What Is Big Data? Applications and Careers

Written by Coursera Staff • Updated on

Learn what big data is, common applications in a wide variety of industries, top desired skills, and what types of careers you can find in this industry.

[Featured image] Students learn big data concepts in a classroom.

Big data describes large, often complex, volumes of data that are difficult to process using traditional statistical methods. As technology has expanded to allow for consistent monitoring of data from a large variety of sources, developing methods to collect, store, analyse, and interpret this volume of information has become a priority. Patterns, trends, and associations that emerge from big data can help to inform individual and business-level decisions in ways that were not possible in the past.

In this article, you will learn more about what big data is, how it is used, and potential career opportunities in this expanding field. 

What makes big data “big”?

Though there is no threshold that separates big data from traditional data, big data is generally considered to be “big” because it cannot be processed effectively and quickly enough by older data analysis tools.


The three Vs of big data

Big data is broadly defined by the three Vs: volume, velocity, and variety.

  • Volume refers to the amount of data. Big data deals with high volumes of data. Data may come from sources such as smart devices, images, social media, medical records, industrial equipment, and transactions, to name a few. 

  • Velocity refers to the rate at which the data is received. As the Internet of Things (IoT) develops and continues to expand, data comes in at faster speeds from a wider variety of sources. Big data streams at a high velocity, often streaming directly into memory as opposed to being stored on a disk.

  • Variety refers to the wide range of data formats. Big data may be structured, semi-structured, or unstructured, and can present as numbers, text, images, audio, and more.

Companies that process big data may hone in on other Vs as well, such as value, veracity, and variability.

What’s driving big data growth?

Emerging information technology has allowed data to be collected, stored, and analysed at unprecedented scales. The internet continues to be adopted by new users in the UK and across the globe, and developing technologies have allowed internet integration into many different products, creating numerous new sources of data. The millions of people watching Netflix, using Google, or buying products online every day contribute to the increasing volume and sophistication of big data.

The government in the UK has launched several initiatives to improve the collection, storage, and analysis of big data to increase access and use of emerging data. One such project was the Big Data Network Support project from 2014-2017, which helped to increase big data architecture and develop guidelines for the collection and sharing of public data sets. Current projects such as the Safe Data Access Professionals (SDAP) Network and the International Data Access Network (IDAN) continue to help to revolutionise the big data landscape across the UK and across Europe.

Examples of big data

  • Health care: The health care system is full of data. Data analysts can use aggregated information on health care records, insurance, and patient summaries to drive new insights and enhance patient care. In countries like the UK with public health care systems, big data has the potential to truly centralise and streamline health care information.

  • Smart (IoT) devices: A connection to the internet enables companies to collect data through devices like smart home systems, robotic vacuum cleaners, smart TVs, mobile devices, and wearable fitness trackers that log files.

  • Social media: Likes, shares, posts, comments, how long you spend looking at a post—all of this information is considered insightful data about people’s behaviour, sentiment, and preferences.

  • Websites: Companies or other website owners can track page visits, general locations of visitors, how long audiences spend on a page, what links are most clicked, and cursor movement.

  • Business transactions: Data can come from customers as they purchase products, online and in person. Price, time of purchase, payment methods, and other details can inform a business about customer demand for their products.

  • Machinery: Even without an internet connection, machines like road cameras, sensors, and medical equipment can record information.

  • Government: Local and national governments can use data from many sources—auto traffic information, agricultural yields, weather tracking systems, and demographic information from censuses, to name a few—to make policy decisions.

How is big data used?

Big data can be used by almost any entity to gain valuable insights and make decisions about their operations. A business, for example, can analyse the data they collect to better understand customer preferences and devise impactful business strategies. If one area of a business is losing money, big data can help organisations to understand why.

Health care systems are another huge use of big data. Large volumes of patient and medical data can be used to find common symptoms of diseases, or decide how much staff to put on a hospital floor at any given time. Governments may use traffic data to plan new roads or track crime rates or terrorism risks to adjust their response accordingly.

