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

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

In this guide, you'll learn more about what big data analytics is, why it's important, and some common benefits. 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 own data analytics professional journey.

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What is big data analytics?

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

Read more: What Is Big Data? A Layperson's Guide

Example of big data analytics

For example, big data analytics is integral to the modern health care industry. As you can imagine, thousands of patient records, insurance plans, prescriptions, and vaccine information need to be managed. It comprises huge 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. 

Why is big data analytics important? 

Data will become increasingly integral and transformative to day-to-day business operations, according to McKinsey & Company [2]. Big data analytics is important because it helps companies leverage their data to identify opportunities for improvement and optimization. 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 analyzes individual records, but plays a critical role in measuring public health outcomes on a global scale. It informs health ministries within each nation’s government on how to proceed with public and population health policy and devises solutions for mitigating future society-wide health problems. 

Benefits of big data analytics

There are quite a few advantages to incorporating big data analytics into a business or organization. These include:

• Cost reduction: Big data can reduce costs in storing all of a business's 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 analyze data helps businesses make better and faster decisions, such as cost and supply chain optimization.

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

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

• Entertainment: Providing a personalized 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 alike 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)

There are four main types of big data analytics that 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 visualize information that can detail company profits and sales. 

Example: During the Covid-19 pandemic, a leading pharmaceuticals company conducted data analysis on its offices and research labs. Descriptive analytics helped them identify unutilized spaces and departments that were consolidated, saving the company millions of dollars.

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 analyze data to predict market trends.

Example: In the manufacturing sector, companies can use machine learning models trained 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 in order to hedge risks.

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Big data analytics tools

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

• Hadoop: An open-source framework that stores and processes big data sets. Hadoop is able to handle and analyze structured and unstructured data. 

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

• 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 analyze 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 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.

Familiarize yourself with big data analytics tools

Knowing how to use industry-standard tools like the ones mentioned above is essential. Whether you're pursuing a promotion or interested in obtaining a more advanced role, you can gain experience in commonly used tools online. Here are a few relevant options to consider:

• Introduction to Big Data with Spark and Hadoop

• Google Cloud Big Data and Machine Learning Specialization

• Data Engineering, Big Data, and Machine Learning on GCP Specialization

• PostgreSQL for Everybody Specialization

• Microsoft Copilot for Data Science

Start advancing your data analytics skills today

If you want to expand your big data analytics skill set to meet your career goals, you have options. For example, you can learn from an industry leader while earning a credential for your resume with IBM's Introduction to Data Analytics.

And, in just six months or less, you can learn in-demand, job-ready skills like data cleaning, analysis, and visualization with the Google Data Analytics Professional Certificate. You'll also gain hands-on experience with spreadsheets, SQL programming, and Tableau.