36+ Big Data Examples and Applications In Real Life

As companies rationalize their database management practices, more firms turn to big data to operationalize their business outcomes. 

The influence of big data and its gargantuan impact on commercial and non-commercial industries today has made headlines. While market leaders are questing to unravel hidden behavior, trends, user sentiments, and failover operations with big data, some entities are still foggy on their correlation with products and services. 

Industries are adopting big data software with advanced data analytics features to forecast predictive abilities, mitigate product flaws, and improve product-led lifecycles. In fact, European Business magazine revealed a 54% increase in investment in data analytics and customer insights in 2024.

Before you check your next weather alert on your phone or total walked steps, learn about 36+ big data examples that are revolutionizing the digital market today.

Big data examples in media and entertainment

The media and entertainment industry will generate an estimated $3.4 trillion in revenue by the end of 2028. Digitization has brought more ways to consume content, and a wealth of big data is generated every day from these channels.

How big data is used in media and entertainment

Analyzing big data is crucial to generating more revenue and providing personalized experiences in this digitally-driven industry. Here are a few ways big data is being applied in media and entertainment today:

1. Content personalization: Companies like Hulu and Netflix use a lot of big data daily to analyze user tendencies, preferred personalized content, consumption trends, and more. In fact, Netflix and HuluPrime used predictive data analysis to craft its show House of Cards, as the data validated that it’d be a hit with consumers.

2. Digital monetization: Big data is unveiling new ways to monetize digital content, creating new revenue sources for media and entertainment companies. With rise in OTT platforms and streaming services, more producers and investors are buying stakes in digital media and creating revenue sources. 

3. Big data analysis: Thanks to big data analytics software, ads are targeted more strategically, helping companies understand the performance of ads more clearly based on certain characteristics of consumers. This software also does a great job of tracking consumer behavior in the form of content nuggets. 

4. Video streaming analytics: Streamers and gamers harness big data to learn about the interests of their gaming audiences. They can also capture real-time data via feedback surveys, comment section analysis, share metrics, and strategize their next live game streams to match search intent. 

5. Cinematic success: Big data software can collate box office data, review and ticket collection data, gross profit margins, and production budgets to predict a movie's success probability in cinemas.  

6. Social media marketing: Most media companies also focus on social media engagement to analyze marketing campaign responses and people's reactions to their new ventures. Big data that flows in via social media marketing tools is leveraged to tweak future marketing campaigns.

Big data examples in finance

Big data has fundamentally changed the finance industry, particularly stock trading. The introduction of quantitative analysis models has marked a shift from manual trading to trading backed by generative AI technology.

Large financial institutions and hedge funds were the first adopters of this technology. Now, quantitative models have become the standard.

These models analyze big data to predict the outcomes of certain financial events, make accurate enter/exit trade decisions, minimize risk using machine learning, and even gauge market sentiment using opinion mining.

How big data is used in finance 

Below are the areas where big data analysis has been one of financial companies' most sought-after verification and error detection mechanisms.

7. Credit card fraud detection: Credit cards are the most vulnerable to fraudulent exploitation because the data is stored in multiple data warehouses. Big data systems leverage machine learning algorithms to heavily monitor and track data activity in case the card gets swiped or dipped by a malicious user. 

8. Risk control: Big data analytics is also used to mitigate and analyze risk control processes for financial organizations. It studies past financial datasets and historical forecast data to empower analysts to assess risk and build strategies for bank accounts, credit accounts, loan accounts, and so on. 

9. Consumer analytics: Big data also analyzes consumers' financial stability, bank records, and investments to extract relevant data for building advisories around insurance coverages, premiums, digital mortgages, and so on. 

10. Cybersecurity: Every day, big data is used to design strong ransomware and anti-theft tech stacks to prevent break-ins and brute-force attacks. McAfee security services integrate big data with their cybersecurity tools to prevent data transmission on the dark web and build robust data security infrastructure.

11. Big data and hybrid cloud: Deploying big data analysis on hybrid cloud programs like Hadoop or PostgreSQL empowers companies to manage large streams of data pools confidently. As big data is mostly comprised of clickstream and log data, these hybrid servers accommodate these large datasets and reduce the load from physical data servers. 