Big data skills

Professionals who work with big data may use the following skills to help them perform their job responsibilities: 

  • Programming languages: Computer programming is often at the heart of big data. Because of the large volume of information, special data architectures, analysis procedures, and management systems are often needed to efficiently handle the data. Knowing programming languages such as Java, C++, Scala, or Python can help you develop the needed skills to excel in this field.

  • Predictive analytics: Analysts can use data to predict the likelihood of events or trends in the future by using predictive models and machine learning technology.

  • Real-time analytics: Real-time analytics is the process of analysing and using data the moment it enters a database to make decisions quickly, such as when a banking system flags a payment as potentially fraudulent when it is made out of the country.

  • Data mining: Data mining refers to a process that combs through huge amounts of data to find patterns, trends, and correlations. Finding relationships between data points is key to helping organisations make decisions.

  • Machine learning: Machine learning—a form of artificial intelligence that learns and improves itself continuously—helps to predict trends and find patterns in large sets of data. Machine learning can be useful in adapting to new data influxes. Professionals in big data often earn more when they have machine learning or artificial intelligence skills compared to professionals in the field who do not.

  • Deep learning: Deep learning is a subset of machine learning that is based on artificial neural networks and mimics the learning process of the human brain. Deep learning is often used in speech and text recognition, and computer vision technology.

  • Data warehouses: Data warehouses store massive amounts of historical data. The data is typically cleaned and organised and can be accessed at a later date to be analysed.

  • Hadoop: Hadoop is a software framework used to store and process vast amounts of data, including data from the IoT, that can work across several clusters of computers. Hadoop’s capacity to be scaled easily and ability to store various types of data at once have made it the go-to platform to process big data.

  • Apache Spark: Apache Spark is a software framework that combines data analysis with artificial intelligence. It has become one of the most important big data frameworks and can be used in conjunction with common programming languages such as Java, Scala, Python, and R.

7 big data careers

Data professions are growing quickly in the UK, with nearly half of businesses struggling to recruit qualified candidates for these roles [1]. Because of this, developing skills to competently fill available big data roles is likely to put you in high demand.  In fact, AI, machine learning specialists, and big data specialists took the top three positions in the World Economic Forum’s list of top job roles with increasing demand across industries in 2020 [2]. When looking for career opportunities that utilise big data skills, consider the following:

1. Data analyst

A data analyst works to gather, clean, and interpret data and create data models. When working with big data, data analysts will perform these tasks on a larger scale by utilising relevant big data software and techniques. Data analysts can work in a variety of different industries, including business, science, and health care.

2. Data engineer

Data engineers work to create and maintain data infrastructure. This can include data warehouses, data pipelines, and other forms of organising data that analysts can use to make predictions or other interpretations. Big data engineers do this with software that allows them to manoeuvre large volumes of data.

3. Data developer

Big data developers are similar to software developers and work directly with writing the code for Hadoop applications. They may use multiple types of programming languages, such as Java, Ruby, or C++. 

4. Data scientist

A data scientist generally uses mathematical or statistical knowledge to build algorithms, models, and other analytical tools to help organise and interpret data. 

5. Data architect

A big data architect builds and maintains infrastructures capable of organising large volumes of data. This allows for convenient access and analysis of subsets of this data.

6. Business intelligence analyst

Business intelligence analysts parse business data like sales information or customer engagement metrics to form actionable insights into how a business is performing. 

7. Operations analyst

Operations analysts gather data about operational issues in businesses or other organisations. Operations analysts can use data to find business insights and solutions to issues in production, staffing, or any other related aspect.

Learn how to use big data

Developing skills in big data has the potential to open career opportunities in many industries. Learning to incorporate big data into your career can bring fresh insights into your work, and data is likely only to continue to grow in importance. 

To get started, consider getting a crash course on the basics and add a relevant certificate to your resume by completing the Google Data Analytics Professional Certificate on Coursera. Build a strong foundation for a career in big data.

Article sources


Gov.uk. “Quantifying the UK Data Skills Gap - Full report, https://www.gov.uk/government/publications/quantifying-the-uk-data-skills-gap/quantifying-the-uk-data-skills-gap-full-report.” Accessed on November 17, 2022.

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