{"id":3820,"date":"2020-07-07T09:00:00","date_gmt":"2020-07-07T07:00:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/big-data-examples\/"},"modified":"2023-09-28T14:44:25","modified_gmt":"2023-09-28T12:44:25","slug":"big-data-examples","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/big-data-examples\/","title":{"rendered":"What Are Some Real-World Examples of Big Data?"},"content":{"rendered":"

Since our first ancestors put ink to parchment, data has been part of the human experience.<\/strong><\/p>\n

From tracking the complex movements of the planets, to more basic things like bookkeeping, data has shaped the way we\u2019ve evolved. Today, thanks to the internet, we collect such vast amounts of data that we have a whole new term to describe it: \u201cbig data.\u201d<\/p>\n

While big data is not only collected online, the digital space is undoubtedly its most abundant source. From social media likes, to emails, weather reports, and wearable devices, huge amounts of data are created and accumulated every second of every day. But how exactly is it used?<\/p>\n

If you\u2019re just starting out from scratch, then try this\u00a0free data short course<\/strong><\/a> on for size.<\/p>\n

In this article, I\u2019ll focus on some of the most big data examples out there. These are ways in which organizations\u2014large and small\u2014use big data to shape the way they work.<\/p>\n

    \n
  1. What is big data and why is it useful?<\/a><\/li>\n
  2. Big data in marketing and advertising<\/a><\/li>\n
  3. Big data in education<\/a><\/li>\n
  4. Big data in healthcare<\/a><\/li>\n
  5. Big data in travel, transport, and logistics<\/a><\/li>\n
  6. Key takeaways<\/a><\/li>\n<\/ol>\n

    First, let\u2019s start with a quick summary of what big data is, and why so many organizations are scrambling to harness its potential.<\/p>\n

    1. What is big data and why is it useful?<\/h2>\n

    \u201cBig data\u201d is used to describe repositories of information too large or complex to be analyzed using traditional techniques. For the most part, big data is unstructured, i.e. it is not organized in a meaningful way.<\/p>\n

    Although the term is commonly used to describe information collected online, to understand it better, it can help to picture it literally. Imagine walking into a vast office space without desks, computers, or filing cabinets. Instead, the whole place is a towering mess of disorganized papers, documents, and files. Your job is to organize all of this information and to make sense of it. No mean feat!<\/p>\n

    While digitization has all but eradicated the need for paper documentation, it has actually increased the complexity of the task. The skill in tackling big data is in knowing how to categorize and analyze it. For this, we need the right big data tools<\/a>\u00a0and know-how. But how do we categorize such vast amounts of information in a way that makes it useful?<\/p>\n

    While this might seem like a fruitless task, organizations worldwide are investing huge amounts of time and money in trying to tap big data\u2019s potential. This is why data scientists and data analysts are currently so in demand.<\/p>\n

    Learn more about it in our complete guide to what is big data<\/a>.<\/p>\n

    But how is it done? Let\u2019s take a look.<\/p>\n

    2. Big data in marketing and advertising<\/h2>\n

    One of big data\u2019s most obvious uses is in marketing and advertising. If you\u2019ve ever seen an advert on Facebook or Instagram, then you\u2019ve seen big data at work. Let\u2019s explore some more concrete examples.<\/p>\n

    Netflix and big data<\/h3>\n

    Netflix has over 150 million subscribers, and collects data on all of them. They track what people watch, when they watch it, the device being used, if a show is paused, and how quickly a user finishes watching a series.<\/p>\n

    They even take screenshots of scenes that people watch twice. Why? Because by feeding all this information into their algorithms, Netflix can create custom user profiles. These allow them to tailor the experience by recommending movies and TV shows with impressive accuracy.<\/p>\n

    And while you might have seen articles about how Netflix likes to splash the cash on new shows<\/a>, this isn\u2019t done blindly\u2014all the data they collect helps them decide what to commission next.<\/p>\n

    Amazon and big data<\/h3>\n

    Much like Netflix, Amazon collects vast amounts of data on its users. They track what users buy, how often (and for how long) they stay online, and even things like product reviews (useful for sentiment analysis<\/a>).<\/p>\n

    Amazon can even guess people\u2019s income based on their billing address. By compiling all this data across millions of users, Amazon can create highly-specialized segmented user profiles.<\/p>\n

    Using predictive analytics<\/a>, they can then target their marketing based on users\u2019 browsing habits. This is used for suggesting what you might want to buy next, but also for things like grouping products together to streamline the shopping experience.<\/p>\n

    McDonald\u2019s and big data<\/h3>\n

    Big data isn\u2019t just used to tailor online experiences. A good example of this is McDonald\u2019s, who use big data to shape key aspects of their offering offline, too. This includes their mobile app, drive-thru experience, and digital menus.<\/p>\n

