What is Big Data? Types, Trends, & Future Explained

Since Big Data first appeared in the digital age, it has been in the limelight. But, even today, only a few people understand Big Data. Big data is proving its benefit to organizations of all sizes and in a diverse range of industries. Big Data’s idea, strategy and use cases have evolved substantially across several sectors. Understanding Big data has become essential for firms that want to understand their customers and operational possibilities better. According to DataProt, 97.2% of the largest firms in the world are increasingly investing in big data and AI. The importance of Big Data has increased more than ever because of developments like cloud, edge computing, streaming, and the Internet of Things (IoT).

Table of Contents 

1) Let's understand - What is Big Data?

   a) Characteristics of Big Data

2) Big Data Analytics 

   a) Benefits of Big Data Analysis 

3) Coming to What are the Types of Big Data 

   a) What is Structured Data in Big Data? 

   b) What is Unstructured Data in Big Data? 

   c) What is Semi-Structured Data in Big Data? 

4) Most popular Big Data Trends

5) Big Data’s Future Potential 

6) Conclusion



 

Let's understand - What is Big Data? 

Big Data is an enormous amount that grows exponentially, generally kept in the cloud or data centres. This extensive amount of Big Data is gathered from both new and old sources. It has become challenging to manage this data because of its large volume. When it comes to Big Data, conventional Data Processing software is fruitless today. Therefore advanced technologies are being employed worldwide to process Big Data. Big data is collected, and in-depth mining is performed in advanced analytics like Machine learning and predictive modelling.   

Some of the biggest challenges of managing Big Data include gathering the data, analysing them, sharing, updating it from time to time and ensuring their privacy. Companies worldwide are spending huge sums on hiring talents to manage big data and bring its value to the organisation. According to Glassdoor, you can earn an average annual salary of £83,747 as Big Data Architect.
 

Characteristics of Big Data 

Big Data is frequently explained by its characteristics, commonly known as the three Vs: 

A) Volume – The volume of Big Data is vital, as it can be tens of gigabytes for some organisations, while it may be several hundreds of petabytes for some others. The Volume of Data is increasing with advancements in technology and cloud storage facilities. Every day, internet users worldwide generate 2.5 quintillion bytes of data. 

B) Variety – Variety denotes the wide range of data that are accessible and stored across environments today. Thanks to Big Data, in today’s day and age data, arrives in an unstructured and semi-structured manner, while in traditional times data types were structured and organised neatly in a database. In the age of social media and the internet, today data can be collected in various formats from Twitter data feeds to various sensor-enabled devices.  

C) Velocity – Velocity refers to the speed at which Data is created, collected and processed. Moore's Law states that as computer speed and capabilities increase, data is produced at astounding rates. Some web-connected devices function in real-time, necessitating real-time analysis and decision-making.  

Recently, Veracity, Value, and Variability are a few more Vs that have been added to the definition of Big Data.   

Although no prescribed size constitutes Big Data, it usually comprises Data in terabytes, petabytes and even exabytes of data that is collected over time. Businesses, organisations, start-ups, and even the government handle and process this data for various objectives.

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Big Data Analytics 

Big Data Analytics helps businesses make informed decisions and fasten the decision-making process. Big Data Analytics is the recurring process of analysing and evaluating a large body of Data. This helps uncover complex information, such as previously unknown patterns and insights, industry trends and consumer preferences. In short, Big Data Analytics is the method used for cleaning and analysing complex data to produce insights that help the organisation's growth. As per DataProt, by 2023, it is anticipated that the significant data analytics software and service industry will be valued at about 90 billion pounds.   

Benefits of Big Data Analysis 

Data scientists and other data analysts need a thorough comprehension of the available data and a clear concept of what they're searching for to provide reliable and pertinent findings from big data analytics. Using Insights obtained from Big Data Analytics, executives can solve many problems in the organisation. Big Data helps companies in Cost reduction, analysing buyer's journey, risk management, protection against cyber fraud, product development and increasing the organisation's overall productivity. 

A) Avoiding risks - Big Data analytics tools assist companies in reducing risk by predicting outcomes accurately. This helps them in avoiding threats that lead to failure.  

