and mostly generated from internet-connected devices. Big data is also data, but the size of the data is enormous. The collected huge data may be structured, unstructured, and semi-structured. The volume, velocity, variety, veracity and value of big data is what makes it so big and complex.

Big data tools or software can help in finding crucial information in several sectors. For instance, from the big data- environmentalists understand sustainability in the future, help healthcare professionals predict epidemics, help marketers target their campaigns more strategically, and much more. There are no traditional tools or systems that can store or process big data efficiently.

Types of Big Data

There are three types of Big Data. Following are the three types of Big Data:

• Structured
• Unstructured
• Semi-structured

Structured

Structured data are those data, which are stored, processed, and retrieved in the form of a fixed format. It refers to highly organised information or data, and you can use search engine algorithms to easily and efficiently store and get access from a structured database.

From the structured data, you can derive value out of big structured data using advanced computer science. An example of structured data is a company employee database.

Unstructured

Unstructured data refer to the data that are in an unknown form or lack any specific structure. Since the data or information is unorganised, it is complicated and time-consuming to process and analyse it.

The examples of unstructured data are the combination of simple text files, images, videos etc., email or google search.

Semi-structured

Semi-structured data is the third type of big data and pertains to both the forms of structured and unstructured data. When you see a datum as in structural form but it is not defined as structural data form or not fully organised. For example, data is represented in an XML file. 

<rec><name> John Adams </name><sex>Male</sex><age>35</age></rec>

<rec><name> Sara Lewis </name><sex>Female</sex><age>41</age></rec>

Now we have come to the end of the type of data, Let’s discuss the 4 V’s of Big Data and its characteristics.

What are the 4 V’s of Big Data?

Understanding and processing big data is not easy because of the huge amount and exponentially growing nature of big data. The big data experts broke it down into four parts to help you understand and make sense of big data.  These four segments are referred to as the 4 V’s of big data:

• Volume
• Variety
• Velocity
• Veracity.

Volume

The first V of big data and the main characteristic that makes data “big” is the sheer volume of data. There’s a lot of data out there, and it’s growing exponentially every second. The size of data plays a crucial role in determining the value or processing the data.

Whether the data is big data or not depends entirely on the volume of data. So, ‘volume’ is the first and foremost feature you should consider when working with big data.

Variety

The second V’s of big data refers to the different data types that are being collected from multiple sources. The nature of big data is fast-growing and extremely diverse- structured, semi-structured and unstructured.

Just a few decades ago, spreadsheets and databases were the only sources of data considered by most applications. There weren’t many tools or systems to analyse this raw data, aside from simple classification or finding a trend.

Nowadays, data is more diverse, and you can find these data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. These data are also being considered and used in analysing the big data. These different types of unstructured data create specific issues when you are storing, mining and analysing data.

Velocity

The term velocity essentially refers to the data generation speed. In a broader perspective, how fast the data is generated and analysed to meet the demands and also determines real value in the data.

The acceleration of big data can present exciting opportunities in different fields. From an exemplary data flow, you can quickly analyse it, and find the efficient result and uncover new realities.

Veracity

Veracity refers to the quality, availability, and trustworthiness of the data. Nowadays, it’s becoming harder to determine which data actually brings value because not all information is precise or consistent and with the exponential growth of big data.

In traditional business analytics, the data source is to be much smaller in both quantity and variety. But the organisation has more control over the data, and their veracity will be greater.

What are Big Data Analytics?

There are millions of data sources producing data at a very fast rate. The process of extracting meaningful insights from these vast sources is called Big Data analytics. Using a big data analytics process, you can find hidden patterns, customer preferences, unknown correlations, and market trends from the raw data.

Big Data analytics is fuelling everything we do online and provides a lot of advantages. For instance, it can be used for:

• Managing risk
• Product development and innovations
• Better decision making
• Preventing fraudulent activities
• Improve customer experience, among other things.

Types of Big Data Analytics

The following are four types of Big Data analytics, and the types of analytics described below:

• Descriptive analysis

The descriptive analysis summarises past data into a form that people can easily read. This helps create reports, graphs, and other visualisations, enabling companies to understand what happened in the past.

• Diagnostic analysis

The diagnostic analysis provides a more in-depth insight into a specific problem.  This analysis is a bit more complicated and helps to understand why a problem occurred in the first place. Sometimes this analysis uses AI or machine learning to find the answer.

• Predictive analysis

This type of analytics helps to make predictions of the future by using previous and current data., Predictive analytics uses data mining, AI, and machine learning to analyse previous and current data. Being able to provide an answer about the future brings a ton of values to the business. 

• Prescriptive analysis

The prescriptive analysis provides you with actual answers rather than a draw out conclusion. This analysis is extremely complex than other data analytics, so it is not yet widely used. You need to use a high level of machine learning to create these types of reports.

How does big data analytics work?

Big data analytics refers to the data professionals collecting, processing, cleaning and analysing the growing volumes of structured and unstructured data.

The following are an overview of the four steps of the data preparation process:

• Collecting the data

Data professionals collect data from a variety of different sources. Often, They gathered the data in a mixed form of semi-structured and unstructured data. Every organisation uses different type of data streams; some common data sources include:

• Web Server logs;
•  Internet clickstream data;
• Cloud applications;
• Social media content;
• Mobile applications;
• Machine data captured by sensors, which is connected to the Internet of Things (IoT). 
• Text from customer emails and survey responses.

• Processing the data. 

After collecting and storing the data in a data warehouse or data lake, data professionals must have to organise, configure and partition the data correctly for analytical queries or use.

• Cleaning the data for use.

Cleansing the data for quality. The next step is to scrub the processed data using various scripting tools or enterprise software. The data scientist looks for any errors or inconsistencies in the data, such as duplicates or formatting mistakes, and organising and tidying up the data.

• Analysing the data.

After the data is collected, processed and cleaned, now you have to analyse the data using Big Data Analytics software. This includes tools for:

• Data mining is finding the patterns and relationships from the data.  
• Predictive analytics, which helps build models to predict customer behaviour and other future developments.
• Machine learning uses algorithms to analyse large data sets.
• Deep learning is a more advanced offshoot of machine learning.
• Artificial intelligence (AI).
• Data visualisation tools.
• Text mining and Statistical analysis software.

What are the Big Data Analytics Tools you can use?

Here are some of the big data software for analysing data:

• Hadoop: This software helps in storing and analysing data.
• MongoDB: It is used on datasets that change frequently.
• Talend: This software is used for data integration and management.
• Cassandra: It is a distributed database used to handle chunks of data.
• Spark: This software is used for real-time processing and analysing large amounts of data.
• STORM:  It is an open-source real-time computational system.
• Kafka: It is a distributed streaming platform that is used for fault-tolerant storage.

Wrapping Up

The rise of big data has put customer-centricity at the forefront. Big data helps businesses to grow faster and in making computational decisions. This is not all of the usages of big data; it can be applied to every field.

Through the use of big data analytics, now it is possible to predict where future problems may occur. You can apply data-driven reasoning to resolve these issues before they occur. A few decades ago, it was beyond our reach and imagination.

If you are interested to know more about Big Data, check out our       which is designed for beginners and working professionals.