What Is Big Data Analytics?

Written by Coursera • Updated on

Here's a guide to what you need to know about big data analytics.

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Big data analytics captures all types of data involved in a business and uses it to apply analytics to gain valuable 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 instance, as a real world example, big data analytics is a must in the 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. 

This guide explains the information you need to know about big data analytics.

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.

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

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A video discussing what is big data, presented by PricewaterhouseCoopers.

Read more: What Is Data Analysis? (With Examples)

Why is big data analytics important? 

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 COVID-19 outcomes on a global scale. It informs health ministries within each nation’s government on how to proceed with vaccinations and devises solutions for mitigating pandemic outbreaks in the future. 

Almost eight in ten users (79 percent) 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 percent of companies with revenues over $10 billion reported being “highly satisfied” or “satisfied” with their big data-driven business outcomes [2]

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Benefits of big data analytics

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

  • 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 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.

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

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  • 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 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 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 in order to hedge risks.

Tools used in big data analytics

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 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

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Related articles

Article sources

1. IBM. “The 5 V’s of big data, https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data/.” Accessed April 14, 2022.

2. Accenture. “Big Success With Big Data, https://www.accenture.com/us-en/_acnmedia/accenture/conversion-assets/dotcom/documents/global/pdf/industries_14/accenture-big-data-pov.pdf.” Accessed April 14, 2022.

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