What Is Big Data Analytics? Definition, Benefits, and More

Big data analytics uses advanced analytics on large structured and unstructured data collections to produce valuable business insights. It is used widely across industries as varied as health care, education, insurance, artificial intelligence, retail, and manufacturing to understand what’s working and what’s not to improve processes, systems, and profitability. 

It comprises vast amounts of structured and unstructured data, which can offer important insights when analytics are applied. Big data analytics does this quickly and efficiently so that health care providers can use the information to make informed, life-saving diagnoses. 

In this guide, you'll learn more about big data analytics, why it's important, and its benefits for many different industries today. You'll also learn about types of analysis used in big data analytics, find a list of common tools used to perform it and find suggested courses that can help you get started on your data analytics professional journey.

What is big data analytics?

Big data analytics is the process of collecting, examining, and analysing large amounts of data to discover market trends, insights, and patterns that can help companies make better business decisions. This information is available quickly and efficiently so that companies can be agile in crafting plans to maintain their competitive advantage.

Technologies such as business intelligence (BI) tools and systems help organisations take unstructured and structured data from multiple sources. Users (typically employees) input queries into these tools to understand business operations and performance. Big data analytics uses the four data analysis methods to uncover meaningful insights and derive solutions.

For example, big data analytics is integral to the modern health care industry. As you can imagine, systems that must manage thousands of patient records, insurance plans, prescriptions, and vaccine information.

So, what makes data "big"?

Big data is characterised by the five V's: volume, velocity, variety, variability, and value. It's complex, so making sense of all the data in the business requires innovative technologies and analytical skills.

The importance of big data analytics

Big data analytics is important because it helps companies leverage their data to identify opportunities for improvement and optimisation. Across different business segments, increasing efficiency leads to overall more intelligent operations, higher profits, and satisfied customers. Big data analytics helps companies reduce costs and develop better, customer-centric products and services.

Data analytics helps provide insights that improve the way our society functions. In health care, big data analytics not only keeps track of and analyses individual records but it plays a critical role in measuring outcomes on a global scale. During the COVID-19 pandemic, big data-informed health ministries within each nation’s government on how to proceed with vaccinations and devised solutions for mitigating pandemic outbreaks in the future.

Use big data to stay competitive

Marketing research firm Mordor Intelligence expects significant growth in the big data technology and service market over the next few years. It recently reported an anticipated CAGR of 35.1 per cent from 2021 to 2026 [1].

This growth is the result of the Indian government's digital India campaign and businesses' increasing use of data to understand the needs and interests of their customers. A survey by Tableau Software and YouGov revealed that more than 80 per cent of Indian companies that prioritise data-driven decision-making grew during the COVID-19 pandemic [2].

Benefits of big data analytics

Incorporating big data analytics into a business or organisation has several advantages. These include:

• Cost reduction: Big data can reduce costs in storing all business data in one place. Tracking analytics also helps companies find ways to work more efficiently to cut costs wherever possible.

• Product development: Developing and marketing new products, services, or brands is much easier when based on data collected from customers’ needs and wants. Big data analytics also helps businesses understand product viability and to keep up with trends.

• Strategic business decisions: The ability to constantly analyse data helps businesses make better and faster decisions, such as cost and supply chain optimisation.

• Customer experience: Data-driven algorithms help marketing efforts (targeted ads, for example) and increase customer satisfaction by delivering an enhanced customer experience.

• Risk management: Businesses can identify risks by analysing data patterns and developing solutions for managing those risks.

Big data in the real world

Big data analytics helps companies and governments make sense of data and make better, informed decisions.

• Entertainment: Providing a personalised recommendation of movies and music according to a customer’s preferences has been transformative for the entertainment industry (think Spotify and Netflix).

• Education: Big data helps schools and educational technology companies develop new curriculums while improving existing plans based on needs and demands.

• Health care: Monitoring patients’ medical histories helps doctors detect and prevent diseases.

• Government: Big data can be used to collect data from CCTV and traffic cameras, satellites, body cameras and sensors, emails, calls, and more, to help manage the public sector.

• Marketing: Customer information and preferences can be used to create targeted advertising campaigns with a high return on investment (ROI).

• Banking: Data analytics can help track and monitor illegal money laundering.

Types of big data analytics (+ examples)

Four main types of big data analytics support and inform different business decisions.

1. Descriptive analytics

Descriptive analytics refers to data that can be easily read and interpreted. This data helps create reports and visualise information that can detail company profits and sales. 

Example: During the pandemic, a leading pharmaceutical company conducted data analysis on its offices and research labs. Descriptive analytics helped them identify consolidated unutilised spaces and departments, saving the company millions of pounds.

2. Diagnostics analytics

Diagnostics analytics helps companies understand why a problem occurred. Big data technologies and tools allow users to mine and recover data that helps dissect an issue and prevent it from happening in the future.

Example: An online retailer’s sales have decreased even though customers continue to add items to their shopping carts. Diagnostics analytics helped to understand that the payment page was not working correctly for a few weeks.

3. Predictive analytics

Predictive analytics looks at past and present data to make predictions. With artificial intelligence (AI), machine learning, and data mining, users can analyse the data to predict market trends.

Example: In the manufacturing sector, companies can use algorithms based on historical data to predict if or when a piece of equipment will malfunction or break down.

4. Prescriptive analytics

Prescriptive analytics solves a problem, relying on AI and machine learning to gather and use data for risk management. 

Example: Within the energy sector, utility companies, gas producers, and pipeline owners identify factors that affect the price of oil and gas to hedge risks.

Big data analytics tools

Harnessing all of that data requires tools. Thankfully, technology has advanced so that many intuitive software systems are available for data analysts to use.

• Hadoop: An open-source framework that stores and processes big data sets. Hadoop can handle and analyse structured and unstructured data.

• Spark: An open-source cluster computing framework for real-time processing and data analysis.

• Data integration software: Programs that allow big data to be streamlined across different platforms, such as MongoDB, Apache, Hadoop, and Amazon EMR.

• Stream analytics tools: Systems that filter, aggregate, and analyse data that might be stored in different platforms and formats, such as Kafka.

• Distributed storage: Databases that can split data across multiple servers and can identify lost or corrupt data, such as Cassandra.

• Predictive analytics hardware and software: Systems that process large amounts of complex data, using machine learning and algorithms to predict future outcomes, such as fraud detection, marketing, and risk assessments.

• Data mining tools: Programs that allow users to search within structured and unstructured big data.

• NoSQL databases: Non-relational data management systems ideal for dealing with raw and unstructured data.

• Data warehouses: Storage for large amounts of data collected from many different sources, typically using predefined schemas.

Explore big data analytics with Coursera

Build toward a career in big data analytics with Google’s Data Analytics Professional Certificate, available on Coursera. In just six months or less, you’ll learn in-demand, job-ready skills (data cleaning, analysis, and visualisation) and tools (spreadsheets, SQL programming, Tableau), to improve your use of data within your business or to get you job ready for a role in big data.