12. Regulatory compliance: Big data and machine learning intelligence tools like PowerBI always abide by state, federal, and local laws of any geographical area. These tools never violate any stringent data erasure or retention policy enforced by the company. 

13. Digital banking: Crunching large volumes of financial data with intelligent automation optimizes traditional banking processes and transaction services.  Many such AI systems have been integrated with credit card data summaries, loan accounts, and net banking for the convenience of the customer. 

14. Customer segmentation: As the systems are powered by data labeling and data annotation features, the algorithms predict customer trends and generate multiple customer records that are categorized into location, age, area, salary, and other parameters. 

Big data examples in healthcare

The ability to improve quality of life, provide hyper-personalized patient treatment, and discover medical breakthroughs makes the healthcare industry a perfect candidate for big data. In fact, the healthcare industry is one of the largest recent adopters of big data analytics.

How big data is used in healthcare

In healthcare, it’s not about increasing profits or finding new product opportunities; it’s about analyzing and applying big data in a patient-centric way. There are already many great examples of this today:

15. Predict negative health events: In our roundup of predictive analytics examples, we discussed how AlayaCare analyzed big data to predict negative health events that seniors could experience from home care. The analysis reduced hospitalizations and ER visits by 73 percent and 64 percent among chronically ill patients.

16. Identify risk factors: Historical big data from healthcare providers can be used to identify and analyze certain risk factors in patients. This is useful for earlier detection of diseases, allowing doctors and their patients to take action sooner.

17. Analyze patient records: Big data can analyze heterogeneous patient data and can predict their next treatment or consultation cycle. Hospital administration uses data entry solutions infused with big data capabilities to create future consultation schedules and forecast patient expenses. 

18. Identify diseases: Big data can identify disease trends based on demographics, geography, socioeconomics, and other factors. Medical researchers and students can deploy the software to support their predictions for disease trends, causation, and possible remedies.

19. Medical imaging:  Big data in medical imaging involves processing vast datasets from magnetic resource imaging (MRI), X-ray, or other organ scans. Algorithms analyze and detect patterns to improve diagnosis accuracy, speed, and efficiency. For example, AI models identify anomalies in large volumes of images, aiding in early diagnosis and prevention of ailments.

20. 3D anatomy: 3D anatomy utilizes big data to build accurate, detailed digital models of human bodies. These models compile complex, multi-source data, such as MRI and X-ray scans, enabling personalized medical assessments, surgical planning, and real-time training for medical professionals. 

21. Nanoengineering: Big data is also being utilized to develop nanobots for painless cancer immunotherapies.  The data regarding new antidotes, antibiotics and drugs can be fed to a big data system to research, analyze and invent new medications for affected patients.  

Big data examples in education

Modern learning supported by technology is moving away from what we “think” works and more toward what we “know” works. Through big data, educators are able to craft more personalized learning models instead of relying on standardized, one-size-fits-all frameworks.

Big data is helping schools understand the unique needs of students by blending traditional learning environments with online environments. This allows educators to track the progress of their students and identify gaps in the learning process.

In fact, big data is already being used on some college campuses to reduce dropout rates by identifying risk factors in students who are falling behind in their classes.

How big data is used in education

22. Customized programs: Colleges and universities can promote experiential learning with big data. By analyzing attendance, learning patterns, quiz results, assignments, and other submissions, these algorithms can customize the learning management system (LMS) portal according to candidates' mandatory areas of learning and improvement. 

23. Digital curriculum: Big data systems rely on natural language processing (NLP) to build correlations and data comparisons from input datasets. By improving their response accuracy and specialized reinforcement learning with human feedback workflows, these systems can digitize curricula to foster better learning through the academic cycle. 

24. Enhancing student results: Analyzing and scoring candidates via automated grading big data systems also leads to learner happiness and satisfaction. It empowers student engagement and student satisfaction and, in turn, uplifts the scoring practices in educational institutions. 

25. Teaching efficiency: With big data systems, teachers can access first-party student datasets, summarize datasets, and predict student cohesive performance to tweak their teaching strategies and work on actionable data results. This gives them transparency into actual assessment results and a rundown of successes and failures. 

26. Data security and privacy: Big data analytics is installed and maintained in accordance with the organization's special government regulations and data privacy laws. 