B) Saving time and money - Big Data analytics provides tools to filter out irrelevant data that helps in cost reduction and saves time. Big Data integrates pertinent information from several sources to provide extremely useful insights. 

C) Social media analysis - Big Data analytics can streamline their digital marketing tactics, refining advertising and promotions by utilising data from social media. Online marketers can understand the consumer’s purchasing behaviour and enhance their goods and services accordingly.  

D) Competitor analysis - Big Data helps in analysing competitor’s strategies and helps you stay ahead of your competitors.  

E) Performance analysis - Big Data analytics helps businesses understand their product performance in the market, which further helps in generating more sales leads based on insights obtained from the market.  

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Coming to What are the Types of Big Data 

Big data is a collection of structured, semi-structured, and unstructured data that businesses gather and may be mined for information in advanced analytics applications like machine learning and predictive modelling.  

Now that we know all about what Big data is and Big Data Analytics, let's understand: 

What is Structured Data in Big Data? 

Structured Data is a form of Big Data that is highly organised information stored in a conventional database. This data can be processed, stored and retrieved by simple search algorithms. The traditional database includes tables, spreadsheets, databases with rows and columns, Excel and CSV, etc. It was less messy than the data collected today. The only job to do in structured Big Data is to extract the information you require. For instance, the student's knowledge in a school will be stored in a spreadsheet as name, parent's name, class, grade and gender, etc.   

What is Unstructured Data in Big Data? 

Unlike structured data, unstructured data is not available in any specific form or organised manner. It does not exist in a neat row and column format. Earlier, this data had to segregate manually, but thanks to evolving tools and techniques in Big Data analytics this process has gotten easier. It is more challenging and time-consuming to process and analyse unstructured data. Examples of unstructured data include anything you write or post on social media, websites, email, text, consumer habits, GPS, and video activities among many others. 

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What is Semi-Structured Data in Big Data? 

Semi-structured type of Big Data is precisely how it sounds. That means the data isn't entirely organised or structured but is more accessible to process and analyse than unstructured data. While 20% of data is structured, the other 80% per cent falls in the unstructured and semi-structured data category. Semi-structured data contains vital information or tags that segregate individual elements within the data. Examples of semi-structured data include TCP/IP packets, XML and other markup languages, binary executables, data from various web pages, etc.
 

Most popular Big Data Trends 

Currently, the amount of data we produce in one day is more than the data produced in decades in the last century. Every activity creates all this Data we do on the internet. And you have a guess what a vast amount you alone would contribute. Big Data is driving the Fourth Industrial Revolution with AI, machine learning, and other technologies. One of the most potent technological revolutions, Big Data analytics, is changing how businesses operate worldwide.  

Big Data trends are patterns identified in a set of data that analysts use to arrange their searches for real-time data. There are many emerging trends in the field of Big Data, including the rapidly growing IoT networks, Open-source trends, Data as a product, predictive analytics, quantum computing, and data utilisation for hyper-personalisation, etc. 

Artificial Intelligence Trends 

Artificial Intelligence (AI) and Machine Learning (ML) are the top technologies used worldwide today. Using big data analytics to enable AI/ML automation, both for consumer-facing demands and internal operations, is one of the largest big data trends. Traditional analytics methods are difficult to automate for large-scale data analysis due to the enormous volume of data being created. Companies can analyse petabytes of data quickly thanks to distributed processing technologies, particularly those supported by open-source platforms like Hadoop and Spark. They can now more quickly discover trends, identify abnormalities, and make predictions thanks to machine learning and AI technology. AI and machine learning are popular in Big Data solutions due to features like speech recognition, improved privacy, real-time interactions, and many more. 

Cloud Analytics Trends 

In previous decades, enterprises had to handle their storage infrastructure. This resulted in massive data centres that they had to manage, secure and operate. Today, this has gotten much easier for firms with the development of cloud-based storage systems. To deal with the huge data generated on a day-to-day basis, organisations are investing more resources in storing data in a variety of cloud and hybrid cloud systems. An on-premises private cloud and a public cloud from a third party are both used in a cloud computing system with management between two interfaces. This offers superior flexibility and more data deployment possibilities. Organizations looking for the financial and technological benefits of cloud computing will undoubtedly see an increase in the expansion of public cloud and hybrid cloud infrastructures. In addition to innovations in cloud storage and processing, organisations are shifting toward new data architecture approaches that allow them to handle the 3 Vs of the big data efficiently. 