    With its own app, McDonald\u2019s collects vital information about user habits. This lets them offer tailored loyalty rewards to encourage repeat business. But they also collect data from each restaurant\u2019s drive-thru, allowing them to ensure enough staff is on shift to cover demand. Finally, their digital menus offer different options depending on factors such as the time of day, if any events are taking place nearby, and even the weather.<\/p>\n

    So, if it\u2019s a hot day, expect to be offered a McFlurry or a cold drink\u2026not a spicy burger!<\/p>\n

    3. Big data in education<\/h2>\n

    Until recently, the approach to education was more or less one-size-fits-all. With companies now harnessing big data, this is no longer the case. Schools, colleges, and technology providers are all using it to enhance the educational experience.<\/p>\n

    Reducing drop-out rates with big data<\/h3>\n

    Purdue University in Indiana was an early adopter of big data in education. In 2007, Purdue launched a unique, early intervention system called Signals, which was designed to help predict academic and behavioral issues.<\/p>\n

    By applying predictive modeling to student data (e.g. class prep, level of engagement, and overall academic performance) Purdue was able to accurately forecast which students were at risk of dropping out. When action was required, both students and teachers were informed, meaning the college could intervene and tackle any issues. As a result, according to one study, those taking two or more Signals courses were 21% less likely to drop out.<\/a><\/p>\n

    Improving the learner experience with big data<\/h3>\n

    Some educational technology providers use big data to enhance student learning. One example of this is the UK-based company, Sparx<\/a>, who created a math app for school kids. Using machine learning, personalized content, and data analytics, the app helps improve the pupil learning experience.<\/p>\n

    With over 32,000 questions, the app uses an adaptive algorithm to push the most relevant content to each student based on their previous answers. This includes real-time feedback, therefore tackling mistakes as soon as they arise. Plus, by collecting data from all their users across schools, Sparx gains broader insight into the overall learning patterns and pitfalls that students face, helping them to constantly improve their product.<\/p>\n

    Improving teaching methods with big data<\/h3>\n

    Other educational technology providers have used big data to improve teaching methods. In Roosevelt Elementary School in San Francisco, teachers use an analytics app called DIBELS<\/a>. The app gathers data on children\u2019s reading habits so that teachers can see where they most need help.<\/p>\n

    Aggregating data on all pupils, teachers can group those with the same learning needs, targeting teaching where it\u2019s most needed. This also encourages educators to reflect on their methods. For instance, if they face similar issues across multiple students, they might need to adapt their approach.<\/p>\n

    \"Healthcare<\/h2>\n

    4. Big data in healthcare<\/h2>\n

    From pharmaceutical companies to medical product providers, big data\u2019s potential within the healthcare industry is huge. Vast volumes of data inform everything from diagnosis and treatment, to disease prevention, and tracking.<\/p>\n

    Electronic health records and big data<\/h3>\n

    Our medical records include everything from our personal demographics to our family histories, diets, and more. For decades, this information was in a paper format, limiting its usefulness.<\/p>\n

    However, health systems around the world are now digitizing these data, creating a substantial set of electronic health records (EHRs). EHRs have vast potential. On a day-to-day level, they allow doctors to receive reminders or warnings when a patient needs to be contacted (for instance, to check their medication).<\/p>\n

    However, EHRs also allow clinical researchers to spot patterns between things like disease, lifestyle, and environment\u2014correlations that would previously have been impossible to detect. This is revolutionizing how we detect, prevent, and treat disease, informing new interventions, and changes in government health policy.<\/p>\n

    Big data and wearable devices<\/h3>\n

    Healthcare providers are always seeking new ways to improve patient care with faster, cheaper, more effective treatments. Wearables are a key part of this. They allow us to track patient data in real-time.<\/p>\n

    For instance, a heart monitor worn to detect blood pressure can allow doctors to track patients for extended periods at home, rather than relying on the results of a quick hospital test. If there\u2019s a problem, doctors can quickly intervene. More importantly though, using big data analytics<\/a> tools, information collected from countless patients can offer invaluable insights, helping healthcare providers improve their products. This ultimately saves money and lives.<\/p>\n

    Big data for disease tracking<\/h3>\n

    Another application of big data in healthcare is disease tracking. The current coronavirus pandemic is a perfect example. Since the coronavirus outbreak began, governments have been scrabbling to launch track-and-trace systems to stem the spread of disease.<\/p>\n

    In China, for instance, the government has introduced heat detectors at train stations to identify those with fever. Because every passenger is legally required to use identification before using public transport, authorities can quickly alert those who may have been exposed. The Chinese government also uses security cameras and mobile phone data to track those who have broken quarantine. While this does come with privacy concerns, China\u2019s approach nevertheless demonstrates the power of big data.<\/p>\n

