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

Big data analytics uses advanced analytics on large collections of structured and unstructured data 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. 

For example, big data analytics is integral to the modern health care industry. As you can imagine, organisations have thousands of patient records, insurance plans, prescriptions, and vaccine information to manage. Those vast amounts of structured and unstructured data can offer important insights, particularly after applying analytics. Big data analytics makes the process fast and efficient, making it easier for health care providers to 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 of today’s industries. 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 to help you get started on your own 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 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.

So, what makes data 'big'?

Big data is characterised by the five V's: volume, velocity, variety, variability, and value [1]. It’s complex, so making sense of all the data in the business requires both innovative technologies and analytical skills.

To learn more about big data and how it informs business decisions, watch this video from PricewaterhouseCoopers:

Why is big data analytics important? 

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 COVID-19 outcomes on a global scale. It 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

Almost eight in ten users (79 per cent) believe that ‘companies that do not embrace big data will lose their competitive position and may even face extinction’, according to an Accenture report [2]. In their survey of Fortune 500 companies, Accenture found that 95 per cent of companies with revenues over $10 billion reported being ‘highly satisfied’ or ‘satisfied’ with their big data-driven business outcomes [2]. In the UK, 85 per cent of businesses with more than 10 employees are data-driven [3]. 

Benefits of big data analytics

You find quite a few advantages to incorporating big data analytics into a business or organisation. These include:

• Cost reduction: Big data can reduce costs in storing all the 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 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 individual preferences has been transformative for the entertainment industry (think Spotify and Netflix).

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

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

• Government: Big data can 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 help 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. They include:

1. Descriptive analytics

Descriptive analytics refers to data that’s read and interpreted easily. 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 unutilised spaces and departments that decision-makers consolidated, 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: A clothing company’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 properly 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 provides a solution to a problem, relying on AI and machine learning to gather data and use it 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.

Tools used in big data analytics

Harnessing all of that data requires tools. Thankfully, technology has advanced, providing many intuitive software systems for data analysts.

• 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 used for real-time processing and analysing data.

• Data integration software: Programmes that streamline big data 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 have the capability to 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: Programmes 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. 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.