Edge Computing Trends

Edge processing is the act of running processes and transporting those processes to a local system, such as a user's PC, an IoT device, or a server. Edge computing reduces the need for data to pass via networks, hence lowering processing and computing costs, particularly those associated with cloud storage, bandwidth, and processing. This optimises performance and storage. Data analysis is accelerated by edge computing, which also gives users quicker responses. It boosts data streaming, including real-time processing and data streaming without delay. It makes it possible for the gadgets to react right away. Edge computing effectively processes large amounts of data while using less bandwidth 

Dark Data Trends 

Data that is not used in any analytical system by a firm and concealed, unidentified, and unprocessed. Dark data is usually the offline data that is kept as handwritten notes or as other paper-based work. Organisations aren't seeing any value in this data current, but are aware of the value it may bring in the future. The industry should be aware that any untapped data can pose a security concern because the amount of data is increasing daily. Another trend that has been observed is the increase in the volume of Dark Data. Big Data can help tackle this problem and turn the majority of it into online stored data for future use. 

Data Fabric Trends 

Data Fabric is an architecture and set of data networks. It is a major development that focuses on increasing the space available for digital transformation in an organisation. These developments are being adopted by organisations that require more space and accessibility for their expanding big data pools. Data Fabric streamlines and combines data storage across cloud and on-premises environments to promote digital transformation. Firms can store and retrieve necessary data sets across distributed on-premises, cloud, and hybrid networks with a data fabric architecture. 

Quantum Computing 

Quantum Computing helps in processing huge datasets at a much faster rate. This helps AI technologies to study data more thoroughly and in finding patterns and irregularities in data. Quantum Computing reduces the time of processing data and helps in compressing billions of data at once only in a few minutes. For instance, Google's Sycamore solved an issue in 200 seconds that would have taken the fastest supercomputer in use today 10,000 years to complete. This indicates better future opportunities for Big Data and analytics in the future.  

Data as a Service (DaaS) 

Data-as-a-Service is growing in popularity because of Big Data analytics, and by 2023, the market is expected to be worth 10.7 billion pounds. Data as a Service (DaaS) is a data management approach and deployment model that focuses on the cloud to provide a range of data-related services like storage, processing, and analytics. Daas offers easy data access from any device at any place, affordable data collection and processing, and assists in easy updates and monitoring. DaaS uses software-as-a-service (SaaS) model, which enables users to use cloud-based software programmes provided over a network. 

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Big Data’s Future Potential

Big Data will increasingly play a massive role in Machine Learning, Artificial Intelligence and Data Science in the future. Because these sectors demand data, big data will continue to play a significant role in enhancing current models and allowing for improvements in research.  

With the vast amount of data generated every single minute, the potential and future of Big Data will be limitless. Organisations can leverage the 3 Vs of Big Data for visualisation and discover newer insights into the business.   

Consider Tesla: each self-driving Tesla car is simultaneously training Tesla's AI model and continuously improving it with each error. All this is possible with the help of Big Data and Data analysts who can extract valuable insights.   

The benefits derived from Big Data can be limitless if used effectively, along with ensuring the safety and privacy of the customers. For instance, Companies can leverage Big Data to promote their products and grow their business. OTT platforms can streamline their shows based on the preferences of their viewers. They can analyse by processing the Big Data.  

As data continues to increase and develop, cloud storage companies such as AWS, Microsoft Azure, and Google Cloud will dominate the storage domain of extensive data. This enables the organisation to attain unprecedented levels of efficiency and scalability. This also implies that more employees will be employed to handle this data, resulting in additional employment prospects for "data officers" to administer a company's database. 

Conclusion

Now that you know what Big Data is and the future it holds. The next step is to get certification in Big Data and secure a bright future for yourself.  To get Let us know in the comments below if you have any enquiries.

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