Big data examples in retail 

The retail industry has gone digital, and customers expect a seamless experience from online to brick-and-mortar. Big data analytics allows retail companies to provide a variety of services and understand more about their customers.

How big data is used in retail

You’ll find that some of the use cases of big data in retail closely mimic those of media and entertainment. But in retail, it’s a bit more focused on the full customer lifecycle.

27. Product recommendations: Big data is used to store customer browsing behavior and clickstream to make personalized recommendations of e-commerce products. For example, Amazon analyzes products based on past searches on its platform. Using predictive analytics, Amazon it predicts what you’re likely to purchase next accurately.

28. Demand forecasting: Demand forecasting is another application that is widely used by supply chain teams to understand economy factors, generate demand and supply aggregate scores and predict sales. Retailers like Walmart and Walgreens regularly analyze weather changes to identify patterns in product demand.

29. Crisis control: Big data is useful for crisis control. For example, in product recalls, big data helps retailers identify who purchased the product and allows them to reach out accordingly. It initiates customer recovery and support processes based on product images and comments and helps brands deal with dissatisfaction. 

30. Dynamic Pricing: Retailers leverage big data to adjust prices dynamically based on factors like demand, competitor pricing, and customer purchase patterns. This approach maximizes revenue, especially during peak times, while staying competitive in a fluctuating market.

31. Sentiment Analysis: Retailers analyze vast amounts of social media and customer review data to analyze sentiments around products or brands. This helps them make data-driven decisions, adjusting marketing strategies or product offerings to align with consumer opinions and improve brand perception.

32. Fraud detection: Big data enables real-time fraud detection by analyzing patterns in transaction data to flag suspicious activity. Retailers like eBay and Target use machine learning models to quickly identify anomalies, minimizing financial losses and enhancing customer trust.

33. Store Layout Optimization: Retailers use big data on customer movements and purchasing behavior within brick-and-mortar stores. Heatmaps and foot traffic data reveal popular sections, helping retailers position high-demand products to boost sales and improve the shopping experience.

Big data examples in manufacturing

Supply chain management and big data go hand in hand, which is why manufacturing is one of the top industries that benefit from big data. Big data analytics make monitoring the performance of production sites more efficient. Analytics is also extremely useful for quality control, especially in large-scale manufacturing projects.

Big data analytics plays a key role in tracking and managing overhead and logistics across multiple sites. For example, being able to measure the cost of shop floor tasks accurately can help reduce labor costs.

Then there’s predictive analytics software, which uses big data from sensors attached to manufacturing equipment. Early detection of equipment malfunctions can save sites from costly repairs capable of paralyzing production.

How big data is used in manufacturing

34. Inventory control systems: Big data systems can store inventory data to optimize supply chain and logistics processes. Knowing in advance which raw material has been replenished or how much factory waste is produced with these systems can empower teams to thoroughly check bills of materials (BOM), purchase order value, average value cost, and production setups to avoid any fallacy.  

35. Dropshipping and trucking: These systems add virtual markups for each shelf and row of the warehouse, conduct risk assessments, supply monitoring, and factory inspection of your goods. They also automate trucking routes and build intelligent GPS navigation tracker strategies to produce more throughput with less manpower. 

36. Logistics: Big data systems can generate a sync between manufacturing teams and supply chain teams to adjust inventory batches based on the exact demand and regularize delivery timelines. It optimizes logistics and production workflows so that not a lot of pressure is pounded on factory workers and assemblers. 

Go big or go home

The global big data market is forecasted to grow to 103 billion US dollars by 2027. With recent news of the Artemis rocket returning to its Kennedy Space Center launchpad and Elon Musk inaugurating the first-ever Tesla Robotaxi, the sky is the limit for big data consumption. 

Not just enterprises but businesses of all sizes are on their heels in investing in AI prototypes and strategizing to gain a mature AI infrastructure in a predictable future. Their main driving force is eliminating workplace creative blockers, forecasting new trends and algorithmic updates, and upgrading products and services to tap into new market opportunities. Needless to say, big data makes it possible. 

Learn how you can build intelligent, data-driven dashboards to visualize past big data trends and consumer reports with the 10 best free dashboard software.

This article was originally published in 2019 and has been updated with new information.