    5. Big data in travel, transport, and logistics<\/h2>\n

    From flying off on vacation to ordering packages to your front door, big data has myriad applications in travel, transport, and logistics. Let\u2019s explore further.<\/p>\n

    Big data in logistics<\/h3>\n

    Tracking warehouse stock levels, traffic reports, product orders, and more, logistics companies use big data to streamline their operations. A good example is UPS. By tracking weather and truck sensor data, UPS learned the quickest routes for their drivers.<\/p>\n

    This itself was a useful insight, but after analyzing the data in more detail, they made an interesting discovery: by turning left across traffic, drivers were wasting a lot of fuel<\/a>. As a result, UPS introduced a \u2018no left turn\u2019 policy. The company claims that they now use 10 million gallons less gas per year, and emit 20,000 tonnes less carbon dioxide. Pretty impressive stuff!<\/p>\n

    Big data and city mobility<\/h3>\n

    Big data is big business in urban mobility, from car hire companies to the boom of e-bike and e-scooter hire. Uber is an excellent example of a company that has harnessed the full potential of big data. Firstly, because they have a large database of drivers, they can match users to the closest driver in a matter of seconds.<\/p>\n

    But it doesn\u2019t stop there. Uber also stores data for every trip taken. This enables them to predict when the service is going to be at its busiest, allowing them to set their fares accordingly. What\u2019s more, by pooling data from across the cities they operate in, Uber can analyze how to avoid traffic jams and bottlenecks. Cool, huh?<\/p>\n

    Big data and the airline industry<\/h3>\n

    Aircraft manufacturer, Boeing, operates an Airplane Health Management System. Every day, the system analyzes millions of measurements across their entire fleet. From in-flight metrics to mechanical analysis, the resulting data has numerous applications.<\/p>\n

    For instance, by predicting potential failures, the company knows when servicing is required, saving them thousands of dollars annually on unnecessary maintenance. More importantly, this big data provides invaluable safety insights, improving airplane safety at Boeing, and across the airline industry at large.<\/p>\n

    6. Big data in finance and banking<\/h2>\n

    Fraud detection with big data<\/h3>\n

    Banks and financial institutions process billions of transactions daily\u2014in 2022 there were more than 21,510 credit card transactions<\/span><\/a> per second<\/strong>!\u00a0With the rise of online banking, mobile payments, and digital transactions, the risk of fraud has also increased.<\/p>\n

    Big data analytics can help in detecting unusual patterns or behaviors in transaction data. For instance, if a credit card is used in two different countries within a short time frame, it might be flagged as suspicious. By analyzing vast amounts of transaction data in real-time, banks can quickly detect and prevent fraudulent activities.<\/p>\n

    Personalized banking with big data<\/h3>\n

    With over 78% of Americans banking digitally<\/span><\/a>, banks are increasingly using big data to offer personalized services to their customers. By analyzing a customer’s transaction history, browsing habits, and even social media activities, banks can offer tailored financial products, interest rates, or even financial advice.<\/p>\n

    For instance, if a bank notices that a customer is frequently spending on travel, they might offer them a credit card with travel rewards or discounts.<\/p>\n

    7. Big data in agriculture<\/h2>\n

    Precision farming with big data<\/h3>\n

    Farmers are using big data to make more informed decisions about their crops. How do they achieve this? Well, with sensors placed in fields measure the moisture levels, temperature, and soil conditions, as well as on tractors and other farm machinery.<\/p>\n

    Speaking of farm machinery, here’s an unusual but not for long example: d<\/strong>rones<\/strong>. By equipping drones with cameras can provide detailed aerial views of the crops, helping in detecting diseases or pests. Hobby drone giant DJI<\/a> already produces its own line of drones for this purpose.<\/p>\n

    By analyzing this data, farmers can determine the optimal time to plant, irrigate, or harvest their crops, leading to increased yields and reduced costs.<\/p>\n

    Supply chain optimization with big data<\/h3>\n

    Agricultural supply chains are complex, with multiple stages from farm to table. Big data can help in tracking and optimizing each stage of the supply chain. For instance, by analyzing data from transportation vehicles, storage facilities, and retail outlets, suppliers can ensure that perishable goods like fruits and vegetables are delivered in the shortest time, reducing wastage and ensuring freshness.<\/p>\n

    These examples can be integrated into the article to provide a more comprehensive overview of the diverse applications of big data across different sectors.<\/p>\n

    8. Key takeaways<\/h2>\n

    In this post, we\u2019ve explored big data\u2019s real-world uses in several industries. Big data is regularly used by:<\/p>\n