Big Data definition: Benefits, Types and Examples

Big data is a collection of vast and varied sets of structured, unstructured‌ and semi-structured data that grow rapidly over time. The sheer size, speed of accumulation‌ and diversity of these datasets make them too complex for traditional data management systems to effectively store, process‌ and analyse.

Big data technologies are integral to various advanced applications such as Machine Learning (ML) and predictive modelling‌. They help businesses tackle complex problems and make informed decisions by analysing large amounts of data quickly and efficiently.

Big data offers a multitude of benefits that can transform the way you operate your business and make decisions. Here’s how leveraging big data can be a game-changer for your business 👇

1 - Improved decision-making

When you effectively manage and analyse your big data, you unlock the ability to spot patterns and insights that weren't apparent before. This can significantly enhance both your operational and strategic decisions, helping you to act based on evidence rather than intuition.

2 - Better customer experiences

By doing big data analysis of both structured and unstructured data, you gain deeper insights into consumer behaviour. This enables you to tailor experiences, personalise interactions‌ and meet consumer needs more effectively, enhancing overall customer satisfaction.

3 - Increased agility

With big data tools, you can collect and analyse data in real-time. This capability allows you to plan and launch new products and features‌ faster and, at the same time, adapt to market changes and stay ahead of your competition.

4 - Operational efficiency

Using big data analytics tools enhances your ability to process data swiftly and accurately. This means you can identify areas where you can cut costs, save time‌ and boost your overall operational efficiency.

5 - Improved risk management

Big data analytics help you identify and mitigate potential threats, enhancing your ability to develop effective control strategies and managing risks better.

There are 3 main types of big data. Each type has its own unique characteristics 👇

1 - Structured

Structured data is highly organised and formatted in a way that makes it easily searchable in databases. It follows a strict schema, so it can be easily entered, queried‌ and analysed using standard tools and methods.

Examples of structured data include:

• Excel spreadsheets where names, addresses‌ and phone numbers are stored in dedicated columns

• Database tables where data is stored in predefined models like rows and columns, such as SQL databases used for customer relationship management (CRM) systems

2 - Unstructured

Unstructured data is raw data that doesn't follow a specific format or structure, making it more difficult to collect, process‌ and analyse. It makes up most of the data available in the digital universe today.

Examples of unstructured data include:

• Text files, such as emails, articles‌ and documents that don't have a rigid structure

• Multimedia files like videos, audio files‌ and photos, where content isn't organised in a predefined manner

3 - Semi-structured

Semi-structured data is a blend of both structured and unstructured data. It isn't organised in a strict tabular form, but still contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields.

Examples of semi-structured data include:

• JSON files, which are used to transmit data between a server and a web application. JSON files contain data that's hierarchical and tagged but doesn't fit neatly into tables, rows‌ and columns.

• XML files are used for various web technologies that store data in a tree-like structure, which is more flexible than a table but still contains tags to separate elements.

Today, big data originates from numerous distinct sources, each contributing extensively to the vast collections of data. Here are some of the examples 👇

1 - Social media

Platforms such as Facebook, Instagram and YouTube are massive sources of big data. Every interaction, from uploads to likes, generates data, contributing to an ever-growing dataset of user behaviour and preferences.

2 - Customer feedback

Online stores like Amazon and eBay, gather tonnes of data from customer reviews and feedback. This helps them figure out what customers think and how they can improve.

3 - Ecommerce and transactional data

Every time you buy something online or make a transaction, data is created. This includes details about what was bought, how much it cost‌ and how it was paid for, which helps businesses understand shopping habits.

4 - Sensors and IoT devices

IoT devices such as smart TVs and smart fridges are always connected to the internet, sending data back and forth. Also, sensor data from sensors around cities collect environmental data like temperature and humidity.

5 - GPS Tracking

GPS-enabled devices in vehicles provide real-time data on vehicle positions and movements, which helps in planning efficient routes to reduce time and fuel consumption.